Drought prediction Using Deep Learning

Abstract

Drought is a complex natural disaster that will directly impact human living status, not only the living status of individuals but also the economic status. The best prediction of whether there is a drought in the upcoming seasons or not is important for effective water resource management and agricultural planning. This study presents analysis of various methods to drought prediction using Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) based approaches. We utilized a model to forecast rainfall, maximum temperature, and minimum temperature, which are critical indicators of drought conditions. The study leverages the strengths of Artificial Neural Network to estimate and predict drought for specific regions. The deep learning model was trained and validated with an extensive dataset that includes over 30 years of monthly records for precipitation, maximum temperature, and minimum temperature from various geographical locations. Based on these forecasts, we utilize the Standardized Precipitation Evapotranspiration Index (SPEI) to predict drought conditions. Our evaluation of various LSTM-based models, including Vanilla LSTM, Stacked LSTM, Bidirectional LSTM, Stacked Bidirectional LSTM, CNN- LSTM, and Conv LSTM, revealed that the Vanilla LSTM and Conv LSTM models performed best for short-term predictions, especially for SPEI-3, achieving high scores in accuracy, precision, recall, and F1- measure. Still, all models showed a decline in performance for longer timescales demonstrating the challenge of maintaining accuracy over extended period.