Advance drought prediction through rainfall forecasting with hybrid deep learning model
Brij B. Gupta, Akshat Gaurav, Razaz Waheeb Attar, Varsha Arya, Shavi Bansal, Ahmed Alhomoud, Kwok Tai Chui
Abstract
Drought is a natural disaster that can affect a larger area over time. Damage caused by the drought can only be reduced through its accurate prediction. In this context, we proposed a hybrid stacked model for rainfall prediction, which is crucial for effective drought forecasting and management. In the first layer of stacked models, Bi-directional LSTM is used to extract the features, and then in the second layer, the LSTM model will make the predictions. The model captures complex temporal dependencies by processing multivariate time series data in both forward and backward directions using bi-directional LSTM layers. Trained with the Mean Squared Error loss and Adam optimizer, the model demonstrates improved forecasting accuracy, offering significant potential for proactive drought management.