The presented paper compares forecast of drought indices based on two different models of artificial neural networks. The first model is based on feedforward multilayer perceptron, sANN, and the second one is the integrated neural network model, hANN. The analyzed drought indices are the standardized precipitation index (SPI) and the standardized precipitation evaporation index (SPEI) and were ...

the impact of meteorological and hydrological drought on groundwater resources in coastal deserts in the south of iran was investigated during 1991-2011, using standardized precipitation index (spi), standardized runoff index (sri), and groundwater resources index (gri). the results indicated that wet and drought spells governed the area in the first and second decades, respectively, which was ...

In this paper, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) is analyzed for the assessment of meteorological drought. The evaluation is conducted over China at 0.5 ̋ spatial resolution against a ground-based gridded China monthly Precipitation Analysis Product (CPAP) from 1983 to 2014 (32 years). The Standardized Pr...

This paper focus on the drought monitoring and forecasting for semi-arid region based various machine learning models SPI index. Drought phenomena are crucial role in agriculture drinking purposes area. In this study, Standardized Precipitation Index (SPI) was used to predicted future upper Godavari River basin, India. We have selected ten input combinations of ML model were prediction three ti...

Abstract In recent years, greenhouse gas emissions have caused extensive changes in the global climate. Climate change leads to extreme events, such as droughts. The present study investigates precipitation and temperature variations past future drought characteristics Iran through data from 40 synoptic stations 33 general circulation models (GCMs) under RCP4.5 RCP8.5 scenarios. As a first step...