Spatio-temporal modeling of climate change impacts on drought forecast using Generative Adversarial Network: A case study in Africa

Drought is an extreme weather event, affecting the ecological conditions of vegetation and agricultural productivity, poses challenges for millions of people in Africa, and its long-term prediction is definitely important. Accurate drought forecasting is a challenging subject due to its dependence on different climatic variables, and its spatio-temporal, nonstationary and non-linear characteristics. In particular, Deep Learning technologies have achieved excellent results in long-term time series forecasting. Thus, this study proposes a Generative Adversarial Networks (GAN) model which combines Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for drought forecasting in Africa. This approach focuses on how the future spatio-temporal variations of drought will vary under climate change effects using multivariate remote sensing data over Africa from 1999–2022. We considered hydrological, meteorological and vegetation spectral factors for GAN as model input variables. The study assessed agricultural drought using the soil moisture index (SMI) as a response parameter. Experimental results confirmed the reliability of the proposed model for forecasting agricultural drought. Compared to existing deep learning models, the proposed GAN based CNN-LSTM model achieved the lowest RMSE, MAPE, and MAE values of 1.008, 0.009, and 0.739, respectively. The findings demonstrate that the proposed model can be used as a reliable forecasting method that helps to estimate drought in arid and semi-arid regions.