Trend detection

Drought is a recurrent natural hazard affecting arid/semi-arid, as well as temperate regions. Numerous research efforts have been made for all types of drought (e.g. meteorological, hydrological and agricultural) in arid/semi-arid regions, whereas only a small number of studies have been performed in temperate regions. Moreover, in these regions, the type of hydrological drought which is related to groundwater is largely left unstudied. The GRoWaDRISK project is aimed to contribute to covering this gap. This paper summarises the aims, scope and methodology of the project. A large part of the Belgian territory was chosen as the study area. The project encompasses three groups of research tasks. First, knowledge regarding influencing factors of groundwater drought will be increased through using the best available spatial information on related variables (groundwater level, recharge and discharge). Second, the socio-economic and environmental impacts of droughts will be assessed for t...

This paper presents trends computed for the 31-year period of monthly streamflows obtained from 26 basins over Turkey. Four non-parametric trend tests (the Sen's T, the Spearman's Rho, the Mann-Kendall, and the Seasonal Kendall which are known as appropriate tools in detecting linear trends of a hydrological time series) are adapted in this study. Moreover, the Van Belle and Hughes' basin wide trend test is included in the analysis for the same purpose. Homogeneity of trends in monthly streamflows is also tested using a procedure developed by Van Belle and Hughes. Thus, this study includes a complete application of both the Van Belle and Hughes' tests for homogeneity of trends and basin wide trend (originally developed for trend detection in water quality data) on a hydroclimatic variable. As a result, basins located in western Turkey, in general, exhibit downward trend, significant at the 0.05 or lower level, whereas basins located in eastern Turkey show no trend. In most cases, the first four tests provide the same conclusion about trend existence. Use of the Seasonal Kendall, which involves a single overall statistic rather than one statistic for each season, is justified by the homogeneity of trend test. Moreover, some basins located in southern Turkey exhibit a global trend, implying the homogeneity of trends in seasons and stations together, based on the Van Belle and Hughes' basin wide trend test.

Managers are often required to monitor networks of sites to make inferences about trends in fish populations over large geographic areas. We evaluated the statistical power of four revisit monitoring designs (always revisit design; two types of augmented serially alternating design; and serially alternating design) to detect declines in trout population biomass over a 30-year time horizon. The Medicine Bow National Forest, Wyoming, was used as a case study, and the designs were based on sampling 20 or 30 sites every other year but varied in the total number of sites (20-40 or 30-60) and the timing and frequency with which sites were monitored. The always revisit design, in which the same 20 or 30 sites were sampled during every monitoring period, often had slightly higher power than the other designs. However, any differences in power quickly diminished due to the rapid increase in power over time for all four designs. The similar levels of statistical power to detect population declines among the designs we evaluated suggest that managers have flexibility to choose a revisit design with increased spatial coverage and implementation complexity without sacrificing trend detection capability.

In this paper changes in the wave pattern of the Catalan coast are analyzed in terms of wave storminess and wave direction based on series of 44 years of hindcast wave data. The analysis is performed employing two different techniques: one resulting from the combination of regression and bootstrapping and the other applying a Bayesian method. Although the trends are, in general, null, in some locations there are clear changes in the wave directional frequencies. These changes can significatively affect coastal hydrodynamics and morphodynamics.

This study aims to determine trends in the long-term annual mean and monthly total precipitation series using nonparametric methods (i.e. the Mann-Kendall and Sen's T tests). The change per unit time in a time series having a linear trend was estimated by applying a simple non-parametric procedure, namely Sen's estimator of slope. Serial correlation structure in the data was accounted for determining the significance level of the results of the Mann-Kendall test. The data network used in this study, which is assumed to reflect regional hydroclimatic conditions, consists of 96 precipitation stations across Turkey. Monthly totals and annual means of the monthly totals are formed for each individual station, spanning from 1929 to 1993. In this case, a total of 13 precipitation variables at each station are subjected to trend detection analysis. In addition, regional average precipitation series are established for the same analysis purpose. The application of a trend detection framework resulted in the identification of some significant trends, especially in January, February, and September precipitations and in the annual means. A noticeable decrease in the annual mean precipitation was observed mostly in western and southern Turkey, as well as along the coasts of the Black Sea. Regional average series also displayed trends similar to those for individual stations.

This paper describes the development and application of a procedure that identi®es trends in hydrologic variables. The procedure utilizes the Mann±Kendall non-parametric test to detect trends, a permutation approach to estimate the test distribution, and accounts for the correlation structure in the data in determining the signi®cance level of the test results. The research investigates 18 hydrologic variables that re¯ect different parts of the hydrologic cycle. The hydrologic variables are analyzed for a network of 248 Canadian catchments that are considered to re¯ect natural conditions. A selection of catchments identi®ed to have trends in hydrologic variables is studied further to investigate the presence of trends in meteorological variables and the relationship between the hydrologic and the meteorological response to climatic change. It is concluded that a greater number of trends are observed than are expected to occur by chance. There are differences in the geographic location of signi®cant trends in the hydrologic variables investigated implying that impacts are not spatially uniform.

We summarized the temporal variation in populations of inland trout Salmo, Salvelinus, and Oncorhynchus spp. from streams in North America and determined the statistical power to detect trends over time. The coefficients of variation in abundance averaged 49% (SD ¼ 27%; range ¼ 15-108%) over time for all ages of trout. Temporal variation was lower when more age-classes were monitored, but whether abundance or biomass was more variable differed among populations. Detecting population trends was difficult when using the traditional a ¼ 0.05 criterion. For example, detecting a 5% annual decline with good power (1 -b ! 0.80) would require about 20 years if only one site were monitored. Even when a was relaxed to 0.20, 15 years were required to detect a 5% annual decline when the variation was average. Using a network of sites improved the ability to detect changes: a 5% annual decline at a ¼ 0.05 could be detected in 10 years when 30 sites were monitored. For high-value populations, it may require relaxing a to ensure that declines are detected, even if this increases the risk of claiming change when none has occurred and thus undertaking unnecessary management action. For example, a 5% annual decline could be detected with good power (!0.80) in 8 years when a network of 30 sites is monitored at a ¼ 0.20. Thus, biologists should monitor the least-variable component of a population, monitor a network of sites, and increase a for species of concern to ensure that real population trends are detected. Estimates of trend parameters (and their uncertainty) should be considered in addition to whether or not a statistical test for trend is significant. A pilot study or existing data can help estimate the variation that is typical of the population(s) to be monitored, determine whether trends can be reliably detected, and identify how much risk needs to be incurred to detect trends.

What is corporate reputation? How can it be measured? These two questions have been widely discussed by academics, without coming to a shared definition or evaluation methodology. Each research gave its own corporate reputation definition, but all the studies agree on one point: corporate reputation is the result of the relationship between a company and its stakeholders. On the stakeholder’s opinion rely most of the corporate rep- utation measurement techniques that have been proposed during the past years, techniques that were criticized. In this work, we have investigated if corporate reputation can be evaluated from social media data We focused on the Volkswagen scandal and the buzz it created within the Twitter social network. VW’s scandal was chosen because its widely covered evolution through time and its broad effects on VW’s financial performance. In order to fulfill the research goal, tweets about VW (from 28/8/15 - to 6/6/16) were collected. This vast dataset was firstly ...

The CO 2 fertilization hypothesis stipulates that rising atmospheric CO 2 has a direct positive effect on net primary productivity (NPP), with experimental evidence suggesting a 23% growth enhancement with a doubling of CO 2. Here, we test this hypothesis by comparing a bioclimatic model simulation of NPP over the twentieth century against tree growth increment (TGI) data of 192 Pinus banksiana trees from the Duck Mountain Provincial Forest in Manitoba, Canada. We postulate that, if a CO 2 fertilization effect has occurred, climatically driven simulations of NPP and TGI will diverge with increasing CO 2. We use a two-level scaling approach to simulate NPP. A leaf-level model is first used to simulate high-frequency responses to climate variability. A canopy-level model of NPP is then adjusted to the aggregated leaf-level results and used to simulate yearly plot-level NPP. Neither model accounts for CO 2 fertilization. The climatically driven simulations of NPP for 1912-2000 are effective for tracking the measured year-to-year variations in TGI, with 47.2% of the variance in TGI reproduced by the simulation. In addition, the simulation reproduces without divergence the positive linear trend detected in TGI over the same period. Our results therefore do not support the attribution of a portion of the historical linear trend in TGI to CO 2 fertilization at the level suggested by current experimental evidence. A sensitivity analysis done by adding an expected CO 2 fertilization effect to simulations suggests that the detection limit of the study is for a 14% growth increment with a doubling of atmospheric CO 2 concentration.

The Water Quality Index (WQI) is an essential tool for monitoring trends, conducting scientific research, and making informed decisions. It offers a comprehensive view of the spatiotemporal variation in water quality in a watershed. Water quality data from various environmental assessment sources are often scattered and contain valuable information that researchers do not use. This study addresses the challenge of analyzing temporally and spatially dispersed monitoring data to assess water quality in riverine ecosystems over time. Our objective was to investigate fluctuations in water quality by constructing an aggregate water quality index, combining data from multiple public monitoring initiatives over 40 years in the Mediterranean region of central Chile, specifically in the Mataquito River Basin. Hydrochemical data were obtained from the measurement records available through the National Water Quality Monitoring Network of the Dirección General de Aguas of the Ministry of Public Works, integrating additional information from the water quality output station in the basin, which included sources from the Ministry of the Environment. Outliers for each parameter were removed and a data availability criterion was defined in relation to the total number of campaigns. Missing data were imputed to complete the matrix of water quality parameters. A total of 550 sampling campaigns provided 6624 water quality data points distributed across 10 chemical parameters and two chemical indicators, which were used to calculate the water quality index. The aggregation WQI water quality function was calculated using a modified additive method. Our research revealed that supplemented data offer valuable insights into evaluating water quality in the river ecosystems of the Andean watershed. In the lower zone, the water quality was determined to be good, while in the middle of the basin near the Andean Cordillera, it was considered acceptable. The analysis of the WQI over a decade indicated changes in water quality, irrespective of the station location in the watershed and the level of human intervention. The WQI values suggest a decrease in the water quality over the past 40 years. This study demonstrates that integrating information from diverse environmental monitoring sources into a WQI can be used to assess water quality trends and patterns in the Andean watersheds of Central Chile over time and space. These findings have significant implications for water management and decision making, providing a solid foundation for implementing appropriate strategies aimed at preserving water quality and ensuring water security in central Chile.

In this paper four nonparametric tests for monotonic trend detection are compared with respect to their power and accuracy. The importance of comparing powers at equal empirical significance levels rather than nominal levels is stressed. Therefore, an appropriate graphical method is presented. The effect of the sampling frequency is also assessed using Monte Carlo simulations and a trajectory representation that visualizes the dynamics of the trade-off between the type I and type II errors. These methods are applied to compare four nonparametrical tests (seasonal Mann. Kendall, modified seasonal Mann-Kendall, covariance eigenvalue and covariance inversion) under several conditions. It is concluded with respect to the power that it is not worthwhile for the modified seasonal Mann-Kendall test applied to the AR(1) process considered in this paper to increase the sampling frequency from monthly to biweekly for detecting a monotonic trend of 5 percent, 10 percent, or 15 percent of the process variance. Under these conditions the seasonal Mann-Kendall test is highly liberal, while the covariance inversion and the covariance eigenvalue test are conservative. This research is situated in the development of an efficient sampling design for the Flemish water quality monitoring network.

The present study examines the trends of hydro-climatic parameters over the Black Volta Basin in Ghana. Trend analysis was carried at different time scales (i.e., monthly, seasonal, and annual) from 1961 to 2016. Modified Mann-Kendall (MMK) test, Sen's slope estimates, and Pettit-Mann-Whitney test were applied to compute the existence of a trend, the degree of change, and probable change point, respectively. The results revealed that there are warming trends over the entire Black Volta Basin. Both temperature extremes, i.e., highest and lowest (annual, seasonal, and monthly scale), for upstream and downstream region revealed an increasing trend. The annual rainfall in the upstream region depicted a downward trend, while downstream showed an increasing trend in the Basin. The seasonal trend analysis for rainfall depicted a falling trend (@ Sen's slopes − 0.47 and − 0.69) with a percentage change over the 56 years − 19.66% and − 19.30%, respectively, for upstream and downstream regions during the dry periods. While, the rainy season showed a decreasing rainfall trend (@ Sen's slope − 0.71 and percentage change − 4.41%) for the upstream region and increasing (@ + 0.71 & 4.39%) for the downstream. However, annual rainfall for the sites in the Basin depicted a decreasing trend (@ − 0.88 and − 4.76%) for upstream and an increasing trend (@ + 0.16 with 0.81% change) for downstream region. Annual streamflow revealed an increasing trend (@ + 0.02 with a 1.53% change) over the 43 years for upstream and a decreasing trend (@-0.41 and − 15.04% change) for downstream region at Chache-Bole. Therefore, this study output will be helpful for different stakeholders and policymakers within the Black Volta Basin of the West African sub-region toward improving decisions on water resources management.

Most investigations on the nature and effects of solar ultraviolet (UV) radiation at the Earth's surface require measurements of high accuracy combined with well‐defined procedures to assess their quality. Here we present a general evaluation of all relevant errors and uncertainties associated with measurements of spectral global irradiance in the UV. The uncertainties are quantified in terms of dependence of the characteristics of the spectroradiometer, the uncertainty of calibration standards, the solar zenith angle, and atmospheric conditions. The methodologies and equations presented can be applied to most spectroradiometers currently employed for UV research. The sources of error addressed include radiometric calibration, cosine error, spectral resolution, wavelength misalignment, stability, noise, stray light, and timing errors. The practical application of the method is demonstrated by setting up a complete uncertainty table for the mobile spectroradiometer of the Fraunho...

Climate change could increase the risk of future hydrological extremes over large regional areas and trigger further pressure on water resource availability. In this study, the overall characteristics of dry and wet spells during a 56-year period (1951-2006) were analysed in the Calabria region (southern Italy), considering both yearly and seasonal scales. In particular, the mean and maximum lengths of dry/wet spells, the number of dry and wet spells with different lengths, and the contribution of dry/wet spells with different lengths to the total number of dry/wet days have been investigated. The results showed similar behaviour of both dry and wet spells in the spatial distribution of the mean lengths, with a difference between the two opposite sides (Ionian and Tyrrhenian) of the region. In fact, mean values ranging between 6 and 8 days, and higher than 8 days have been detected on the Tyrrhenian and on the Ionian side, respectively. The results regarding the maximum values of the lengths show much higher values of dry spells than the wet ones, and different temporal distributions throughout the year. In particular, with respect to dry spells, in autumn the maximum lengths reached values higher than 200 days, while the longest wet spells have been identified in winter, with a maximum value of 44 consecutive days with rain. The outcomes of this study can be useful for both water resources (with respect to the dry sequences) and risk reduction (regarding the wet spells) management.

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km2 with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Fur...

Data bases such as MEDATLAS [1] have proved to be an extremely valuable tool for assessing long term changes. Nevertheless, recent works have shown that the irregular time and spatial distribution of data can decrease the robustness of the analyses [2, 3]. ...

The deep waters in the Western Mediterranean (N600 m) are the result of mixing between the two water masses above it (Atlantic Water, 0-200 m and Levantine Intermediate Water, 200-600 m) and heat and buoyancy losses in late winter. Deep waters in the Western Mediterranean have undergone a continuous warming during the second half of the twentieth century and initially it was hypothesized that this had been caused by the warming of the contributing water masses, very likely linked to global warming. Nevertheless, no clear signals of warming have been detected in the intermediate layers and no warming trends were detected in the upper layer before the 1980s. This fact suggested that the cause of deep water warming could be linked to river damming and the consequent salinity increase, instead of to an increase of the heat absorbed by the upper ocean, as in other parts of the world ocean. In this work we use the data base MEDATLAS and data from more recent monitoring programs to construct the longest temperature and salinity time series ever analysed in the Western Mediterranean (1900 to 2008). These time series show that both the upper and intermediate layers have warmed throughout the twentieth century. Long term and decadal variability in the upper layer correlate with surface air temperature in the northern hemisphere and heat absorbed by the upper North Atlantic Ocean, suggesting that the time series analysed in this work reflect the present heat absorption of the oceans in the context of global warming. The present data set highlights the importance of monitoring programs and provides a proxy for the study of climate change.

Trend detection analysis from unstructured data poses a huge challenge to current advanced, web-enabled knowledge-based systems (KBS). Consolidated studies in topic and trend detection from text streams have concentrated so far mainly on identifying and visualizing dynamically evolving text patterns. From the knowledge modeling perspective identifying and defining new, relevant features that are able to synchronize the emergent user intentions to the dynamicity of the system's structure is a need. Additionally the advanced KBS have to remain highly sensitive to the content change, marked by evolution of trends in topics extracted from text streams. In this paper, we are describing a three-layered approach called the "user-system-content method" that is helping us to identify the most relevant knowledge mapping features derived from the USER, SYSTEM and CONTENT perspectives into an overall "context model", that will enable the advanced KBS to automatically str...

This work is a climatological evaluation of terrestrial water storage anomalies (TWSAs), which act as driving forces for sustainable development, in one of the most populous countries of the world. The objective of this work is to evaluate RL06 mascon data from the GRACE and GRACE-FO satellite missions over India to explore seasonal and interannual changes in terrestrial water storage, encompassing an area of ~3.29 million km2 with 285 grid points, from 2002 through to 2020. Several statistical tests are performed to check the homogeneity (i.e., Pettitt’s test, the BRT, the SNHT, and the VNRT). Most of the homogeneous data are found in winter, pre-monsoon, and post-monsoon, approximately above 42% to 47%, and the least are found in monsoons and annual with only 33%, at a 95% significance level. According to Pettitt’s test, the majority of the breakpoints are present in 2014 for winter, 2012 for pre-monsoon, 2011 for monsoons and post-monsoon, and 2008 as well as 2011 for annual. Fur...

The impacts of climate change are already being felt, not only in terms of increase in temperature but also in respect of inadequate water availability. The Mkomazi River Basins (MRB) of the KwaZulu-Natal region, South Africa serves as major source of water and thus a mainstay of livelihood for millions of people living downstream. It is in this context that the study investigates water flows abstraction from headwaters to floodplains and how the water resources are been impacted by seasonal climate variability. Artificial Neural Network (ANN) pattern classifier was utilized for the seasonal classification and subsequence hydrological flow regime prediction between the upstream-downstream anomalies. The ANN input hydroclimatic data analysis results covering the period 2008-2015 provides a likelihood forecast of high, near-median, or low streamflow. The results show that monthly mean water yield range is 28.6-36.0 m 3 /s over the Basin with a coefficient of correlation (CC) values of 0.75 at the validation stage. The yearly flow regime exhibits considerable changes with different magnitudes and patterns of increase and decrease in the climatic variables. No doubt, added activities and processes such as land-use change and managerial policies in upstream areas affect the spatial and temporal distribution of available water resources to downstream regions. The study has evolved an artificial neuron system thinking from conjunctive streamflow prediction toward sustainable water allocation planning for medium-and long-term purposes.

One of the important consequences of the climatic changes is the potential danger of increasing the concentrations of some pollutants, which may cause damages to humans, animals and plants. Therefore, it is worthwhile to study carefully the impact of future climate changes on the high pollution levels. The major topic of the discussion in this paper is the increase of some ozone levels in Bulgaria, but several related topics are also discussed. The particular mathematical tool applied in this study is a large-scale air pollution model, the Unified Danish Eulerian Model (UNI-DEM), which was successfully used in several investigations related to potentially dangerous pollution levels in several European countries. This model is described by a non-linear system of partial differential equations, which is solved numerically by using (a) advanced numerical algorithms and (b) modern computer architectures. Moreover, (c) the code is parallelized and (d) the cache memories of the available computers are efficiently utilized. It is shown that in Bulgaria, as in the other European countries, the climatic changes will result in permanent increases of some quantities related to the ozone pollution. The important issue is that in our study the changes of the dangerous pollution levels are followed year by year. In this way, an attempt is made both to capture the effect of the inter-annual variations of the meteorological conditions on the levels of the ozone concentrations and to follow directly the influence of the climatic changes on the pollution levels. Moreover, the sensitivity of the pollution levels to variations of the human made (anthropogenic) and natural (biogenic) emissions is also discussed.

One of the important consequences of the climatic changes is the potential danger of increasing the concentrations of some pollutants, which may cause damages to humans, animals and plants. Therefore, it is worthwhile to study carefully the impact of future climate changes on the high pollution levels. The major topic of the discussion in this paper is the increase of some ozone levels in Bulgaria, but several related topics are also discussed. The particular mathematical tool applied in this study is a large-scale air pollution model, the Unified Danish Eulerian Model (UNI-DEM), which was successfully used in several investigations related to potentially dangerous pollution levels in several European countries. This model is described by a non-linear system of partial differential equations, which is solved numerically by using (a) advanced numerical algorithms and (b) modern computer architectures. Moreover, (c) the code is parallelized and (d) the cache memories of the available computers are efficiently utilized. It is shown that in Bulgaria, as in the other European countries, the climatic changes will result in permanent increases of some quantities related to the ozone pollution. The important issue is that in our study the changes of the dangerous pollution levels are followed year by year. In this way, an attempt is made both to capture the effect of the inter-annual variations of the meteorological conditions on the levels of the ozone concentrations and to follow directly the influence of the climatic changes on the pollution levels. Moreover, the sensitivity of the pollution levels to variations of the human made (anthropogenic) and natural (biogenic) emissions is also discussed.

Climatic changes have a significant impact on the hydrologic behavior of a river especially its discharge. Sustainable management of water resources necessitates an examination of spatiotemporal variation in climatic parameters such as precipitation and temperature to quantify their relationships with the river discharge. The present study develops both parametric and non-parametric models by modifying the tools of 'R' statistical software (version 3.2.2) to investigate variations in 11 climatic parameters for a 117-year dataset (1900-2017) in Ganges River basin of India. The novelty of the modified hydroclimatic spatiotemporal trend model is its ability to explore seasonal trends and Sen's slopes of climatic parameters while avoiding trend-autocorrelation complications using an advanced Mann-Kendall test and Sen's slope estimator. Furthermore, relationships among the Sen's slopes of each climatic parameter are assessed to investigate the trend interdependencies. Parametric modeling has been performed to develop relationships among precipitation and remaining climatic parameters. Model validation results suggest non-parametric model to quantify relationships between precipitation and river discharge for a long-term data series. The results demonstrate that the forecasted precipitation exhibits a gradually decreasing trend leading to a significantly decreasing trend in river discharge (15-21%) for the next three decades (2030, 2040 and 2050). The model outcomes guide the water managers towards framing sustainable policies for managing water supplies, floods and droughts, hydropower development, barrage operation control, and environmental flows. Keywords Trend analysis Á Climatic change impacts Á River discharge Á Hydro-climatic models Electronic supplementary material The online version of this article (

The impacts of climate change are already being felt, not only in terms of increase in temperature but also in respect of inadequate water availability. The Mkomazi River Basins (MRB) of the KwaZulu-Natal region, South Africa serves as major source of water and thus a mainstay of livelihood for millions of people living downstream. It is in this context that the study investigates water flows abstraction from headwaters to floodplains and how the water resources are been impacted by seasonal climate variability. Artificial Neural Network (ANN) pattern classifier was utilized for the seasonal classification and subsequence hydrological flow regime prediction between the upstream-downstream anomalies. The ANN input hydroclimatic data analysis results covering the period 2008-2015 provides a likelihood forecast of high, near-median, or low streamflow. The results show that monthly mean water yield range is 28.6-36.0 m 3 /s over the Basin with a coefficient of correlation (CC) values of 0.75 at the validation stage. The yearly flow regime exhibits considerable changes with different magnitudes and patterns of increase and decrease in the climatic variables. No doubt, added activities and processes such as land-use change and managerial policies in upstream areas affect the spatial and temporal distribution of available water resources to downstream regions. The study has evolved an artificial neuron system thinking from conjunctive streamflow prediction toward sustainable water allocation planning for medium-and long-term purposes.

Most of the existing text mining approaches are proposed, keeping in mind, transaction databases model. Thus, the mined dataset is structured using just one concept: the "transaction", whereas the whole dataset is modeled using the "set" abstract type. In such cases, the structure of the whole dataset and the relationships among the transactions themselves are not modeled and consequently, not considered in the mining process. We believe that taking into account structure properties of hierarchically structured information (e.g. textual document, etc …) in the mining process, can leads to best results. For this purpose, an hierarchical associations rule mining approach for textual documents is proposed in this paper and the classical set-oriented mining approach is reconsidered profits to a Direct Acyclic Graph (DAG) oriented approach. Natural languages processing techniques are used in order to obtain the DAG structure. Based on this graph model, an hierarchical bottom up algorithm is proposed. The main idea is that each node is mined with its parent node.

This paper presents the formulation and evaluation of effective algorithms of reliable data analysis for real-time monitoring of incipient faults and anomalies, data fusion and event classification. The objective is to alleviate the shortcomings of the existing techniques for data mining by taking advantage of nonlinear filtering to handle non-Gaussian and non-stationary multiplicative noise and uncertainties. New concepts have been developed toward characterization of the data features and behavior interpretation of the underlying processes to evaluate their performance. In particular, the techniques of wavelet transform, Hilbert-Huang transform, and symbolic encoding are investigated to explore their effectiveness and relative simplicity to interpret and implement data mining tasks.

The response of a selected number of chemical species is inspected with respect to climate change. The coupled Atmosphere-Ocean General Circulation Model ECHAM4-OPYC3 is providing meteorological fields for the Chemical long-range Transport Model DEHM. Three selected decades (1990s, 2040s and 2090s) are inspected. The 1990s ACPD

Co-seismic changes in groundwater regime are often observed after moderate to strong earthquakes. The 24 August 2016 M w 6.0 extensional Amatrice earthquake, which was the first event of a long-lasting seismic sequence, including the 30 October 2016 M w 6.5 Norcia event, triggered a significant discharge alteration to the Pescara di Arquata spring, located in the Umbria-Marche Apennines (Northern Apennines, Central Italy) and exploited for drinking purposes. During the first five months after the first mainshock, an extra flow of about 30% was recorded, while both water chemistry and temperature did not show significant changes. Thereafter, the spring discharge decreased significantly, and at the end of 2019 it was still lower than normal. The Standardized Precipitation Index (SPI) indicates that these low mean monthly discharge values are not related to particularly dry conditions. The increase in post-seismic depletion coefficients indicates that the aquifer empties faster than it did during the inter-seismic period. The observed transient increase and subsequent decrease of discharge are consistent with a transient, earthquake-related increase in hydraulic conductivity.

The information on the Web increases tremendously. A number of search engines have been developed for searching Web information and retrieving relevant documents that satisfy the inquirers needs. Search engines provide inquirers irrelevant documents among search results, since the search is text-based rather than semantic-based. Information retrieval research area has presented a number of approaches and methodologies such as profiling, feedback, query modification, human-computer interaction, etc for improving search results. Moreover, information retrieval has employed artificial intelligence techniques and strategies such as machine learning heuristics, tuning mechanisms, user and system vocabularies, logical theory, etc for capturing user's preferences and using them for guiding the search based on the semantic analysis rather than syntactic analysis. Although a valuable improvement has been recorded on search results, the survey has shown that still search engines users are...

Social media platforms and microblogs have become popular fora where the general public expresses opinions and concerns on a variety of matters. As a result, private and public organizations have been looking into ways for finding, understanding and communicating insights extracted from this massive amount of text-based interconnected data. There are, however, important difficulties associated with the noisiness and reliability of the content that hinder the analysis of the data. This paper reports the main challenges found in a real-world experience with social media used as a source of data to support policy making and assessment. We also propose a set of strategies for the precise retrieval of data, the profiling of social media users, and the involvement of policy makers in the analytical process.

Rainfall is an irregular phenomenon showing large spatial variability. Unfortunately, spatially distributed data are not always available, because rain gauge networks are not continuously dense and adequately distributed, and they provide only point measurements. In this work, the temporal variability of rainfall in Italy has been investigated using the Tropical Rainfall Measuring Mission monthly product for the period 1998-2018. Within this aim, more than 650 grid points falling within the Italian territory have been selected. In order to evaluate the presence of trends and their significance, the monthly, seasonal and annual rainfall series were analysed at each grid point with the Mann-Kendall nonparametric test and the Theil-Sen estimator. The broad extension of the area under investigation allowed a better understanding of rainfall trend patterns over space. At annual scale, results evidenced few relevant trends, either positive or negative, involving central Italy (negative trend) and southern Italy (positive trend). A similar trend behaviour has been identified, at seasonal scale, in autumn, with a clear spatial difference between the north and the south of Italy, in winter for the negative trend and in spring for the positive one. In summer, only few grid points in central Italy showed a significant increasing trend.

Due to the important role of water resources in the growth of the world’s economy, drought causes global concern for its severe worldwide implications on different sectors, such as biodiversity, farming, public water supply, energy, tourism, human health, and ecosystem services. In particular, drought events can have strong environmental and socioeconomic impacts in countries depending on rain-fed agriculture such as the ones in the Mediterranean region, which, due to a detected increase in warming and precipitation decrease, is considered a climate change hotspot. In this context, in this paper, meteorological drought in the Calabria region (southern Italy) has been characterized considering the Standardized Precipitation Index (SPI) evaluated at different timescales. First, the temporal distribution of the most severe dry episodes has been evaluated. Then, a trend analysis has been conducted considering the different seasons, the wet (autumn and winter) and dry (spring and summer)...

Rainfall is an irregular phenomenon showing large spatial variability. Unfortunately, spatially distributed data are not always available, because rain gauge networks are not continuously dense and adequately distributed, and they provide only point measurements. In this work, the temporal variability of rainfall in Italy has been investigated using the Tropical Rainfall Measuring Mission monthly product for the period 1998-2018. Within this aim, more than 650 grid points falling within the Italian territory have been selected. In order to evaluate the presence of trends and their significance, the monthly, seasonal and annual rainfall series were analysed at each grid point with the Mann-Kendall nonparametric test and the Theil-Sen estimator. The broad extension of the area under investigation allowed a better understanding of rainfall trend patterns over space. At annual scale, results evidenced few relevant trends, either positive or negative, involving central Italy (negative trend) and southern Italy (positive trend). A similar trend behaviour has been identified, at seasonal scale, in autumn, with a clear spatial difference between the north and the south of Italy, in winter for the negative trend and in spring for the positive one. In summer, only few grid points in central Italy showed a significant increasing trend.

A spatio-temporal approach is presented for the assessment of trends in river monitoring network (RMN) data. In contrast with most existing meth-ods used for analyzing river data, our model incorporates the spatio-temporal dependence structure explicitly. The temporal dependence structure is assumed to follow an AR(1) process. The spatial dependence structure accounts for the flow direction and is implied by the river topology. The dependence structure is represented by a latent state variable which is further embedded into an ob-servation model to make the correlation structure less rigid. A semi-parametric model is used for the marginal mean, modelling the seasonal trend (ST) and a nonparametric long term trend (NLT). The model is applied to a case study of the river Ijzer (Belgium), where the NLT is shown to be decreasing over the last couple of years.

Since the introduction of the Water Framework Directive (WFD), regional authorities have to take actions to improve the aquatic environment. In Flanders (Belgium), for instance a Manure Decree (MD) has been introduced to reduce the impact of eutrophication. It is of course important to assess the evolution of the water quality. In this context, we develop a spatio-temporal method for the assessment of trends in river monitoring network data. The aim is to detect trends on a more regional scale. In contrast with most existing methods used for analysing river data, our model incorporates the spatio-temporal dependence structure explicitly. An AR(1) process is assumed for the temporal dependence. The spatial dependence is implied by the river topology and the flow direction. The dependence structure is modelled by a state variable which is embedded into an observation model that makes the correlation structure less rigid. An additive model incorporates a nonparametric long-term trend and it corrects for seasonal effects. To detect when the local trend is beneficial, tests on the first derivative of the nonparametric long-term trend are performed at each time step. This, however, results in a large number of simultaneous tests. To correct for multiplicity, we propose to use a variant of the free step-down method of Westfall and Young (1993) to take these dependences into account. The methodology is applied to a case study on the river Yzer (Belgium). A significant decrease in the nitrate concentration is detected after the implementation of the MD.

The effects of temperature and rainfall changes on hydropower generation in Ghana from 1960-2011 were examined to understand country-wide trends of climate variability. Moreover, the discharge and the water level trends for the Akosombo reservoir from 1965-2014 were examined using the Mann-Kendall test statistic to assess localised changes. The annual temperature trend was positive while rainfall showed both negative and positive trends in different parts of the country. However, these trends were not statistically significant in the study regions in 1960 to 2011. Rainfall was not evenly distributed throughout the years, with the highest rainfall recorded between 1960 and 1970 and the lowest rainfalls between 2000 and 2011. The Mann-Kendall test shows an upward trend for the discharge of the Akosombo reservoir and a downward trend for the water level. However, the discharge irregularities of the reservoir do not necessarily affect the energy generated from the Akosombo plant, but rather the regular low flow of water into the reservoir affected power generation. This is the major concern for the operations of the Akosombo hydropower plant for energy generation in Ghana.

Statistical monitoring techniques like CUSUM, Trigg's tracking signal and EMP filtering have a major advantage over more recent techniques, such as Kalman filtering, because of their inherent simplicity. In many biomedical applications, such as electronic implantable devices, these simpler techniques have greater utility because of the reduced requirements on power, logic complexity and sampling speed. The determination of signal means using some of the earlier techniques are reviewed in this paper, and a new Monte Carlo based method with greater capability to sparsely sample a waveform and obtain an accurate mean value is presented. This technique may find widespread use as a trend detection method when reduced power consumption is a requirement.

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A study of the trends and variability of hydrological variables was conducted for natural streamflow gauging stations within the Liard River basin in northern Canada. Trends were investigated using the Mann-Kendall test, with an approach that corrects for serial correlation. The field significance of the results was evaluated using a bootstrap resampling approach. The relationships between trends in hydrological variables and both meteorological variables and a large-scale oceanic and atmospheric process were investigated using correlation analysis. The results reveal more trends in some hydrological variables than are expected to occur by chance. The observed trends are related to both trends in meteorological variables and a large-scale oceanic and atmospheric process.

Co-seismic changes in groundwater regime are often observed after moderate to strong earthquakes. The 24 August 2016 Mw 6.0 extensional Amatrice earthquake, which was the first event of a long-lasting seismic sequence, including the 30 October 2016 Mw 6.5 Norcia event, triggered a significant discharge alteration to the Pescara di Arquata spring, located in the Umbria-Marche Apennines (Northern Apennines, Central Italy) and exploited for drinking purposes. During the first five months after the first mainshock, an extra flow of about 30% was recorded, while both water chemistry and temperature did not show significant changes. Thereafter, the spring discharge decreased significantly, and at the end of 2019 it was still lower than normal. The Standardized Precipitation Index (SPI) indicates that these low mean monthly discharge values are not related to particularly dry conditions. The increase in post-seismic depletion coefficients indicates that the aquifer empties faster than it d...

In recent years with the advancement in internet technologies and increase of smartphone adoption among youngsters has made information easily accessible in different forms such as text, images, audio and videos. Today communication has become very fast, it is very much possible that an event that happens in any part of the world gets communicated in few seconds/minutes to the rest of the world. This demands for a system that can automatically identify events such as bomb blasts, floods, cyclone, fires, political events etc., reported in various Newswires and Social Media text. In this paper we explore the development of an event extraction system for Malayalam Language. This work was done as part of the shared task on Event Extraction from Newswires and Social Media Text in Indian Languages (EventXtract-IL) in Forum for Information Retrieval and Evaluation(FIRE 2017). The experiments were done on the standard data set provided and the results showed that the system was able to give...

We have written a computer program called TrenD, for automated trend detection during process monitoring. The program uses a representation called ttwzd templates that define disorders as typical patterns of relevant variables. These patterns consist of a partially ordered set of temporal intervals with uncertain endpoints. Attached to each temporal interval are value constraints on real-valued functions of measurable parameters. As TrenD, receives measured data of the monitored process, the program creates hypotheses of how the process has varied over time. We introduce the importance of a distinct trend representation in knowledge-based systems. Then we demonstrate how trend templates may represent trends that occur at fixed times or at unknown times, and their utility for domains that are quantitatively both poorly and well understood. Finally we present experimental results of TrenD, diagnosing pediatric growth disorders from heights, weights, bone ages, and pubertal data of twe...

In previous work we have defined our trend template epistemology for clinically significant trends and we have illustrated and tested a program TrenDx that monitors time-ordered process data by matching the data to trend templates. Our initial application domain was pediatric growth monitoring. For this symposium we have explored monitoring hemodynamic and respiratory parameters of intensive care unit patients. This application has highlighted the needs for advances in our representation and monitoring algorithms. In particular, we have added reasoning with uncertainty to the trend template epistemology, and a new control structure allowing numerical ranking of competing trend templates. We also have added persistence heuristics to reduce branching of the different chronologies for how data matches a trend template. We demonstrate these techniques on a set of trend templates that monitor if oxygen handbagging is adequate or compromises venous return. 1