Trend Analysis and Prediction of Rainfall Using Deep Learning Models in Three Sub-Divisions of Karnataka

Abstract

Precise estimation of rainfall is a crucial and challenging task in environmental science. It involves the use of advanced and powerful models to forecast non-linear and dynamic changes in rainfall. Deep learning, a recently developed method for handling vast amounts of data and resolving complex problems, has proven to be an effective tool for rainfall forecasting. In this study, we applied various deep learning models such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Stacked LSTM, Gated Recurrent Units (GRUs), and a traditional model called Autoregressive Integrated Moving Average (ARIMA), to forecast monthly rainfall data (mm) for three regions of Karnataka: Coastal Karnataka, North Interior Karnataka (NIK), and South Interior Karnataka (SIK). Trend analysis was conducted using the Mann-Kendall trend test (MK test) and the Seasonal Mann-Kendall trend test, along with Sen's Slope Estimator, to determine trends and slope magnitudes. The results showed that deep learning models perform better than traditional methods in forecasting rainfall. The performance of different models was evaluated using forecasting evaluation criteria and found that the LSTM model performed best for Coastal Karnataka, with an RMSE value of 149.45, while the Bi-LSTM model performed best for NIK, with an RMSE value of 32.57, and the Stacked LSTM model performed best for SIK, with an RMSE value of 45.33. Therefore, deep learning models can be effectively used to predict rainfall data with greater accuracy.