An innovative method for improving rainfall prediction in Gujarat state through a fusion model DWT, 1DCNN and LSTM

In countries like India where agriculture is a major industry, accurate prediction of rainfall is crucial. The ability to forecast rainfall helps in planning crop cultivation and has a positive impact on both local and national economies. Nevertheless, conventional statistical techniques prove inadequate for forecasting precipitation over extended periods of time owing to the erratic characteristics inherent in climate phenomena. To address this issue, various ML models such as Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Discrete Wavelet Transform, have been explored for precipitation prediction. Large-scale data analysis and overcoming intricate challenges have become achievable through the utilization of deep learning, which has proven to be a potent approach. It combines the advantages of multiple methodologies and algorithms in order to develop accurate precipitation forecasting models. In this study, researchers treated rainfall time series as signals and used the Discrete Wavelet Transform method along with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network methods to analyze and reconstruct these signals. The research employed a dataset acquired from the Indian Meteorological Department and NASA, covering the years 1901 to 2022. This extensive collection comprised diverse meteorological parameters such as relative humidity, wet bulb temperature, Surface Pressure, soil dampness levels, relative humidity, temperatures (min and max), wind speed data in addition to rainfall records. The fusion of these three methods - DWT-CNN-LSTM deep model - consistently demonstrated excellent performance in key evaluation metrics such as MAE and RMSE. This approach achieved remarkable results in accurately predicting precipitation patterns for Gujarat State over the specified time period.