Diffusion-Driven Time-Series Forecasting to Support Sustainable River Ecosystems and SDG-Aligned Water-Resource Governance in Thailand

Abstract

Time-series water-quality forecasting plays a crucial role in sustainable environmental monitoring, early-warning surveillance, and data-driven water-resource governance. Degradation of river ecosystems poses significant risks to public health, biodiversity, and long-term socio-economic resilience, particularly in rapidly developing regions. In this study, a multi-scale diffusion forecaster (MDF) is introduced to enhance predictive accuracy and uncertainty quantification for river water-quality dynamics in Thailand. The proposed framework integrates seasonal-trend decomposition with a hierarchical denoising diffusion process to model stochastic environmental fluctuations across multiple temporal resolutions. Experiments conducted using real water-quality datasets from the Mae Klong, Khwae Noi, and Khwae Yai Rivers, and the Port Authority of Thailand, demonstrate that MDF achieves superior probabilistic calibration under noise and data incompleteness compared to conventional deterministic baselines. The forecasting capability supports proactive pollution control, sustainable resource allocation, and climate-resilient water-policy design, directly contributing to Sustainable Development Goals (SDG 6: Clean Water and Sanitation; SDG 13: Climate Action; and SDG 14: Life Below Water). The findings highlight the potential of diffusion-based learning as an enabling technology for sustainable aquatic ecosystem governance and long-term environmental planning.