Forecasting Drought via Soft-Computation Multi-layer Perceptron Artificial Intelligence Model

Abstract

Drought is a natural and gradual threat, with many devastating consequences for all aspects of human life. Accurate drought forecasting is a promising step to help decision-makers develop strategies to manage drought risks. To achieve this goal, choosing a suitable model plays a vital role in the forecasting method. Various artificial neural network (ANN) models are used to predict short-term and long-term droughts on different time scales using the Standardized Precipitation Index (SPI), including 3, 6, 12, 24, and 48 months in Rajasthan and Gujarat. Due to the frequent danger of drought, people today are facing many environmental challenges. It affects the environment of the country, community, and industry. Some of the adverse effects of the drought threat persist in Pakistan, including other threats. However, early measurement and identification of drought can guide water resources management to use drought-resistant strategies. In this article, we use the Multilayer Perceptron Neural Network (MLPNN) algorithm to predict drought. Seventeen weather stations in Daman and Diu.