Rainfall Prediction using Long Short-Term Memory and Gated Recurrent Unit with Various Meteorological Parameters - Pujara, Mahesh; Paudel, Nawaraj (Nepalese Journal of Statistics, 2024)
Background: Rainfall prediction is a critical task in meteorology and environmental science, with far-reaching implications for disaster preparedness, agriculture, and water resource management. Rainfall prediction can benefit greatly from the application of deep learning techniques like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, which have demonstrated great promise in time series forecasting. Objective: To use LSTM and GRU to forecast rainfall in the Kathmandu metropolitan area using information gathered from the Department of Hydrology and Meteorology, Babarmahal, Kathmandu, Nepal. Materials and Methods: Historical meteorological data was collected from Department of Hydrology and Meteorology, Babarmahal, Kathmandu, Nepal and preprocessed to create a suitable dataset. With this preprocessed dataset containing variables such as temperature, humidity, atmospheric pressure, wind speed, and direction, two deep learning methods, LSTM and GRU, were trained. To assess the performance, various evaluation metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R2 were used. Results: The daily rainfall has been predicted using LSTM and GRU using 0.0001 learning rate, 50 epochs and 8 batch size. RMSE, MAE and R2 values of LSTM are 2.51, 1.79 and 0.81 respectively. Similarly, RMSE, MAE and R2 values of GRU are 2.31, 1.51 and 0.95 respectively. Conclusion: Test results show that the GRU model's predictions are generally near to the actual recorded rainfall amounts, as evidenced by the fact that the model's test RMSE and MAE are fewer than those of the LSTM. A higher R2 value of GRU suggests a better fit in the rainfall data, as more of the variance in the outcome is explained by the predictors.