In recent decades, substantial efforts have been devoted in flood monitoring, prediction, and risk analysis for aiding event preparedness plans mitigation measures. Introducing an initial framework of spatially probabilistic research, this study highlights integrated statistical copula satellite data-based approach to modelling the complex dependence structures between characteristics, i.e., du...

Drought prediction is the most effective way to mitigate drought impacts. The current study examined ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, AR-RSS-M5P) in predicting standardized precipitation evapotranspiration index (SPEI) multiple time scales. SPEIs were calcu...

in order to avoid drought effects, it is essential to detect and monitor the spatial andtemporal changes of the phenomenon. in general, drought indices are made use of toachieve the goals. the main aim followed in this paper is to introduce and assess a newdrought index termed mrdi. mrdi (modified reconnaissance drought index) is thencompared with modified standardized precipitation index (mspi...

in this paper by using the data related to the monthly precipitation of 13 stations situated in west azerbaijan province of iran in a time period of 34 years, monitoring and forecasting the probability of a drought occurrence is evaluated in next coming months. in this process, first the monthly precipitation amounts for each rainfall station are used in order to calculate the standardized prec...

Drought is a major environmental disaster in many parts of the world. Knowledge about the timing, severity and extentof drought can aid planning and decision-making. Drought indices derived from in-situ meteorological data have coarsespatial and temporal resolutions, thus, obtaining a real-time drought condition over a large area is difficult. This studyused advanced very high resolution radiom...