Rainfall Forecasting Using Historical Rainfall and Humidity Data in Wonorejo Watershed, Indonesia - Nastasia Margini; M. Bagus Ansori (Program Studi Teknik Sipil, Fakultas Teknik, Universitas Islam Lamongan, 2026)
Rainfall prediction plays a crucial role in the design, planning, and management of water resource systems. This study addresses the importance of accurate rainfall forecasting to optimize the operational efficiency of the Wonorejo Reservoir in Tulungagung Regency, Indonesia. By improving rainfall predictions, it becomes possible to estimate available water volumes more precisely and enhance reservoir utilization beyond current practices. This research employs three analytical approaches—Decomposition, Multiple Linear Regression, and Nonlinear Regression—to forecast rainfall in the Wonorejo Watershed. The models are developed using monthly total rainfall and average air humidity data collected from January 1998 to December 2018. Model performance is evaluated using the Root Mean Square Error (RMSE). The Nonlinear Regression method yields the lowest RMSE (72.71), followed by Decomposition (73.78) and Multiple Linear Regression (103.57). Based on these results, the Nonlinear Regression model is identified as the most suitable approach for forecasting rainfall in the Wonorejo Basin for the subsequent 24 months. This study recommends the application of the Nonlinear Regression method for rainfall forecasting in this specific case study area.