UTILIZING DEEP LEARNING MODELS IN KABIRDHAM, CHHATTISGARH, TO FORECAST AND MODEL RAINFALL - Kaushik, Jaleshwar; Chandrakar, Omprakash; Shameem, Bakhtawer (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Deep learning has emerged as a key area for modeling and forecasting complex time series data. The future performance of Kabirdham rainfall data was investigated in this machine learning project. To construct and validate the model, the dataset is divided into 35% test sets and 65% training sets. We utilized the Root Mean Square Error (RMSE) measure to compare these deep learning models. In this data set, the Modified BPN ANN model performs better than the BILSTM and GRU models. The predictions of these three models are comparable. The development of a comprehensive Kabirdham weather forecast book might benefit from this knowledge. Scholars and policymakers would both benefit from this information. Beyond statistical methods, we think this study can be utilized to apply machine learning to complicated time series data.