Toward Sustainable Water Resource Management Using a DWT-NARX Model for Reservoir Inflow and Discharge Forecasting in the Chao Phraya River Basin, Thailand

Thannob Aribarg, Karn Yongsiriwit (), Parkpoom Chaisiriprasert, Nattapat Patchsuwan and Seree Supharatid
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Thannob Aribarg: College of Digital Innovation Technology, Rangsit University, 52/347 Muang-Ake Phaholyothin Road, Lak-Hok, Muang, Pathum Thani 12000, Thailand
Karn Yongsiriwit: College of Digital Innovation Technology, Rangsit University, 52/347 Muang-Ake Phaholyothin Road, Lak-Hok, Muang, Pathum Thani 12000, Thailand
Parkpoom Chaisiriprasert: College of Digital Innovation Technology, Rangsit University, 52/347 Muang-Ake Phaholyothin Road, Lak-Hok, Muang, Pathum Thani 12000, Thailand
Nattapat Patchsuwan: College of Digital Innovation Technology, Rangsit University, 52/347 Muang-Ake Phaholyothin Road, Lak-Hok, Muang, Pathum Thani 12000, Thailand
Seree Supharatid: Climate Change and Disaster Center, Rangsit University, 52/347 Muang-Ake Phaholyothin Road, Lak-Hok, Muang, Pathum Thani 12000, Thailand

Sustainability, 2025, vol. 17, issue 22, 1-25

Abstract: The 2011 Great Flood in Thailand exposed critical deficiencies in water management across the Chao Phraya River Basin, particularly in controlling inflows and discharges from major reservoirs such as Sirikit and Bhumibol. Inadequate rainfall monitoring at the Nakhon Sawan station further intensified the disaster’s impact. As climate change continues to amplify extreme weather events, this study aims to improve flood forecasting accuracy and promote sustainable water resource management aligned with the Sustainable Development Goals (SDGs 6, 11, and 13). Advanced climate data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were spatially refined and integrated with hydrological models to enhance regional accuracy. The Discrete Wavelet Transform (DWT) was applied for feature extraction to capture hydrological variability, while the Nonlinear Autoregressive Model with Exogenous Factors (NARX) was employed to model complex temporal relationships. A multi-model ensemble framework was developed to merge climate forecasts with real-time hydrological data. Results demonstrate significant model performance improvements, with DWT-NARX achieving 55–98% lower prediction errors (RMSE) compared to baseline methods and correlation coefficients exceeding 0.91 across all forecasting scenarios. Marked seasonal variations emerge, with higher inflows during wet periods and reduced inflows during dry seasons. Under RCP8.5 climate scenarios, wet-season inflows are projected to increase by 15.8–17.4% by 2099, while dry-season flows may decline by up to 33.5%, potentially challenging future water availability and flood control operations. These findings highlight the need for adaptive and sustainable water management strategies to enhance climate resilience and advance SDG targets on water security, disaster risk reduction, and climate adaptation.

Keywords: climate change; hydrological forecasting; reservoir inflow; river discharge; Discrete Wavelet Transform (DWT); Nonlinear Autoregressive Model with Exogenous Factor (NARX); multi-model ensemble; Sustainable Development Goals (SDGs); water security; climate resilience (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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