Landslide Susceptibility and Hazard Modeling

Scour depth prediction is a vital issue in bridge pier design. Recently, good progress has been made in the development of artificial intelligence (AI) to predict scour depth around hydraulic structures base such as bridge piers. In this study, two hybrid intelligence models based on combination of group method of data handling (GMDH) with harmony search algorithm (HS) and shuffled complex evolution (SCE) have been developed to predict local scour depth around complex bridge piers using 82 laboratory data measured by authors and 615 data points from published literature. The results were compared to conventional GMDH models with two kinds of transfer functions called GMDH1 and GMDH2. Based upon the pile cap location, data points were divided into three categories. The performance of all utilized models was evaluated by statistical criteria of R, RMSE, MAPE, BIAS, and SI. Performances of developed models were evaluated by experimental data points collected in laboratory experiments, together with commonly empirical equations. The results showed that GMDH2SCE was the superior model in terms of all the statistical criteria in training when the pile cap was above the initial bed level and completely buried pile cap. For a partially-buried pile cap, GMDH1SCE offered the best performance. Among empirical equations, HEC-18 produced relatively good performances for different types of complex piers. This study recommends hybrid GMDH models, as powerful tools in complex bridge pier scour depth prediction.

Chefchaouen province, located in northern Morocco, is prone to landslides because of its geological features, climate change, and human activities. We investigated landslide dynamics in this mountainous province using a multi-criteria spatial approach and GIS, applying the Analytical Hierarchy Process (AHP). Our findings identified 1267.877 km 2 as low-risk, 1695.334 km 2 as moderate-risk, and 904.2071 km 2 as high-risk, mainly in the northern and northwestern regions. Forests were the most susceptible at 13.83%, followed by agricultural and bare lands, with water bodies least affected. Contributing factors include deforestation, wildfires, and agricultural practices. Our results highlight the need for targeted risk mitigation and continuous land use monitoring. Strategies like slope stabilisation and reforestation can improve landslide prediction and prevention, enhancing community resilience in Chefchaouen. This study's innovative approach and insights into landslide prediction and prevention provide valuable knowledge to the field.

An 8 Richter Scale (RS) earthquake struck West Sumatra on Wednesday, 30 September 2009, at 17.16 pm which led to huge number of landslides. Hence a comprehensive landslide susceptibility mapping (LSM) should be produced in order to reduce the damages to people and infrastructures. In the international landslide literature, various statistical methods such as frequency ratio (FR) and logistic regression (LR) have been widely used individually for LSM, but they have some weaknesses. FR which is able to perform bivariate statistical analysis (BSA) assesses the influence of classes of each conditioning factor on landslide occurrence. However, the correlation between the factors is mostly neglected. On the other hand, LR is able to analyze the relationship among the factors while it is not capable to evaluate the classes of each landslide conditioning factor. This paper aims to propose an ensemble method of FR and LR in order to overcome their weak points. For LSM, a landslide inventory map with a total of 87 landslide locations was extracted from various sources. Then the landslide inventory was randomly divided into two datasets 70% for training the models and the remaining 30% was used for validation purpose. The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). Four PGA of 7.5, 8, 8.6 and 9 were acquired and PGA 8 which was related to the 2009 earthquake was used to generate the model. Finally, the produced landslide susceptibility maps were validated using an area under the (ROC) curve method. For the model which was derived by PGA 8, the validation results showed 84% and 78% success and prediction rates respectively. Furthermore, the prediction rates for the models made by PGA 7.2, 8.6 and 9 are 79%, 78% and 81% respectively. The result proved the reasonable efficiency of the proposed method for earthquake induced landslide susceptibility mapping. Also the proposed ensemble method can be used in other hazard studies as it is capable to produce rapid and accurate assessment for disaster management and decision making.

Landslides are among the great destructive factors which cause lots of fatalities and financial losses all over the world every year. The aim of the research was landslide susceptibility mapping by remote sensing data processing and GIS spatial analysis. The area study in research is central Zab basin in west Azerbaijan province, Iran. In this research, through geological maps and field studies, we primarily prepared a map for landslide distributions in Zab basin. Then, applying other information sources such as the existing thematic maps, we studied and defined the 8 factors such as, lithology, slope, slope aspect, annual rainfall, land use, distance to waterway, distance to the fault , and distance to road. That affect occurrence of the landslides. To get more precision, speed and facility in our analysis all descriptive and spatial information was entered into GIS system. After preparation of the needed information layers by influential parameters on landslides, we drew the zoning maps of landslide hazard via information coming from satellite image classification (Quickbird, Ikonos), and then evaluated and compared them. According to the obtained index, and the comparison of landslide distribution map and zoning map of landslide hazard prepared by each of the methods in GIS environment, This model gives also indications about the relevant factors influencing slope instability.

Landslides are one of the most prevalent catastrophes in the world, which can be caused by a variety of factors, human-related and natural. This study looks at the landslides that severely damaged Taliye village in the Raigad area of Maharashtra State, and it investigates the relationship between the causes of landslides and the primary contributory factor. Out of all the techniques for researching, detecting, and mitigating landslides, remote sensing and GIS have the best precision and dependability. Significant pre-and post-disaster changes in slope, land use, contour, hill shade, soil texture, NDVI, and rainfall were found through a comparative study of digitally processed thematic layers, highlighting the region's susceptibility to landslides. In this instance, we utilized the rainfall data and Weighted Overlay Method to analyze causative elements effectively. Efforts are made to identify the landslide key indicators in pre-landslide results and causative factors behind devastating landslides through post-landslide results. A major observation of the pre-and postlandslide susceptibility map classifies the area into six categories, with high-risk zones 5 and 6 showing 1.13 and 3.93%, respectively, reduced to 0 and 0.07%. It is obvious that the collapse of the zone vulnerable to landslides is what caused this alteration. Meteorological data, such as rainfall stats, serve as evidence to show that a significant amount of input led to triggering landslides. Therefore, it is crucial to apply these findings more broadly in order to formulate preventive strategies that are intended to mitigate potential repercussions and safeguard the welfare of every individual concerned.

The growing popularity of GIS technology in Ethiopia has encouraged multiple scholars to investigate landslide hazards using quantitative approaches, despite its limitations. The present review examined the approach used in the evaluation of landslide hazards by five prior studies that shared catchments. The review results reveal that the controlling factors assumed by the five researchers were inconsistent and resulted in highly divergent frequency ratio (FR) values, even for the same factors. This implies that the contribution of a single instability factor can be inferred sufficiently for landslide hazard assessment and mapping; otherwise, the results are highly subjective and disputable. Since the soil type in the region was alluvial-colluvial in the five studies, and a majority of the failures occurred shortly after rainfall, rainfall data and basic soil properties (classification and shear strength) should not be overlooked. In addition to the nonstandard use of morphometric parameters, the inherent limits of GIS methodologies, the omission of hydrogeotechnical properties, and the observed subjective outcomes make the GIS-based approach imprecise, error-prone, and doubtful. The total effect will result in ineffective early warning systems and unworthy mitigation measures, resulting in significant life costs and damage. As a result, it is recommended that GIS technology should be coupled with software (TRIGRS, Scoops3D, SINMAP, OpenLISEM, GLM, and SLIP) that considers hydrogeotechnical properties to provide more reliable conclusions. In addition to using instability factors consistently, regional statistical correlations of all morphometric parameters can be developed, allowing for less complex and realistic empirical models to be used.

Landslides are natural disasters that lead to widespread social disruption, property damage, damage of communication and infrastructure network such as roads, railways, telephone lines, cultivable lands, crops, plantations and loss of life. India has a history of unique and unparalleled catastrophes due to landslides. It is imperative to identify active or potential landslide locations while planning the alignment of a road, as it helps to avoid landslide prone stretches on the road. Through this paper, the author highlights the delineation of landslide susceptible areas in and around Karnaprayag, Chamoli district, Uttarakhand, India. The main objective of the study was to delineate landslide prone zones via multi-spectral information resulting from remotely sensed data. Landsat 8 images were used for lithological, geomorphological, structural, lineaments, landuse/landcover and drainage mapping. DEM generated using Cartosat data was used and further slope and aspect was derived. Add...

Tolnaku, a village situated in the Kupang Regency, East Nusa Tenggara, Indonesia, is an area susceptible to landslides. An investigation into the likelihood of slope failures in this locality is imperative for landslide mitigation. This study endeavors to elucidate the subsurface conditions and evaluate the physical parameters and contributing factors that could precipitate landslides in this territory. The method of geoelectric resistivity, employing the Schlumberger configuration, has been applied to discern the varieties of rocks and cavities present in this area. The data amassed through the resistivity survey has been processed and analyzed to delineate the subsurface strata and assess their stability. The findings indicate that clay and limestone are the predominant materials in this area, and are deemed unstable. Furthermore, cavities and voids were identified in this area, amplifying the probability of landslide occurrences therein. Ultimately, the Safety Factor (SF) was ascertained to be less than 1, robustly suggesting that the slopes are perilous and vulnerable to landslides. To alleviate landslide risks in Tolnaku, Continuous Monitoring, Infrastructure Strengthening, Community Education, Coordination with Local and National Authorities, and the Development of Spatial Regulations are advocated for the future.

This study aimed to assess landslide susceptibility in the Sahla watershed in northern Morocco. Landslides hazard is the most frequent phenomenon in this part of the state due to its mountainous precarious environment. The abundance of rainfall makes this area suffer mass movements led to a notable adverse impact on the nearby settlements and infrastructures. There were 93 identified landslide scars. Landslide inventories were collected from Google Earth image interpretations. They were prepared out of landslide events in the past, and future landslide occurrence was predicted by correlating landslide predisposing factors. In this paper, landslide inventories are divided into two groups, one for landslide training and the other for validation. The Landslide Susceptibility Map (LSM) is prepared by Logistic Regression (LR) Statistical Method. Lithology, stream density, land use, slope curvature, elevation, topographic wetness index, slope aspect, and slope angle were used as conditioning factors. The Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) was employed to examine the performance of the model. In the analysis, the LR model results in 96% accuracy in the AUC. The LSM consists of the predicted landslide area. Hence it can be used to reduce the potential hazard linked with the landslides in the Sahla watershed area in Rif Mountains in northern Morocco.

[Introducción]: el método más común para determinar el contenido de materia orgánica del suelo es el Walkley-Black, pero este presenta desventajas medioambientales por el uso del tóxico dicromato de potasio; como alternativa, surge el método de pérdidas por ignición. Ambos métodos presentan resultados significativamente diferentes debido a que el método de ignición puede reportar datos mayores por la presencia de arcillas y por considerar todo el contenido orgánico de la muestra, a diferencia de Walkley-Black, que considera únicamente, los compuestos de cadenas más largas. [Objetivo]: se determinó la relación que existe entre el contenido de materia orgánica por Walkley-Black y perdidas por ignición considerando el contenido de arcilla. [Metodología]: Se estudiaron 60 muestras de suelo del nororiente y centro de Honduras. El contenido de arcilla fue determinado a través del método de Bouyoucos. La materia orgánica se determinó por el método de Walkley-Black y por el método de ignición siguiendo lo establecido por los estándares internacionales de AASHTO T 194-97 (2018) y AASHTO T 267-86 (2013), respectivamente. [Resultados]: se encontró una relación lineal válida entre el contenido de materia orgánica determinado por Walkley-Black y por perdidas por ignición independiente del contenido de arcilla. Fue posible establecer un rango de interpretación del contenido de materia orgánica para el método de pérdidas por ignición a partir de los rangos de interpretación típicos reportados para el contenido de materia orgánica de Walkley-Black. [Conclusiones]: Los resultados de este estudio demostraron que es posible usar el método por ignición como alternativa al método Walkley-Black.

In this study, direct risk assessment was developed for various scenarios on the basis of hazard (e.g. spatial probability, temporal probability and magnitude class), vulnerability and estimating cost of road damage. Indirect risk assessment was derived from traffic interruption. The impact of landslide both direct and indirect impact were analyzed in the road segment 174. The research results show the highest direct impact of debris slide type of magnitude I located in the 20th mapping unit. The lowest direct impact of debris slide type of magnitude I can be founded in the 18th mapping unit. The direct impact of rock fall type of magnitude I which is located in the 6th mapping unit. Meanwhile, indirect impact which was caused by road blockage is Rp. 4,593,607.20 and Rp. 4,692,794.40 by using network analysis and community perception methods respectively. After class classification, road segment 174 is dominated by very low hazard, very low vulnerability and very low direct impact.

In this study, direct risk assessment was developed for various scenarios on the basis of hazard (e.g. spatial probability, temporal probability and magnitude class), vulnerability and estimating cost of road damage. Indirect risk assessment was derived from traffic interruption. The impact of landslide both direct and indirect impact were analyzed in the road segment 174. The research results show the highest direct impact of debris slide type of magnitude I located in the 20th mapping unit. The lowest direct impact of debris slide type of magnitude I can be founded in the 18th mapping unit. The direct impact of rock fall type of magnitude I which is located in the 6th mapping unit. Meanwhile, indirect impact which was caused by road blockage is Rp. 4,593,607.20 and Rp. 4,692,794.40 by using network analysis and community perception methods respectively. After class classification, road segment 174 is dominated by very low hazard, very low vulnerability and very low direct impact.

The development of storage-type hydropower projects is crucial for enhancing energy security and managing water resources in Nepal. However, these projects often face significant challenges related to terrain stability, particularly in landslide-prone regions of the Nepal Himalayas. This study presents an innovative approach to terrain visualization using Red Relief Image Maps (RRIM) generated through free and open-source GIS tools. This article presents RRIM visualization techniques developed entirely with free and open-source GIS tools, such as QGIS and Topographic Openness. The selection criteria for these techniques prioritize simplicity and speed of implementation while ensuring their suitability for effective visualization. The study focuses on the application of these techniques to Nationwide Master Planned Storage-type Hydropower Projects in Nepal. By generating and analyzing several RRIM, the article demonstrates how these tools can aid in visualizing terrain features and identifying potential risks associated with deep-seated landslides distribution. The RRIM has been prepared for all 20-master planned storage-type hydropower, diversion and multipurpose projects of Nepal. Additionally, the integration of 3D map visualization of the RRIM enhances the understanding of the terrain's topography and landform. The findings highlight the effectiveness of RRIM and open-source GIS tools in enhancing geohazard assessment and planning for sustainable infrastructure development in future.

The Khimti Khola watershed of the Eastern Nepal experiences numerous landslides each year, pose a significant natural hazard that can cause property damage and fatalities. In mountainous areas like Nepal, an accurate evaluation of landslide hazards may be an essential tool for disaster risk reduction and the strategic planning of development initiatives. To address this concern, a landslide susceptibility assessment was conducted in the Khimti Khola watershed, which covers an area of 440 km 2. The purpose of this assessment is to locate areas that are susceptible to landslides and to provide light on the factors that affect them. A total of 415 landslides were identified, covering 1.715 km 2 or 0.39% of the entire study area. The assessment derived the landslide susceptibility map using the Weights-of-Evidence (WoE) model from selected conditioning factor classes that were processed in ArcGIS and categorized into five classes: Very High, High, Moderate, Low, and Very Low. Each of these classes covers 7.12%, 16.63%, 25.23%, 30.24%, and 20.78% of the total study area, respectively. According to the findings, the Khimti Khola watershed has a 33.2% landslide occurrence percentage in the high susceptibility zone, out of the total area. Additionally, the very high, moderate, low, and very low susceptibility zones cover 24.35%, 20.75%, 17.08%, and 4.62% area of the landslide occurrence, respectively. In this assessment, the Area Under Curve (AUC) is used to validate the model and is plotted using ArcSDM. Identified a total of 415 landslides, out of which 311 (75%) were used as training sample for susceptibility mapping, while the remaining 104 (25%) were used to validate. The AUC for the landslide susceptibility map is 83%, indicating a very good degree of accuracy and satisfaction. The resulting susceptibility map provides information for predicting landslide-prone areas. It can be used to minimize the risk of potential landslides, safeguard lives, and prevent property damage. It will be a vital tool for disaster preparedness, future construction planning and development projects in this area.

On September 2, 2023, Sakura Science Club Nepal (SSCN) enjoyed their first in-person alumni meeting, “Coming Together with the Sakura & Japan Experiences”, at Hotel Himalaya in Kathmandu. It was a cozy gathering where alumni and guests could talk and interact closely with each other.

Assessing landslide risk is a fundamental requirement to plan suitable prevention actions. To date, most risk studies focus on individual slopes or catchments. Whereas regional, national or continental scale assessments are hardly available because of methodological and/or data limitations. In this contribution, we present an overview of all requirements and limitations in landslide risk studies across all spatial scales, by means of a hybrid form that combines elements of original research with the comprehensive characteristics of a review study. The review critically analyses each component in the landslide risk analysis providing a detailed explanation of their state-of-the-art, with dedicated sections on susceptibility, hazard, exposure, and vulnerability. To put the theoretical framework to test, we also dive into a case study, expressed at the continental scale. Specifically, we take the main European mountain ranges and provide the reader with a textbook example of risk assessment for such a large territory. In doing so, we take into account issues associated with cross-national differences in landslide mapping. As a result, we identify landslide-prone European landscape and explore the associated possible economic consequences (human settlements and agricultural areas). We also analyze the population at risk during daytime and nighttime. Moreover, a modern view of the problem is explored in the form of how risk outcomes should be delivered to master planners and geoscientific personnel alike. Specifically, we convert our output into an interactive Web Application (https://pan-european-landslide-risk.github.io/) to include notions of scientific communication both to a large public as well as to a technical audience.

With the development of remote sensing technology, satellite-based soil moisture estimates become more and more available, and the potential of using satellite soil moisture products in landslide hazard assessment has been widely recognized. However, to our knowledge, there is a lack of studies exploring the performance difference of various satellite soil moisture products for such an application. Therefore, this study aims to compare several state-of-the-art satellite soil moisture products on their potentials in landslide applications. The selected products include the ESA CCI soil moisture dataset, the SMAP Level-3 (L3), enhanced Level-3 (L3), Level-4 (L4) surface, and Level-4 (L4) root zone soil moisture datasets. Specifically, the completeness of different datasets is calculated to assess their applicability in practical applications. To investigate the relationship between the soil moisture and the commonly used rainfall information in landslide predictions, the correlation study of the satellite soil moisture with the antecedent cumulated rainfall is also carried out. In addition, to explore whether the satellite soil moisture can provide valuable information for landslide hazard assessment, infiltration events are identified based on the time series of satellite soil moisture, and the significance of event characteristics (such as event duration, soil moisture change, etc.) in landslide occurrence is then investigated with Bayesian analysis. This study is carried out in a landslide-prone area, the Emilia-Romagna region in northern Italy. Results show that the SMAP L4 product does not have any missing values, beneficial to the continuous monitoring of landslides. As for the correlation relationship between soil moisture and antecedent cumulated rainfall, the SMAP L4 product also has more rational spatial distribution of the Pearson correlation coefficients compared with other datasets, which can be better explained by the distribution of slope and TWI (topographic wetness index). Bayesian analysis on the infiltration events shows that our prior knowledge of the probability of landslide occurrence is better improved by using the 'SMAP L4 root zone soil moisture'-derived infiltration events, indicating its greater potential to be used for landslide hazard assessment in the study region.

For rainfall-induced landslides, their occurrence is attributed to both the antecedent wetness condition and the recent rainfall condition. However, when defining rainfall thresholds for the landslide occurrence, these two types of information have been used incompletely or implicitly, which may affect the threshold's predictive capability. This study aims to investigate how to make a better use of these two types of information in the landslide threshold definition. Here four types of thresholds are proposed, by including different variables that are responsible for landslide occurrences, these thresholds could represent different cases, like whether to include the antecedent wetness information or whether to consider the recent rainfall condition explicitly. The predictive capability of these thresholds is then compared crossly with the help of the receiver operating characteristic (ROC) approach. We carry out this study in a northern Italian region called Emilia-Romagna. Results show that the antecedent wetness condition plays a crucial role in the occurrence of rainfall-induced landsides. It is beneficial for the threshold's predictive capability to explicitly include the antecedent wetness information and the recent rainfall in the definition of landslide thresholds. When including soil moisture information in landslide threshold, the reliability of the soil moisture measurement is a key factor affecting the threshold's prediction performance. Landslides are one of the most frequent and widespread natural hazards, posing threat to human lives and local infrastructures. These threats increase with the continuous development in the mountain areas. Landslide alerts or early warnings are able to provide useful information for disaster managers and emergency planners to mitigate the related socioeconomic risk (

This study aims to evaluate landslide causative factors in landslide susceptibility assessments and to compare landslide susceptibility models based on the bivariate frequency ratio (FR), multivariate logistic regression (LR), and artificial neural network (ANN). The majority of landslide occurrences in Ambon, Indonesia is induced by heavy rainfall events where slope failures occur mostly on steep slopes thereby endangering municipality areas at the base of the hills. Eight landslide causative factors were considered in the landslide susceptibility assessments. The causative factors were elevation, slope angle, slope aspect, proximity to stream network, lithology, density of geological boundaries, proximity to faults, and proximity to the road network. The output susceptibility maps were reclassified into five classes ranging from very low to very high susceptibility using Jenks natural breaks method. Twenty percent of all mapped landslides were used as the validation of the susceptibility models. The validity and the accuracy of each model were tested by calculating areas under receiver operating characteristic curves (ROCs), and the areas under the curve (AUC) for the success rate curves of FR, LR, and ANN were 0.688, 0.687, and 0.734, respectively. The AUC for the prediction rate curve of FR, LR, and ANN were 0.668, 0.667, and 0.717, respectively. All findings of the models show good results with the accuracy of all models being higher than 66%. The ANN method proved to be superior in explaining the relationship of landslide with each factor studied.

The main objectives of this study are to assess the underlying causative factors for landslide occurrence due to earthquake in upper Indrawati Watershed of Nepal and evaluating the region prone to landslide using Landslide Susceptibility Mapping (LSM). We used logistic regression (LR) for LSM on geographic information system (GIS) platform. Nine causal factors (CF) including slope angle, aspect, elevation, curvature, distance to fault and river, geological formation, seismic intensity, and land cover were considered for LSM. We assessed the distribution of landslide among the classes of each CF to understand the relationship of CF and landslides. The northern part of the study area, which is dominated by steep rocky slope have a higher distribution of earthquake-induced landslides. Among the CF, 'slope' showed the positive correlation as landslide distribution is increasing with increasing slope. However, LR analysis depict 'distance to the fault' is the best predict...

Öz: Yüksek standartlı demiryolları, kat ettikleri uzun mesafeler ve içerdiği yoğun mühendislik yapıları sebebiyle tasarım, yapım ve işletmecilik açısından oldukça karmaşık yapılardır. Demiryolu güzergah çalışmaları sırasında tünel, viyadük, altgeçit, üstgeçit gibi sanat yapılarına sıklıkla başvurulmakta, buda projenin maliyetini, yapım süresini ve işletmecilik sürecinde bakım maliyetlerini doğrudan etkilemektedir. Bu nedenle yüksek standartlı demir yolu projelerinin temelini güzergah çalışmaları oluşturmaktadır. Bu çalışmada, heyelan altından geçen Doğançay Ripajı T8 Tüneli 2 farklı güzergah alternetifi (heyelana en yakın ve en uzak) irdelenmiştir. Bu amaçla öncelikle sondaj ve jeodezik yöntemler kullanılarak heyelan geometrileri belirlenmiş ardından geri analiz ve ileri analiz yöntemleri kullanılarak heyelanların farklı güzergahlara etkileri değerlendirilmiştir. Sonuç olarak heyelanlı bir bölgeden geçen T8 Tünelinde en kısa tünel güzergahının daha fazla destekleme gerektireceği bu nedenle heyelan kayma yüzeyinden en uzak mesafede tünelin konumlandırılması gerektiği anlaşılmıştır. Bu anlayışa göre konumlandırılan T8 Tüneli sorunsuz bir şekilde 2021 yılında ulaşıma açılmıştır.

This study introduces a novel, fog computing-based real-time flood prediction and early warning system that integrates advanced machine learning algorithms with remote sensing technologies to address the limitations of traditional flood monitoring infrastructures. Existing systems often struggle with latency, over-reliance on centralized cloud computing, and fragmented data integration, which collectively hinder their effectiveness during critical flood events. To overcome these challenges, this research employed a mixed methods research design, combining quantitative experimentation with qualitative inquiry to comprehensively evaluate the system's predictive performance, operational resilience, and stakeholder usability. The quantitative component involved the deployment of an optimized wireless sensor network, designed using Low Energy Adaptive Clustering Hierarchy (LEACH) and Particle Swarm Optimization (PSO), to collect real-time hydrological data including rainfall, river levels, and soil moisture. These data streams were fused with high-resolution remote sensing imagery from Sentinel-1 SAR and Sentinel-2 MSI, and processed using advanced machine learning models such as Long Short-Term Memory (LSTM) networks and Random Forest classifiers. The LSTM model achieved high predictive accuracy with an RMSE of 0.38 and NSE of 0.84, while fog computing nodes reduced latency by over 60%, enabling localized, real-time alert dissemination even during internet outages. The qualitative component involved 23 semi-structured interviews with disaster management authorities, field technicians, and community representatives to assess the system's usability, trustworthiness, and practical challenges. The triangulation of findings confirmed the technical validity, operational reliability, and end-user relevance of the system. By integrating decentralized processing, intelligent forecasting, and human-centered design, this research contributes a scalable, adaptive, and fieldtested framework for intelligent flood early warning systems. It holds significant potential for deployment in climate-vulnerable, infrastructure-constrained regions globally, advancing both technological innovation and disaster resilience in the face of increasing hydrometeorological extremes.

Landslides present a significant geohazard in Orumba North, southeastern Nigeria, with varying impacts on urban and rural regions. This study aims to assess and compare the vulnerability of both regions to landslides using a combination of remote sensing, (GIS), and the fuzzy analytical hierarchy process (Fuzzy AHP). The integration of these methodologies allows for a precise spatial delineation of landslide-prone zones based on the factors contributing to the differential susceptibility of urban and rural landscape. These conditioning factors include; slope, rainfall, elevation, curvature, land use and land cover, normalized difference vegetation index, and proximity to streams and roads, which were used for the assessment. Through fuzzy-AHP, these factors were assigned weighted importance based on expert judgement and environmental conditions, the defuzzification process converted the fuzzy weights into crisp value for precise evaluation these crisp values was used to rank the factors based on their relative importance. Using the defuzzified weights, a landslide vulnerable map was generated, classifying the study area into four susceptibility levels; low, moderate, high, very high. The result highlight that the urban areas such as Oko, Nanka, and Ajalli exhibit higher vulnerability to landslide due to intense human activities, including construction, deforestation, and poor drainage management that exacerbate slope instability. In contrast rural area like Ufuma and Omogho are largely affected by natural factors including steep slope, agricultural practices, leading to significant but less human induced susceptibility. Statistically, the affected urban area covers about 78% of the study area, while the rural area cover about 22%. This geospatial assessment offers valuable knowledge for local planner and decision markers, as the generated susceptibility map can serve as a guide for hazard mitigation strategies, By identifying high risk area, particularly those in urban centres with higher development pressure, targeted efforted can be made to improve land use planning, drainage system and slope stabilization. The study findings are crucial for formulating policies that address both human and natural factors contributing to landslides, ultimately aiding in disaster risk reduction in the urban and rural area of Orumba North region.

It is recognised that earthquake design of high cut slopes in hilly to mountainous terrain is important to ensure that the next generation of transportation routes have adequate resilience to provide access to communities following an earthquake event. There is very little guidance in New Zealand or worldwide on the seismic design of cut slopes. Research and development of guidelines for the seismic design of high cut slopes was carried out by Opus for the New Zealand Transport Agency. The guidelines propose a new performance based approach where the need for resilience underpins the design. The design loadings and approach are based on the importance of the route and the resilience expectations for that link, given the national resilience context. A new Resilience Importance Category (RIC) is proposed to guide the design approach. The guidance addresses a number of critical issues in the seismic design of cut slopes, such as selection of topographic amplification factors, the use o...

The study explores topography and vegetation changes in the Cradle Nature Reserve's landscape, which is characterised by interconnected water infiltration and drainage patterns with geological features such as sinkholes, using indices such as the Enhanced Vegetation Index (EVI), the Topographic Position Index (TPI), the Topographic Ruggedness Index (TRI), and the Topographic Wetness Index (TWI). The high-resolution satellite images, including Sentinal-2A, the Shuttle Radar Topography Mission Digital Elevation Model, and NASSA Power Rainfall, Temperature, and Ground Wetness in the Root Zone-Modern Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), were analysed using advanced statistical models. The software tools Stata/SE v.13.1, QGIS v.3.36, and ArcGIS desktop v. 10.8.2 were utilised for this analysis. The results emphasise the significance of incorporating topography into ecological research and highlight the necessity of focused conservation initiatives to address habitat suitability and erosion risk in challenging landscapes. Specifically, the results show that the Enhanced Vegetation Index (EVI) has a strong negative correlation with the Topographic Position Index (TPI) (R 2 ¼ 0.95), indicating that TPI usually decreases as EVI increases. This relationship is influenced by landscape features such as sinkholes and depressions, which impact plant health. Additionally, the strong positive relationship between EVI and percentage slope gradient (R 2 ¼ 0.85) offers valuable insights for environmental studies and land management practices. Additionally, TRI shows a negative correlation with EVI (R 2 ¼ 0.94), emphasising the impact of terrain ruggedness on vegetation density. TWI analysis strongly correlates with slope gradients (R 2 ¼ 0.96), highlighting topography's role in hydrological dynamics. Despite these insights, we acknowledge limitations such as scale dependency and the inability to capture fine terrain details. Integrating topographic information into ecological assessments and land management strategies is crucial for promoting ARTICLE HISTORY

L'uomo, è certo, vincerà la natura. E lo farà grazie ad una scienza, una tecnica ed una amministrazione, che non si affitteranno a nessuno.

Prima di piegare a noi la natura, dovremo aver piegate le sinistre forze sociali che ci schiavizzano peggio di milioni di metri cubi di pietre sepolcrali, e che mettono il responso degli esperti di oggi sotto la condanna dei lauti compensi e dei profitti esosi. Dobbiamo arginare le frane non di acque e terra; ma di schifosissimo oro.

Landslides are projected to increase in the future due to population pressure and associated landuse changes exacerbated by climate change in the tropics. Studies about the impacts of landslides pay much attention to the negative effects at the expense of any positivity. This qualitative study seeks to fill the gap by focusing on both the positive and negative impacts of landslides in Murang'a County through descriptive statistics and narrative analysis. Primary data was collected through Household (HH) questionnaires, Focus Group Discussions (FGD), and Interview schedules. The sample size is 393 proportionate HHs in the study area, with a total population of 85,895 people distributed over 26,201 HHs. A total of 8 key informant interviews were conducted alongside 6 FGD. An overwhelming count of 97 percent of local people considered landslides as disasters, and 88.7 percent said that there has been an increase in cases over time. Importantly, 80.8 percent of those who testified to have experienced a landslide (94.3%) reported a landslide at close proximity of less than 1 kilometre from their homes. A significant 92.3 percent of the landslide cases were reported to have caused damages ranging from destruction of properties, displacements, loss of human life and animals, and psychological stress, among others. Prominent negative impacts of landslides are psychological effects reported by 63.4 percent of the affected respondents. Other adverse effects included loss of properties, displacements, deaths, and loss of animals and crops. The positive impacts include monetary gains for the affected residents, income for absentee house owners, and rebuilding resilient infrastructures. The study concludes that landslide disasters in the prone areas of Murang'a County result in both negative and positive impacts. The impacts of landslides on residents living in prone areas indicated more pronounced negative effects compared to the positive ones. Therefore, the study concludes that efforts by different stakeholders should be put in place to minimize the adverse effects of landslides. Both county and national governments should have an all-inclusive disaster management programme involving the residents who are directly impacted.

A landslide is one of the geological hazards that cause the most disaster in densely populated areas. The landslide that occurred in Gezie Gofa woreda, Gofa Zone, Kencho Shacha Gozdi village, killed more than 250 people. Two landslides occurred on July 22, 2024, at 8:30 and 10:40 AM The first landslide killed six people and demolished three houses. The second landslide killed more than 245 people, including those who came to the site to excavate the buried bodies during the first landslide. The primary objective of this research is to evaluate the landslide causative factors, model the landslide susceptibility, and characterize the landslide disaster on socioeconomic effects that occurred on July 22, 2024. The landslide inventory data, field surveys, laboratory analyses, and various geophysical surveys characterized the current and past landslides of the area. The landslide susceptibility model was modeled using a statistical approach in the GIS. environment. The socioeconomic effects were assessed using field surveys and systematic interviews with the victims. The conditioning factors selected for landslide susceptibility modeling are lithology, geological structures, groundwater, slope, land use or land cover, aspect, curvature, and elevation. The major triggering factor of the landslide in the area was heavy rainfall, which occurred on July 21-22, 2024, between 3:00 a.m.-8:30 a.m. The results reveal that the significant conditioning factors of the landslide in the study area are geological structures (both visible and inferred), groundwater, slopes, and human activities. The characterized socioeconomic effects include the destruction of agricultural land, the demolishing of houses, and the loss of human lives, as well as several people evacuated and sheltered under tents and churches. However, the most momentous disaster in the area is the loss of human life. Based on the research results, it would be better to relocate those people living in the high landslide susceptible zones, and all high landslide-prone areas and mountainous terrain in southern Ethiopia should be mapped, and the people should be aware of the landslide risk areas.

Landslides pose a significant geohazard globally, particularly in Asia, which accounts for nearly 75% of worldwide landslide-related fatalities. The regions under the Himalayan Arc in India are especially vulnerable due to complex geomorphological, climatic, and anthropogenic factors. This conceptual review synthesizes existing methodologies for landslide hazard mapping by exploring four key dimensions: inventories, susceptibility, vulnerability, and risk assessment. The study highlights the importance of landslide inventories which serve as the foundation for accurate susceptibility modelling. Various susceptibility mapping approaches-qualitative, semi-quantitative, and quantitative have been reviewed. Further, vulnerability assessments through multidimensional frameworks, including heuristic and index-based methods, fragility curves, and numerical modelling, which evaluate the impact on both human and structural assets, are discussed. Finally, risk analysis is also deliberated through qualitative and quantitative lenses, integrating hazard intensity, vulnerability, and value of elements at risk. This review highlights the need for a multidisciplinary and mixed-method approach to enhance the accuracy, reliability, and practical applicability of landslide hazard assessments, particularly in data-scarce and high-risk regions.

Introducción. Las actividades agropecuarias y las anomalías atmosféricas representan un alto porcentaje de alteraciones en ecosistemas riparios, modificando rápidamente servicios ecosistémicos, como la pérdida de la calidad del entorno. Esta investigación midió el efecto de variaciones climáticas sobre el uso del suelo agropecuarios asociados a la zona riparia. Métodos. Con imágenes Lansat 8 y el preprocesamiento de estas en SAGA GIS, se realizó la extracción de datos del Índice de Diferencia de Vegetación Normalizada (NDVI, por sus siglas en inglés) por mes durante el 2022. Se procedió a realizar análisis estadísticos en RStudio para test Kruskal Wallis (KW). Dentro del método se analizaron los tipos de correlaciones existentes entre los datos atmosféricos y los NDVI. Resultados. La dispersión de los datos en usos de suelo y vegetación adyacente (Va) presentaron valores similares a lo largo del año, considerando que esta última (Va) mostraron valores asociados a vegetación media. D...

In mountainous areas, understanding the sizes and types of rainfall-induced landslides, and identifying areas at high risk, are critical challenges for disaster management. Shallow and deepseated landslides are the two major types in mountainous regions, distinguished by their failure mechanisms, geomaterials, depths and sizes. Shallow landslides typically involve planar failures at depths less than 3 m. In contrast, deep-seated landslides exhibit ellipsoidal or hemispherical geometries, with rotational failure planes exceeding 3 m in depth. The infinite slope stability model is commonly used to analyse shallow landslides, but it tends to overestimate the occurrence of deep-seated landslides due to its unsuitability for ellipsoidal or hemispherical failure surface geometries. To address this issue, this study proposes modifying the slope stability model by incorporating a hemispherical shape aligned with the failure characteristics of deep-seated landslides as hemispherical slope stability. This is coupled with infiltration and groundwater recharge analysis, and is used to simulate rainfall-induced deep-seated landslides induced within GIS environment. Model validation was conducted by comparing the predictions of the hemispherical slope stability model with historical landslide data and the traditional Scoops3D model, using ROC-AUC values. The hemispherical slope stability model demonstrated good agreement with historical data, achieving AUC values of 81.08% to 83.85%, and showed consistent results with the Scoops3D model in predicting stable and unstable zone. Therefore, these advancements are particularly valuable for real-time application as they enable the precise delineation of landslide susceptibility area.

This study investigates landslide susceptibility and proposes potential relocation sites in the Gofa Zone of Southern Ethiopia, utilizing combined geospatial analysis techniques such as Geographic Information Systems (GIS) and Remote Sensing (RS). Factors such as slope, soil type, land use, rainfall, etc., were integrated into the analysis to identify the susceptible zones. A landslide susceptibility map was developed, categorizing the region into five susceptibility classes: very low, low, moderate, high, and very high. The analysis revealed that 11.74% of the Gofa Zone is classified as very low susceptibility, 31.33% falls under low susceptibility, and 30.02% is categorized as moderate susceptibility. Areas of high susceptibility cover 19.89%, while very high susceptibility zones account for 7.02% of the region. The findings indicate that high-risk areas are predominantly located in steep, mountainous terrains with unstable soils and intensive agricultural activities. In contrast, moderate and low-risk regions are characterized by more stable slopes and lower levels of human impact. Additionally, the study identifies potential relocation sites with minimal landslide risk, primarily located in flatter, stable terrains with less human encroachment. This study highlights the importance of combining GIS and RS for practical landslide risk assessment and provides valuable insights for disaster risk management, urban planning, and identifying safer areas for population relocation. The results are intended to assist local authorities and planners in making informed decisions to mitigate landslide hazards and promote sustainable regional development.

Malaria is one of the killer human diseases caused by protozoan parasites from plasmodium family which foremost transmitted by the bite of the female Anopheles species of Mosquito. Ethiopian lowland areas are favorable for mosquito breeding and malaria transmission due to their tropical location and environmental factors. The current study was aimed to analyze the malaria hazard and risk areas in the case of Kebridahar and Sheykosh districts of Korahey zone by using GIS and RS technique. In order to achieve the study objective, different data’s such as satellite images, digital elevation model, topographic map, study area shape file and the environmental factors like temperature, elevation, slope, soil, land use land cover and proximity to pond water site were used as an input data for the analysis. Weight was assigned for these parameters by pairwise comparison method and weighted overlay was used in Arc GIS spatial analyst tools to produce the final malaria hard and risk map of th...

MESTRADO PROFISSIONAL EM ENGENHARIA GEOTÉCNICA DA UFOP OURO PRETO -MARÇO DE 2013 v AGRADECIMENTOS Agradezco primeramente a Dios. A mi orientador el profesor Romero César Gomes (UFOP), por el aprendizaje brindado, la confianza, paciencia y oportunidad de iniciarme en el área de Geotécnia. A mi co-orientador Géol. Ramón Chaile, que permitió poder trabajar en Mina Pirquitas, por todo el apoyo, conocimiento y consejos aportado.

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as "elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology", were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km 2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km 2 (26.09%) as low susceptibility, 174.10 km 2 (27.12%) as moderate susceptibility, 137.03 km 2 (21.34%) as high susceptibility, and 70.30 km 2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km 2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km 2 (18.59%) falls into the low susceptibility zone, 147.94 km 2 (23.04%) falls into the moderate susceptibility zone, 154.04 km 2 (23.99%) falls into the high susceptibility zone, and 82.78 km 2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model's effectiveness relies on the individual's expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas. Highlights • The study combines Frequency Ratio and Analytical Hierarchy Process models with GIS to improve landslide risk assessment in Ethiopia's highlands.

This study sought to assess the influence of causal factors related to anthropogenic activities on landslide occurrence in Bukit Antarabangsa, a township northeast of Kuala Lumpur in Ampang Jaya Municipal Council. Anthropogenic factors were chosen based on the township’s rapid growth, numerous landslide records and intensity of hillside development. The study used a data-driven statistical model to identify factors most predictive of landslide occurrence based on an inventory of 20 landslides, and to evaluate the extent to which susceptibility was driven by factors related to urban
development. A total of 17 factors were categorized into four clusters (geological, geomorphological, hydro-tographical and anthropogenic). Factor maps were classified to derive factor classes for each parameter. The dataset was then processed using a weight-of-evidence statistical model to determine the contrast value of each factor class. Contrast value (C) reflects the extent to which each factor class predicts landslide occurrence. The C-weighted factor maps were then combined to derive the landslide susceptibility index (LSI). The LSI enabled visualization of the spatial distribution of
susceptibility based on a given combination of factors. Susceptibility maps were
prepared for combinations containing only non-anthropogenic parameters and all
landslide parameters. The study compared these combinations to determine the
influence of anthropogenic factors on total LSI. Similar analyses were conducted to
determine the effect of each anthropogenic factor on LSI. The results indicated that
lineament density, distance to lineament and distance to road had a significant influence
on landslide occurrence. A strong correlation with landslide occurrence was observed
for land use/land cover, especially in high susceptibility zones, followed closely by the
influence of one distance to road factor class. The results could be useful in planning
infrastructure corridors in densely built-up landslide-prone areas.

La Teoría Sincrónica del Futuro-Pasado y su extensión ofrecen un marco innovador para reducir la incertidumbre al modelar el futuro como un "pasado determinado" mediante herramientas estadísticas, matemáticas y computacionales. Basada en la premisa de que el pasado es certero y el futuro indeterminado, esta teoría utiliza modelos predictivos robustos, como cadenas de Markov, redes neuronales recurrentes (RNN) y simulaciones Monte Carlo, para proyectar escenarios futuros a partir de patrones históricos. La relación entre pasado y futuro se define mediante una función g, donde f(tf )=g(Pt<tf ), permitiendo predecir eventos futuros con mayor precisión. Además, incorpora factores externos (multifactores) como variables latentes L, capturando interacciones complejas entre economía, clima y sociedad mediante funciones no lineales h.
La extensión permite recrear un futuro deseado (Fd) y retroceder para determinar las condiciones actuales (Pa) necesarias para lograrlo, utilizando una función inversa g −1. Esto transforma la toma de decisiones en una herramienta proactiva, optimizando recursos y estrategias en áreas como commodities, cambio climático y agricultura. En ciencia, facilita avances en inteligencia artificial y biología al modelar futuros escenarios y retroceder para identificar intervenciones actuales. En tecnología, mejora simulaciones y sistemas automatizados. En negocios, optimiza la planificación estratégica al predecir precios futuros o tendencias de mercado. Al integrar retroalimentación iterativa, se ajustan predicciones y maximiza su precisión. En esencia, esta teoría redefine la relación tiempo-causalidad, demostrando que el futuro es el pasado que aún no hemos determinado, pero que ahora podemos moldear desde hoy.

Due to rapid urbanization and infrastructure developments, there is scarcity of stable construction/foundation sites and Geo-technical Engineers often encounter situations where the selected site is not found suitable to take the load of the proposed structure.in such cases, various methods of soil stabilization can be used to improve the ground conditions. Though there are various soil stabilization techniques available, use of saw dust Ash (SDA) has been successfully used in recent times to improve the shear parameters of the marginal/weak soils. Among Various stabilizing materials saw dust Ash (SDA) can be used as to enhance the shear strength and bearing capacity of a clayey soil for sustainable use of waste materials and sustainable development of infrastructures in a rapid urbanization. Saw dust is a natural waste material obtained from sawmills, timbering works etc., which creates health and environmental problems if not disposed-off in scientific manner. This is available in abundance at a very low cost and can be easily used as a stabilizing material not only to improve poor/unsuitable construction sites for sustainable construction but also to avoid its disposal problems. Therefore, this project presents some studies on strength properties of clayey soil stabilization with saw dust Ash. The SDA randomly distributed in clayey soil samples were tested for its engineering properties by performing CBR and Unconfined test on a number of samples by using the different percentage of saw dust Ash and comparing the results with non-stabilized soil. The test results revealed that the strength significantly improves with the inclusion of SDA and also prevents the sampling from cracking.

Karewa soils are extensively spread in the state of Jammu and Kashmir and they've been used as subgrade of pavement layers. Karewa soils are generally muddy soils and are characterized as low bearing capacity materials for flexible pavement layers. When similar soils can not be replaced, its subgrade performance should be increased by several revision ways. In the place, where ground water table is high, the strength of subgrade is negatively affected by humidity infiltration to subgrade and base due to capillary action. The purpose of this design is to estimate the use of low contents of lime in the revision of Karewa soil properties. The main goal of this research is to find the ideal lime dosage for stabilising the readily available karewa soil in the area. The research includes analyses of basic characteristics of soil. Moreover tests are done to find the extent of modification by analyzing the impact of adding different amounts of lime to estimate the extent of revision on MDD, OMC, and CBR of the soil. The experimental examinations shown that there's a tremendous increase in the CBR value of the soil treated with lime, therefore leading to dropped consistence conditions of the sub-base and base courses.

Absztrakt Az ipari balesetek és szerencsétlenségek egyre fontosabb szerepet kapnak a védelmi szféra területén. A cikk célja, hogy bemutasson két esettanulmányt, az elsőnél egy veszélyes árú közúti szállítása során kialakult balesetet, a másodiknál egy ún. Seveso-s létesítményben bekövetkezett vegyi balesetet. A katasztrófának való ún. kitettség a kockázat velejárója, akárcsak a sebezhetőség és a veszély. A cikkben jelentős szerepet kap a potenciális veszélyeknek való kitettség értékelése és elemzése, figyelembe véve a legfontosabb társadalmi, gazdasági és környezeti elemeket. Veszélyes áru szállítása esetén vannak olyan útvonalak, amelyek nagyobb kockázattal és nagyobb kárértékkel rendelkeznek, mint más területek. Romániában az egyik ilyen útvonal Râmnicu Vâlcea város közelében található, ahol az ilyen jellegű vegyi balesetek különösen fenyegetik a lakosságot és a környezetet. Abstract The paper presents a study on the exposure analysis in case of possible toxic chlorine releases fr...

Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data.
Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about
4,100 km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community.
With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods.
Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence.
We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences.