AUTOMATED MULTI-MODEL PREDICTION AND EVALUATION FOR CONNECTING RAINFALL PREDICTION INFORMATION AND SINGLE-YEAR OPERATIONAL PLAN OF LAHOR-SUTAMI DAM - Mahmudiah, Rikha Rizki; Crysdian, Cahyo; Hariyadi, Mokhamad Amin; Kurniawan, Andang (Universitas Lambung Mangkurat, 2024)
There is a gap between existing climate information and the needs of annual dam operational planning. This study aims to demonstrate that the percentile approach currently used for planning is not optimal, especially now that automation has become more accessible. The purpose of this study is to design an automated forecasting and evaluation system based on 36 10-days rainfall projections using a multi-model approach. This approach comprises a percentile, ARIMA, ECMWF+ARIMA, IOD DMI regression, ERSST regression, and ensemble methods models. Additionally, this study aims to demonstrate how a verified, multi-model-based rainfall forecast can provide more reliable assurance for the annual operational planning of Lahor-SutamiDam, simulated operationally in November 2022 for the 2022/2023 planning cycle. Data utilized include historical 10-days rainfall data from 1991 to 2023, ECMWF raw and corrected model outputs, Nino-Dipole index, and global sea surface temperature. The verification method employs four criteria based on MAE and fit index. An operational simulation approach is used for training-testing period segmentation, while a 10-year window is applied to account for possible climate-change-induced shifts in relationships. Single linear regression is used to avoid overfitting. The automation system was developed using R-Statistics. Results indicate that the current approach is only optimal for 58% of locations. Superior methods identified include ECMWFcorrected, ERSST regression, and Ensemble models. A case study for 2022/2023 demonstrates that the forecast results outperform the existing plan for at least 78% of the projected periods.