Evaluation of drought events in various climatic conditions using data-driven models and a reliability-based probabilistic model

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 the 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 the definition of the 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 (T-max), minimum temperature (T-min), average temperature (T-mean), relative humidity (R-H), 24-h rainfall (P-24) and wind speed (U-2) 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.

Automated summary

This study established SPEI values for various climates using three robust Artificial Intelligence (AI) models: Gene Expression Programming (GEP), Model Tree (MT), and Multivariate Adaptive Regression Spline (MARS). The M5 version of MT was found to provide the most accurate SPEI prediction across all climatic situations in comparison with GEP and MARS techniques. Reliability analysis for all synoptic stations indicated that as the probability of exceedance values declined to below 75%, drought situations varied from Normal to Very Extreme Humidity.