Due to a wide range of socio-economic losses caused by drought over the past decades, having a reliable insight of drought properties plays a key role in monitoring and forecasting the drought situations, and finally generating robust methodologies for adapting to various vulnerability of drought situations. The most important factor in causing drought is rainfall, but increasing or decreasing the temperature and consequently evapotranspiration can intensify or moderate the severity of drought events. Standardized Precipitation Evaporation Index (SPEI), as one of the most well-known indices in definition of drought situation, is applied based on potential precipitation, evapotranspiration, and the water balance. In this study, values of SPEI are formulated for various climates by three robust Artificial Intelligence (AI) models: Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS). Meteorological variables including maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tmean), relative humidity (RH), 24-hour rainfall (P24) and wind speed (U2) were used to perform the AI models. Dataset reported from four synoptic stations through Iran, dating back to a 58-year period beginning in 1957. Each AI technique was run for all the climatic situations: Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C) and Arid-Warm (A-W). Results of AI models development indicated that M5 version of MT provided the most accurate SPEI prediction for all the climatic situations in comparison with GEP and MARS techniques. SPEI values for four climatic conditions were evaluated in the reliability-based probabilistic framework to take into account the influence of any uncertainty and randomness associated with meteorological variables. In this way, the Monte-Carlo scenario sampling approach has been used to assess the limit state function from the AI models-based-SPEI. Based on the reliability analysis for all the synoptic stations, as the probability of exceedance values declined to below 75%, drought situations varied from "Normal" to "Very Extreme Humidity".
Excessive population growth and high water demands have significantly increased water extractions from deep and semi-deep wells in the arid regions of Iran. This has negatively affected water quality in different areas. The Water Quality Index (WQI) is a suitable tool to assess such impacts. This study used WQI and the fuzzy hierarchical analysis process of the water quality index (FAHP-WQI) to investigate the water quality status of 96 deep agricultural wells in the Yazd-Ardakan Plain, Iran. Calculating the WQI is time-consuming, but estimating WQI is inevitable for water resources management. For this purpose, three Machine Learning (ML) algorithms, namely, Gene Expression Programming (GEP), M5P Model tree, and Multivariate Adaptive Regression Splines (MARS), were employed to predict WQI. Using Wilcox and Schoeller charts, water quality was also investigated for agricultural and drinking purposes. The results demonstrated that 75% and 33% of the study area have good quality, based on the WQI and FAHP-WQI methods, respectively. According to the results of the Wilcox chart, around 37.25% of the wells are in the C3S2 and C3S1 classes, which indicate poor water quality. Schoeller’s diagram placed the drinking water quality of the Yazd-Ardakan plain in acceptable, inadequate, and inappropriate categories. Afterwards, WQI, predicted by means of ML models, were compared on several statistical criteria. Finally, the comparative analysis revealed that MARS is slightly more accurate than the M5P model for estimating WQI.
Due to a wide range of socio-economic losses caused by drought over the past decades, having a reliable insight of drought properties plays a key role in monitoring and forecasting the drought situations, and finally generating robust methodologies for adapting to various vulnerability of drought situations. The most important factor in causing drought is rainfall, but increasing or decreasing the temperature and consequently evapotranspiration can intensify or moderate the severity of drought events. Standardized Precipitation Evaporation Index (SPEI), as one of the most well-known indices in definition of drought situation, is applied based on potential precipitation, evapotranspiration, and the water balance. In this study, values of SPEI are formulated for various climates by three robust Artificial Intelligence (AI) models: Gene-Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS). Meteorological variables including maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tmean), relative humidity (RH), 24-hour rainfall (P24) and wind speed (U2) were used to perform the AI models. Dataset reported from four synoptic stations through Iran, dating back to a 58-year period beginning in 1957. Each AI technique was run for all the climatic situations: Temperate-Warm (T-W), Wet-Warm (W-W), Arid-Cold (A-C) and Arid-Warm (A-W). Results of AI models development indicated that M5 version of MT provided the most accurate SPEI prediction for all the climatic situations in comparison with GEP and MARS techniques. SPEI values for four climatic conditions were evaluated in the reliability-based probabilistic framework to take into account the influence of any uncertainty and randomness associated with meteorological variables. In this way, the Monte-Carlo scenario sampling approach has been used to assess the limit state function from the AI models-based-SPEI. Based on the reliability analysis for all the synoptic stations, as the probability of exceedance values declined to below 75%, drought situations varied from “Normal” to “Very Extreme Humidity”.
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