Rainfall Prediction using Hybrid CNN-LSTM approach: A case study in the Boudh district, Odisha, India

The forecast of monthly rainfall is a significant topic for water resource management and hydrological disaster prevention. A critical need for precise hydrological forecasts in water resource management is addressed in this study by analyzing machine learning (ML) models for precipitation forecasting in the Boudh district of Odisha, India. Although machine learning (ML) models have demonstrated significant promise in rainfall forecasting due to their high performance, often surpassing that of certain physical models, the intricate physical processes involved in rainfall creation mean that a single ML model is typically insufficient to provide reliable rainfall projections. A thorough set of meteorological parameters, including precipitation wind speed, temperature, and humidity, are utilized to create four distinct models: Support Vector Regression (SVR), long and short memory neural networks (LSTM), Bi-LSTM and Convolutional neural network with LSTM (CNN-LSTM). The performance of these models is thoroughly assessed utilizing a range of evaluation metrics. In this work, the correlations between precipitation and climate factors are assessed using the cross-correlation function (XCF). With maxima consistently reported during months across all four sites, the XCF analysis shows a number of significant trends, including a strong correlation amid precipitation and maximum temperature. Moreover, precipitation is significantly correlated with wind speed and relative humidity. The results demonstrate the effectiveness of hybridized ML techniques in raising the precision of precipitation forecasts. The CNN-LSTM models, which have R² values between 0.93 and 0.97, generally perform better. Their remarkable accuracy highlights their efficacy in precipitation forecasting, outperforming rival models during both training and testing. These findings have important ramifications for hydrological processes, particularly in Odisha's Boudh region, where sustainable water resources management depends on precise precipitation forecasting.