Navigating the Challenges of Rainfall Variability: Precipitation Forecasting using Coalesce Model

This study introduces a coalesce forecasting model tailored for flood-prone regions, specifically focusing on Bihar, India. Research has revealed significant disparities in rainfall patterns across various zones such as Tirhut, Patna, and Munger zones experiencing greater mean rainfall than Bhagalpur and Kosi. To evaluate the forecasting capabilities, coalescing methods were applied which includes the autoregressive integrated moving average (ARIMA), exponential smoothing state space (ETS), neural network autoregressive (NNAR), and seasonal-trend decomposition. Moreover, Loess (STL) methods, and trigonometric seasonality, Box‒Cox transformation, ARMA errors, and trend and seasonal components (TBATS) were also employed to contrast the benchmark models such as the seasonal naïve, naïve, and mean methods. These methods were evaluated using error evaluators such as residual error, root mean square error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE), and autocorrelation of errors at lag 1 (ACF1) to determine the performance of these techniques. Additionally, statistical tests, such as the Box–Pierce and Box–Ljung tests, supported these findings. Among the error evaluators and forecasting models, the ETS and NNAR models remain the top choices for Saran-Tirhut-Bhagalpur and Munger-Magadh-Kosi, respectively, effectively capturing rainfall patterns and minimizing residual errors, as indicated by low RMSE values. Moreover, ARIMA and TBATS remain the top choices for Patna, Purnia and Darbhanga, respectively, followed by ETS model. In addition, the STL model secured the second position for Saran, Tirhut, Bhagalpur, and Purnia zones. This research emphasizes the importance of understanding regional rainfall dynamics for effective flood risk management and climate adaptation strategies. This study provides valuable tools for water resource management and agricultural planning in Bihar amidst climate variability challenges. It advocates for rainfall trend analysis followed by forecasting to achieve more precise water resource management and planning.