Deep learning model for daily rainfall prediction: case study of Jimma, Ethiopia
Demeke Endalie, Getamesay Haile, Wondmagegn Taye
Abstract
Abstract Rainfall prediction is a critical task because many people rely on it, particularly in the agricultural sector. Rainfall forecasting is difficult due to the ever-changing nature of weather conditions. In this study, we carry out a rainfall predictive model for Jimma, a region located in southwestern Oromia, Ethiopia. We propose a Long Short-Term Memory (LSTM)-based prediction model capable of forecasting Jimma's daily rainfall. Experiments were conducted to evaluate the proposed models using various metrics such as Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Nash–Sutcliffe model efficiency (NSE), and R2, and the results were 0.01, 0.4786, 0.81 and 0.9972, respectively. We also compared the proposed model with existing machine-learning regressions like Multilayer Perceptron (MLP), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT). The RMSE of MLP was the lowest of the four existing learning models i.e., 0.03. The proposed LSTM model outperforms the existing models, with an RMSE of 0.01. The experimental results show that the proposed model has a lower RMSE and a higher R2.