Citation

Ibrahim, Noraini and Mat Jan, Nur Amalina and Ahmad, Norhaiza and Zainudin, Zanariah and Jamil, Nurul Syafidah and Badyalina, Basri and Mohd Juffry, Ahmad Zaffry Hadi (2025) Evaluating Statistical and Machine Learning Models for River Flow Forecasting in Terengganu: A Case Study Using Facebook Prophet, XGBoost, and Random Forest. In: 6th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2025, 2 September 2025 - 3 September 2025, West Java, Indonesia.

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Abstract

Accurate forecasting of river flow is essential for effective flood management and sustainable water resource planning, particularly in regions influenced by seasonal monsoons like Terengganu, Malaysia. This study evaluates and compares the predictive performance of three forecasting models including Extreme Gradient Boosting (XGBoost), Facebook Prophet, and Random Forest using monthly river flow data from the Dungun and Kemaman Rivers. The results reveal that the Random Forest model consistently outperforms the other two, achieving the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) at both river stations. Specifically, Random Forest achieved up to 41.5% lower RMSE and 17.1% lower MAE compared to XGBoost for Kemaman River. For Dungun River, Random Forest model outperforms the XGBoost model by 16.45% for RMSE and 16.17% for MAE. The results indicate that the ensemble-based nature of Random Forest provide greater robustness and accuracy in capturing peak-flow events and nonlinear dynamics of river flow series. These findings emphasize the effectiveness of machine learning models, especially Random Forest, in capturing the complex patterns of hydrological time series and highlight their potential integration into regional flood forecasting and water management systems

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Facebook prophet, random Forest
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	22 Dec 2025 04:16
Last Modified:	26 Dec 2025 04:08
URII:	http://shdl.mmu.edu.my/id/eprint/15099

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