meteorological variables

Objective: This article proposes a prediction model applicable to the propagation of noise generated by fixed sources as the result of an analysis of phenomena related to the generation and propagation of sound levels and the subsequent correlation between the levels estimated with the data recorded in the field. Materials and methods: An experimental program was designed that included the measurement of sound pressure levels with a sound level meter to different weather conditions and distances from the noise emission source to compare them with the levels estimated by ISO 9613 Part 2. A statistical analysis of the data recorded in field was performed to observe the dependence with the meteorological variables recorded during the measurements. Results and discussion:  Standard error for the proposed prediction method is 11.4 dB, and absolute average error is 9.1 dB. The correlation coefficient of the proposed model is 0.87. A statistically significant relationship exists between th...

In Argentina, the rotavirus disease exhibits seasonal variations, being most prevalent in the fall and winter months. To deepen the understanding of rotavirus seasonality in our community, the influence of meteorological factors on the rotavirus load and the genetic diversity in urban raw sewage from Córdoba city, Argentina were evaluated. Wastewater samples were collected monthly during a three-year study period and viral particles were concentrated by polyethylene glycol precipitation. RT-nested PCR was applied for rotavirus detection, and VP7/VP4 characterization and real-time PCR for rotavirus quantification. Both molecular techniques showed relatively similar sensitivity rates and revealed rotavirus presence in urban wastewater in cold and warm seasons, indicating its circulation in the local community all year round. However, a slight trend for rotavirus circulation was noted by real-time PCR in the fall and winter seasons, showing a significantly higher peak of rotavirus concentration at mean temperatures lower than 18°C and also higher, although not statistically different during drier weather. VP7 and VP4 gene characterization showed that G1 and P[8] genotypes were dominant, and temporal variations in genotype distribution were not observed. Rotavirus spread is complex and our results point out that weather factors alone cannot explain the seasonal quantitative pattern of the rotavirus disease. Therefore, alternative transmission routes, changes in human behavior and susceptibility, and the stability and survivability of the virus might all together contribute to the seasonality of rotavirus. The results obtained here provide evidence regarding the dynamics of rotavirus circulation and maintenance in Argentina.

In Argentina, the rotavirus disease exhibits seasonal variations, being most prevalent in the fall and winter months. To deepen the understanding of rotavirus seasonality in our community, the influence of meteorological factors on the rotavirus load and the genetic diversity in urban raw sewage from Córdoba city, Argentina were evaluated. Wastewater samples were collected monthly during a three-year study period and viral particles were concentrated by polyethylene glycol precipitation. RT-nested PCR was applied for rotavirus detection, and VP7/VP4 characterization and real-time PCR for rotavirus quantification. Both molecular techniques showed relatively similar sensitivity rates and revealed rotavirus presence in urban wastewater in cold and warm seasons, indicating its circulation in the local community all year round. However, a slight trend for rotavirus circulation was noted by real-time PCR in the fall and winter seasons, showing a significantly higher peak of rotavirus concentration at mean temperatures lower than 18°C and also higher, although not statistically different during drier weather. VP7 and VP4 gene characterization showed that G1 and P[8] genotypes were dominant, and temporal variations in genotype distribution were not observed. Rotavirus spread is complex and our results point out that weather factors alone cannot explain the seasonal quantitative pattern of the rotavirus disease. Therefore, alternative transmission routes, changes in human behavior and susceptibility, and the stability and survivability of the virus might all together contribute to the seasonality of rotavirus. The results obtained here provide evidence regarding the dynamics of rotavirus circulation and maintenance in Argentina.

In Argentina, the rotavirus disease exhibits seasonal variations, being most prevalent in the fall and winter months. To deepen the understanding of rotavirus seasonality in our community, the influence of meteorological factors on the rotavirus load and the genetic diversity in urban raw sewage from Córdoba city, Argentina were evaluated. Wastewater samples were collected monthly during a three-year study period and viral particles were concentrated by polyethylene glycol precipitation. RT-nested PCR was applied for rotavirus detection, and VP7/VP4 characterization and real-time PCR for rotavirus quantification. Both molecular techniques showed relatively similar sensitivity rates and revealed rotavirus presence in urban wastewater in cold and warm seasons, indicating its circulation in the local community all year round. However, a slight trend for rotavirus circulation was noted by real-time PCR in the fall and winter seasons, showing a significantly higher peak of rotavirus concentration at mean temperatures lower than 18°C and also higher, although not statistically different during drier weather. VP7 and VP4 gene characterization showed that G1 and P[8] genotypes were dominant, and temporal variations in genotype distribution were not observed. Rotavirus spread is complex and our results point out that weather factors alone cannot explain the seasonal quantitative pattern of the rotavirus disease. Therefore, alternative transmission routes, changes in human behavior and susceptibility, and the stability and survivability of the virus might all together contribute to the seasonality of rotavirus. The results obtained here provide evidence regarding the dynamics of rotavirus circulation and maintenance in Argentina.

Thirty-seven sandfly species are listed for Argentina distributed in 14 provinces and Leishmaniasis cases extend from the north of the country to Unquillo City (Córdoba Province), but potential vectors are found further to the south. This is the first study on diversity, spatial and temporal distribution of sandflies on the outskirts of the temperate Córdoba City, and the factors that influence their presence. Migonemyia migonei, record here for Córdoba City for the first time, and the Evandromyia cortelezzii-sallesi Complex was found, also Ev. cortelezzii males were captured for the first time, these sandflies being more abundant during the warm months due to meteorological factors and the presence of blood meal sources. At least the eastern outskirts of Córdoba City, the second most important city of the country, are at risk of Leishmaniasis transmission if Leishmania spp. enters into the area due to the presence of competent vectors and adequate vertebrate hosts, in a favorable socioeconomic context.

Thirty-seven sandfly species are listed for Argentina distributed in 14 provinces and Leishmaniasis cases extend from the north of the country to Unquillo City (Córdoba Province), but potential vectors are found further to the south. This is the first study on diversity, spatial and temporal distribution of sandflies on the outskirts of the temperate Córdoba City, and the factors that influence their presence. Migonemyia migonei, record here for Córdoba City for the first time, and the Evandromyia cortelezzii-sallesi Complex was found, also Ev. cortelezzii males were captured for the first time, these sandflies being more abundant during the warm months due to meteorological factors and the presence of blood meal sources. At least the eastern outskirts of Córdoba City, the second most important city of the country, are at risk of Leishmaniasis transmission if Leishmania spp. enters into the area due to the presence of competent vectors and adequate vertebrate hosts, in a favorable s...

Aim of this Paper is to explore which factors have the greatest impact on generating traffic volume and to set up a suitable model for forecasting it for the main road network of Anamorava region. In this regard, several demographic and social economic variables were taken into account for the period 2004-2016. Variability between variables is done through correlative analysis (multi-co linearity problem) between them. After this, models were developed using original data via multiple regression analysis (MLR) and artificial neural network (ANN) methods. However, with the aim of involving as many variables as possible and eliminating the multi co-linearity respectively improving forecasting model capability, a new methodology was used using the principal component analysis (PCA) as an input. These models then are compared based on prediction errors also verified on the basis of some performance indicators. Results show that the use of principal components as inputs improved both models forecasting by reducing their complexity and eliminating data co-linearity.

el periodo 2018-2019. Se utilizó la siguiente metodología: tipo de investigación básica, nivel descriptivo, método científico hipotético-deductivo con diseño no experimental, longitudinal. El instrumento utilizado fue estación meteorológica automatizada de la compañía Weather Link, Marca Davis Instruments, Modelo Vantage Pro y fichas documentadas. La muestra a estudiar fue no probabilística con muestreo por conveniencia. El procesamiento de datos fue mediante software Statistical Analysis System (SAS) 9,4. Los resultados fueron analizados con la estadística descriptiva e inferencial con significancia de 95% de confiabilidad mediante la prueba Anderson-Darling. Se concluye que, las variables meteorológicas del presente estudio siguen un comportamiento esperado para la ciudad de Huancavelica de acuerdo a las estaciones del año. Así también el balance hídrico en el periodo estudiado los meses en que se tiene reposición es decir acumulación de agua en el lugar de estudio corresponde a los meses de agosto, octubre y enero así también los meses de utilización son los meses de setiembre, noviembre, diciembre, abril, mayo, junio y julio.

Dependence of the lead-210 activity concentration in surface air on meteorological variables and teleconnection indices is investigated using multivariate analysis, which gives the Boosted Decision Trees method as the most suitable for variable analysis. A mapped functional behaviour of the lead-210 activity concentration is further obtained, and used to test predictability of lead-210 in surface air. The results show an agreement between the predicted and measured values. The temporal evolution of the measured activities is satisfactorily matched by the prediction. The largest qualitative differences are obtained for winter months.3rd International Conference on Radiation and Applications in Various Fields of Research (RAD), Jun 08-12, 2015, Budva, Montenegr

Water temperature plays an important role in ecological functioning and in controlling the biogeochemical processes of a water body. Conventional water quality monitoring is expensive and time consuming. It is particularly problematic if the water bodies to be examined are large. Conventional techniques also bring about a high probability of undersampling. Conversely, remote sensing is a powerful tool to assess aquatic systems. The objective of this study was to map the surface water temperature and improve understanding of spatiotemporal variations in a hydroelectric reservoir. In this work, MODIS land-surface temperature (LST) level 2, 1-km nominal resolution data (MOD11L2, version 5) were used. All available clear-sky MODIS/Terra images from 2003 to 2008 were used, resulting in a total of 786 daytime and 473 nighttime images. Descriptive statistics (mean, maximum and minimum) were computed for the historical images to build a time series of daytime and nighttime monthly mean temperatures. The thermal amplitude and anomaly were also computed. In-situ meteorological variables were used from 2003 to 2008 to help understand the spatiotemporal variability of the surface water temperature. The surface energy budget and the depth at which the wind can distribute the heat input of a given surface were also measured. A correlation between daytime and nighttime surface water temperatures and the computed heat fluxes were made. These relationships and the causes of the water surface temperature variability are discussed.

El presente trabajo de investigación tiene la finalidad de evaluar el efecto de la variación meteorológica en el balance hídrico de la ciudad de Huancavelica en el periodo 2018-2019. Se utilizó la siguiente metodología: tipo de investigación básica, nivel descriptivo, método científico hipotético- deductivo con diseño no experimental, longitudinal. El instrumento utilizado fue estación meteorológica automatizada de la compañía Weather Link, Marca Davis Instruments, Modelo Vantage Pro y fichas documentadas. La muestra a estudiar fue no probabilística con muestreo por conveniencia. El procesamiento de datos fue mediante software Statistical Analysis System (SAS) 9,4. Los resultados fueron analizados con la estadística descriptiva e inferencial con significancia de 95% de confiabilidad mediante la prueba Anderson-Darling. Se concluye que, las variables meteorológicas del presente estudio siguen un comportamiento esperado para la ciudad de Huancavelica de acuerdo a las estaciones del añ...

New particle formation (NPF) was investigated at a coastal background site in Southwest Spain over a four-year period using a Scanning Particle Mobility Sizer (SMPS). The goals of the study were to characterise the NPF and to investigate their relationship to meteorology, gas phase (O3, SO2, CO and NO2) and solar radiation (UVA, UVB and global). A methodology for identifying and classifying the NPF was implemented using the wind direction and modal concentrations as inputs. NPF events showed a frequency of 24% of the total days analysed. The mean duration was 9.2±4.2 h. Contrary to previous studies conducted in other locations, the NPF frequency reached its maximum during cold seasons for approximately 30% of the days. The lowest frequency took place in July with 10%, and the seasonal wind pattern was found to be the most important parameter influencing the NPF frequency. The mean formation rate was 2.2±1.7 cm(-3) s(-1), with a maximum in the spring and early autumn and a minimum du...

The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatological information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.

In Argentina, the rotavirus disease exhibits seasonal variations, being most prevalent in the fall and winter months. To deepen the understanding of rotavirus seasonality in our community, the influence of meteorological factors on the rotavirus load and the genetic diversity in urban raw sewage from Córdoba city, Argentina were evaluated. Wastewater samples were collected monthly during a three-year study period and viral particles were concentrated by polyethylene glycol precipitation. RT-nested PCR was applied for rotavirus detection, and VP7/VP4 characterization and real-time PCR for rotavirus quantification. Both molecular techniques showed relatively similar sensitivity rates and revealed rotavirus presence in urban wastewater in cold and warm seasons, indicating its circulation in the local community all year round. However, a slight trend for rotavirus circulation was noted by real-time PCR in the fall and winter seasons, showing a significantly higher peak of rotavirus concentration at mean temperatures lower than 18°C and also higher, although not statistically different during drier weather. VP7 and VP4 gene characterization showed that G1 and P[8] genotypes were dominant, and temporal variations in genotype distribution were not observed. Rotavirus spread is complex and our results point out that weather factors alone cannot explain the seasonal quantitative pattern of the rotavirus disease. Therefore, alternative transmission routes, changes in human behavior and susceptibility, and the stability and survivability of the virus might all together contribute to the seasonality of rotavirus. The results obtained here provide evidence regarding the dynamics of rotavirus circulation and maintenance in Argentina.

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET 0). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET 0 was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET 0.

Increasing the accuracy of prediction improves the performance of photovoltaic systems and alleviates the effects of intermittence on the systems stability. A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station. The proposed model uses several inputs (e.g. time, day of the year, sky cover, pressure, and wind speed) to predict hourly solar irradiance. Data obtained from the National Solar Radiation Database (NSRDB) was used to conduct simulation experiments. These simulations validate the use of the proposed model for short-term predictions. Results show that the NARX neural network notably outperformed the other models and is better than the linear regression model. The use of additional meteorological variables, particularly sky cover, can further improve the prediction performance.

Water temperature plays an important role in ecological functioning and in controlling the biogeochemical processes of a water body. Conventional water quality monitoring is expensive and time consuming. It is particularly problematic if the water bodies to be examined are large. Conventional techniques also bring about a high probability of undersampling. Conversely, remote sensing is a powerful tool to assess aquatic systems. The objective of this study was to map the surface water temperature and improve understanding of spatiotemporal variations in a hydroelectric reservoir. In this work, MODIS land-surface temperature (LST) level 2, 1-km nominal resolution data (MOD11L2, version 5) were used. All available clear-sky MODIS/Terra images from 2003 to 2008 were used, resulting in a total of 786 daytime and 473 nighttime images. Descriptive statistics (mean, maximum and minimum) were computed for the historical images to build a time series of daytime and nighttime monthly mean temperatures. The thermal amplitude and anomaly were also computed. In-situ meteorological variables were used from 2003 to 2008 to help understand the spatiotemporal variability of the surface water temperature. The surface energy budget and the depth at which the wind can distribute the heat input of a given surface were also measured. A correlation between daytime and nighttime surface water temperatures and the computed heat fluxes were made. These relationships and the causes of the water surface temperature variability are discussed.

Water temperature plays an important role in ecological functioning and in controlling the biogeochemical processes of a water body. Conventional water quality monitoring is expensive and time consuming. It is particularly problematic if the water bodies to be examined are large. Conventional techniques also bring about a high probability of undersampling. Conversely, remote sensing is a powerful tool to assess aquatic systems. The objective of this study was to map the surface water temperature and improve understanding of spatiotemporal variations in a hydroelectric reservoir. In this work, MODIS land-surface temperature (LST) level 2, 1-km nominal resolution data (MOD11L2, version 5) were used. All available clear-sky MODIS/Terra images from 2003 to 2008 were used, resulting in a total of 786 daytime and 473 nighttime images. Descriptive statistics (mean, maximum and minimum) were computed for the historical images to build a time series of daytime and nighttime monthly mean temperatures. The thermal amplitude and anomaly were also computed. In-situ meteorological variables were used from 2003 to 2008 to help understand the spatiotemporal variability of the surface water temperature. The surface energy budget and the depth at which the wind can distribute the heat input of a given surface were also measured. A correlation between daytime and nighttime surface water temperatures and the computed heat fluxes were made. These relationships and the causes of the water surface temperature variability are discussed.

A multiple model integration scheme driven by artificial neural network (ANN) (MM-ANN) was developed and tested to improve the prediction accuracy of soil hydraulic conductivity (Ks) in Tabriz plain, an arid region of Iran. The soil parameters such as silt, clay, organic matter (OM), bulk density (BD), pH and electrical conductivity (EC) were used as model inputs to predict soil Ks. Standalone models including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), support vector machine (SVM) and extreme learning machine (ELM) were also implemented for comparative evaluation with MM-ANN model predictions. Based on several performance indicators such as Nash Sutcliffe Efficiency (NSE), results showed that the calibrated MM-ANN model involving the predictions of MARS, M5Tree, SVM and ELM models by considering all the soil parameters used in this study as inputs provided superior soil Ks estimates. The proposed hybrid model (MM-ANN) emerged as a reliable intelligence model for the assessment of soil hydraulic conductivity with an NSE = 0.939 & 0.917 during training and testing, respectively. Accurate prediction of field-scale soil hydraulic conductivity is crucial from the view point of agricultural sustainability and management prospects.

Standard Amazonian rainfall climatologies rely on stations preferentially located near river margins. River breeze circulations that tend to suppress afternoon rainfall near the river and enhance it inland are not typically considered when reporting results. Previous studies found surprising nocturnal rainfall maxima near the rivers in some locations. We examine spatial and temporal rainfall variability in the Santarém region of the Tapajós-Amazon confluence, seeking to describe the importance of breeze effects on afternoon precipitation and defining the areal extent of nocturnal rainfall maxima.We used three years of mean S band radar reflectivity from Santarém airport with a Z-R relationship appropriate for tropical convective conditions. These data were complemented by TRMM satellite rainfall estimates. Nocturnal rainfall was enhanced along the Amazon River, consistent with the hypothesis that these are associated with the passage of instability lines, perhaps enhanced by local c...

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical repre...

Soil moisture (SM) is of paramount importance in irrigation scheduling, infiltration, runoff, and agricultural drought monitoring. This work aimed at evaluating the performance of the classical ANFIS (Adaptive Neuro-Fuzzy Inference System) model as well as ANFIS coupled with three bio-inspired metaheuristic optimization methods including whale optimization algorithm (ANFIS-WOA), krill herd algorithm (ANFIS-KHA) and firefly algorithm (ANFIS-FA) in estimating SM. Daily air temperature, relative humidity, wind speed and sunshine hours data at Istanbul Bolge station in Turkey and soil temperature values measured over 2008-2009 were fed into the models under six different scenarios. ANFIS-WOA (RMSE = 1.68, MAPE = 0.04) and ANFIS (RMSE = 2.55, MAPE = 0.07) exhibited the best and worst performance in SM estimation, respectively. All three hybrid models (ANFIS-WOA, ANFIS-KHA and ANFIS-FA) improved SM estimates, reducing RMSE by 34, 28 and 27% relative to the base ANFIS model, respectively. A more detailed analysis of model performances in estimating moisture content over three intervals including [15-25), [25-35) and ≥ 35% revealed that ANFIS-WOA has had the lowest errors with RMSEs of 1.69, 1.89 and 1.55 in the three SM intervals, respectively. From the perspective of under-or over-estimation of moisture values, ANFIS-WOA (RMSE = 1.44, MAPE = 0.03) in underestimation set and ANFIS-KHA (RMSE = 1.94, MAPE = 0.05) in over-estimation set showed the highest accuracies. Overall, all three hybrid models performed better in the underestimation set compared to overestimation set.

In the present study, multilayer perceptron (MLP) based neural network, which is one of the efficient artificial neural network (ANN) was applied for modeling daily rainfall-runoff in a Himalayan watershed called Bino watershed in Almora and Pauri Garhwal districts of Uttarakhand, India using the time series monsoon data of ten years (2000-2009) of rainfall and runoff. Gamma test (GT) technique was applied for selection of the best input combinations. Performance of model developed was evaluated qualitatively as well as quantitatively using indices viz. correlation coefficient (r), root mean square error (RMSE) and coefficient of efficiency (CE). The results of the study showed that model (MLP7) with 5 inputs and one hidden layer with 8 neurons was found to be the best followed by model (MLP 19) having 10 hidden neurons for first hidden layer and 11 for second hidden layer. The r, RMSE and CE values for MLP 7 during testing were determined to be 0.9207, 0.9644 (mm) and 0.7974, respe...

Water resource and environmental engineers need accurate information in harnessing water for diverse uses, therefore it is expedient to accurately predict dry and wet climatic phases in order to ensure optimum water resource planning and management. This study examined the applicability of machine learning models for the prediction of extreme dry and wet conditions in Minna, North Central Nigeria. Recorded rainfall, maximum temperature, minimum temperature, relative humidity, wind speed, sunshine hours and estimated potential evapotranspiration were used as predictors in the machine learning models, while hydrological extremes estimated from standardized precipitation index (SPI) served as a response variable. The performance of Support Vector Machine (SVM) based on different kernel types and Artificial Neural Network (ANN) based on different network structures were assessed for the prediction of the different phases of the climate of the study area. The study showed that while norm...

Proper modeling of rainfall-runoff is essential for water quantity and quality management. However, comprehensive evaluation of soft computing techniques for rainfall-runoff modeling in developing countries is still lacking. Towards this end, in the present study two new soft computing techniques of genetic programming (GP) and M5 model tree were formulated for daily streamflow prediction. Firstly, the daily meteorological and hygrometric data including rainfall, temperature, evapotranspiration, relative humidity and discharge were collected for the years 1970-2012 throughout Amameh Watershed in Tehran, Iran. Secondly, the input variables were determined using cross-correlation and then 62 different scenarios were developed. Thirdly, the data were standardized in the range of zero to one. Finally, performance of the scenarios was assessed using the mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). Totally, 80 and 20 percent of instances were used for training and testing, respectively. The results showed that the scenario number 54 was the best using both GP and M5 model tree techniques. However, GP showed much better performance than M5 model tree with MSE, RMSE, and MAE values of 0.001, 0.031 and 0.009 for training and 0.001, 0.032 and 0.009 for testing, respectively. The scenario 54 had eight inputs including rainfall, discharge, and delay for two days, temperature, evapotranspiration and relative humidity.

Considering the scouring depth downstream of weirs is a challenging issue due to its effect on weir stability. The adaptive neuro-fuzzy inference systems (ANFIS) model integrated with optimization methods namely cultural algorithm, biogeography based optimization (BBO), invasive weed optimization (IWO) and teaching learning based optimization (TLBO) are proposed to predict the maximum depth of scouring based on the different input combinations. Several performance indices and graphical evaluators are employed to estimate the prediction accuracy in the training and testing phase. Results show that the ANFIS-IWO offers the highest prediction performance (RMSE = 0.148) compared to other models in the testing phase, while the ANFIS-BBO (RMSE = 0.411) provides the lowest accuracy. The findings obtained from the uncertainty analysis of prediction modeling indicate that the input variables variability has a higher impact on the predicted results than the structure of models. In general, th...

Appropriate input selection for the estimation matrix is essential when modeling non-linear progression. In this study, the feasibility of the Gamma test (GT) was investigated to extract the optimal input combination as the primary modeling step for estimating monthly pan evaporation (EPm). A new artificial intelligent (AI) model called the co-active neuro-fuzzy inference system (CANFIS) was developed for monthly EPm estimation at Pantnagar station (located in Uttarakhand State) and Nagina station (located in Uttar Pradesh State), India. The proposed AI model was trained and tested using different percentages of data points in scenarios one to four. The estimates yielded by the CANFIS model were validated against several well-established predictive AI (multilayer perceptron neural network (MLPNN) and multiple linear regression (MLR)) and empirical (Penman model (PM)) models. Multiple statistical metrics (normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), Pea...

Este trabajo evalúa la situación de la red hidrometeorológica para la vigilancia del clima en la Huasteca potosina. En los últimos años, la medición y el registro de las variables hidrometeorológicas han pasado de una forma manual a la automatización. Uno de los argumentos para este cambio es la necesidad de contar con una información hidrometeorológica para adaptarse al cambio climático. Al respecto, en este capítulo se discute la fallida política nacional de modernización de la red hidrometeorológica.
Por otro lado, los sistemas de alerta temprana hidrometeorológica permiten prever con antelación situaciones de emergencia provocadas, por ejemplo, por los ciclones tropicales. En este sentido, en el presente texto se hace un análisis de los ciclones tropicales que han provocado precipitaciones en la Huasteca potosina y da un acercamiento a los sistemas de alerta temprana usados en la región.
Por último, en este trabajo se discute el papel que podrían tener los sistemas de vigilancia y de alerta temprana basados en los conocimientos locales del clima. Como el lector puede notar, los tres aspectos abordados atañen directamente al proyecto de investigación global y buscan aportar, en un sentido preciso, de qué manera las medidas de alerta estatal y regional se fundan en información de calidad y fortalecen las decisiones y buenas prácticas locales.

Data-driven models have been explored in numerous studies for solar radiation (R s) prediction. However, the use of different input variable selection (IVS) methods for improving R s prediction accuracy has mostly been neglected. This study explores various IVS methods, including Gamma test (GT), Procrustes analysis (PA) and Edgeworth approximationbased conditional mutual information (EA) and evaluates their ability to improve R s prediction accuracy by coupling them with popular non-linear data-driven models, multilayer perceptron (MLP), support vector machine, extreme learning machine and multi-gene genetic programming (MGGP). The partial correlation input selection method was coupled with multiple linear regression to serve as a linear benchmark. Meteorological data from eight stations in northern Iran was used for building the R s prediction models. The type and number of variables selected at each station was dissimilar and dependent on the IVS method. The models utilizing EA selected fewer variables compared to the GT method and had higher accuracy, while models using PA selected fewer variables than all methods but were not able to adequately predict R s. It was also found that predictive performance substantially varied when pairing the IVS methods with different model types. For example, MLP, the model with the best average performance, when coupled with EA instead of PA resulted in a * 27% improvement (decrease) in the normalized root mean square error (nRMSE). The results also indicated that MGGP produced the least accurate predictions, where the nRMSE increased by up to 40% compared to MLP when the EA method was used for IVS. Finally, IVS hyper-parameter adjustment (which is routinely overlooked in the literature) profoundly affected the results and is recommended as a very important step to consider when developing data-driven models for solar radiation prediction.

The potential of several predictive models including multiple model-artificial neural network (MM-ANN), multivariate adaptive regression spline (MARS), support vector machine (SVM), multi-gene genetic programming (MGGP), and 'M5Tree' were assessed to simulate the pan evaporation in monthly scale (EP m) at two stations (e.g. Ranichauri and Pantnagar) in India. Monthly climatologi-cal information were used for simulating the pan evaporation. The utmost effective input-variables for the MM-ANN, MGGP, MARS, SVM, and M5Tree were determined using the Gamma test (GT). The predictive models were compared to each other using several statistical criteria (e.g. mean absolute percentage error (MAPE), Willmott's Index of agreement (WI), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and Legate and McCabe's Index (LM)) and visual inspection. The results showed that the MM-ANN-1 and MGGP-1 models (NSE, WI, LM, RMSE, MAPE are 0.954, 0.988, 0.801, 0.536 mm/month, 9.988% at Pantnagar station, and 0.911, 0.975, 0.724, and 0.364 mm/month, 12.297% at Ranichauri station, respectively) with input variables equal to six were more successful than the other techniques during testing period to simulate the monthly pan evaporation at both Ranichauri and Pantnagar stations. Thus, the results of proposed MM-ANN-1 and MGGP-1 models will help to the local stakeholders in terms of water resources management.

The geographically nonuniform sea-level change has increased the importance of assessing sea-level variability and the factors
controlling it on regional scales. This study provides a framework, based on the rules governing an artificial neural network (ANN), to identify
an ensemble of large-scale meteorological variables (MVs), which significantly affect long-term monthly water-level anomalies (MWLA) in
northern coast of the Persian Gulf (1990–2013). Horizontal spatial grid cells of 10° × 10°, bounded between (0-100°E and 0-70°N), create a
surface control to address the patterns of six MVs consisting of zonal and meridional wind velocity, total precipitable water, 1,000–500 hPa
thickness, relative humidity, and air temperature at surfaces of 300 and 700 hPa, respectively. Additionally, 14 representative marine regions
are also taken into consideration to assess the potential impact of sea surface temperature (SST) and sea-level pressure (SLP) on local sea-level
variability. The multicollinearity problem is effectively tackled by principal components analysis, which classified the MVs into the independent
categories. A neural network-based pruning algorithm under a statistical hypothesis test is introduced to discern redundant factors,
and then estimate the relative importance of each of the significant predictors in simulating the MWLA. The pruning algorithm detected
the nine meteorological components, which are able to predict up to 56% of the total variance in the MWLA. Moreover, it is found that more
than half of the predicted variability is manifested by zonal wind, SST, and SLP patterns.

The Balbina reservoir (59°28'50'' W, 1°53'25'' S), located near the city of Manaus, AM in central Amazonia, is the second largest hydroelectric reservoir in the Amazon basin. Carbon dioxide concentration measurements were performed at high frequency (10 Hz) at this reservoir with an IRGA Model LiCOR 7500A and meteorological variables were measured with a floating platform with sensors 2 meters above the surface of the lake. Maximum and minimum CO2 concentrations were observed during the night, related to forest breeze enriched with CO2 and the respiration or photosynthetic activity of the lake. Due to the dimensions of the lake, both land and lake breezes control the concentration of CO2. CO2 showed a strong correlation with the meteorological variables, temperature (-0.76) and relative humidity (0.71). However, only the wind direction showed a statistically significant effect at 5% in the cross-correlation. Our results corroborate other studies in this lake and other Amazonian reservoirs.

Supervised learning Advanced regression Evapotranspiration Texas High Plains ET network s u m m a r y Accurate estimates of daily crop evapotranspiration (ET) are needed for efficient irrigation management, especially in arid and semi-arid regions where crop water demand exceeds rainfall. Daily grass or alfalfa reference ET values and crop coefficients are widely used to estimate crop water demand. Inaccurate reference ET estimates can hence have a tremendous impact on irrigation costs and the demands on U.S. freshwater resources, particularly within the Ogallala aquifer region. ET networks calculate reference ET using local meteorological data. With gaps in spatial coverage of existing networks and the agriculturebased Texas High Plains ET (TXHPET) network in jeopardy due to lack of funding, there is an immediate need for alternative sources capable of filling data gaps without high maintenance and field-based support costs. Non-agricultural weather stations located throughout the Texas High Plains are providing publicly accessible meteorological data. However, there are concerns that the data may not be suitable for estimating reference ET due to factors such as weather station siting, fetch requirements, data formats, parameters recorded, and quality control issues. The goal of the research reported in this paper is to assess the use of alternative data sources for reference ET computation. Towards this objective, we trained Gaussian process models, an instance of kernel-based machine learning algorithms, on data collected from weather stations to estimate reference ET values and augment the TXHPET database. Results show that Gaussian process models provide much greater accuracy than baseline least square regression models.

Carbon dioxide (CO 2 ) observations collected at 5 min interval at Sriharikota during October 2011-January 2012 from the Vaisala GMP-343 sensor were averaged on an hourly basis. The baseline of atmospheric CO 2 during study period is 382 ppm. Minimum (maximum) mixing ratios were observed during the afternoon (night times) indicating the role of photosynthetic activity and the atmospheric boundary layer on this parameter. Sriharikota being a coastal station, the land and sea breezes mainly control CO 2 mixing ratios. The correlation between CO 2 and the wind speed is significantly less during sea breeze than during land breeze in October, compared to other months, where the correlations are more during sea breeze. The less correlation during sea breeze in October is due to the heavy rainfall in this month during daytime.