Hashemi Monfared, Seyed Arman, Jafari-Asl, Jafar, Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408, Thi Hoang, Thu-Huong, Nazari, Rouzbeh, Sharifi, Soroosh and Abolfathi, Soroush (2026) Reimagining coastal water quality forecasting with hydrodynamic simulation and advanced machine learning. Journal of Environmental Management. ISSN 1095-8630

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Abstract

Effective and sustainable management of coastal ecosystems relies on accurate water quality monitoring and forecasting, particularly in regions facing heavy pollutant loads. Phosphate and nitrate play crucial roles in coastal water quality and ecosystem health. However, predicting their concentrations remains challenging due to complex spatiotemporally varying environmental interactions. Leveraging advanced numerical modelling and artificial intelligence (AI), this study introduces a novel hybrid machine learning (ML) framework based on least-squares support vector regression (LSSVR) to simulate coastal water quality using physically grounded simulation data generated by the TELEMAC-WAQTEL numerical framework, calibrated against field-measured boundary and initial conditions. The proposed model is applied to Ha Long Bay, Vietnam, utilizing a dataset comprising 8760 hourly samples collected over one year (2021–2022). The data was generated through a two-dimensional TELEMAC hydrodynamic model integrated with a water quality module. Key hydrodynamic variables, including free surface elevation (FS), velocity (U), and flow direction (α), serve as inputs to predict nitrate (NO3) and phosphate (PO4) concentrations. To optimize the LSSVR hyperparameters, we introduced the Improved Dragonfly Algorithm (IDA), which integrates Cauchy walk mutation and adaptive inertia weighting to enhance convergence, global search efficiency, and robustness. Benchmarking against Gaussian Process Regression (GPR), ε- LSSVR, ANN, Random Forest, and Model Tree demonstrate the superior predictive capability of IDA-LSSVR. On an independent testing dataset comprising 20% of the total samples (1752 observations), IDA-LSSVR achieved the lowest RMSE (NO3 = 1.55E-02 mg/L, PO4 = 5.88E-04 mg/L), MAE (NO3 = 3.45E-07 mg/L, PO4 = 2.39E-04 mg/L), and MAPE (NO3 = 3.41, PO4 = 4.49E-04), coupled with the highest R2 (NO3 = 0.95, PO4 = 0.97). By coupling high-resolution physics-based hydrodynamic simulations with advanced machine learning and optimisation techniques, the proposed framework provides an accurate, computationally efficient, and reliable tool for coastal water quality forecasting. The integration of physically consistent simulation data with robust uncertainty-aware prediction offers valuable decision-support capabilities for nutrient management, eutrophication risk assessment, and environmental monitoring in dynamic coastal environments. The framework therefore presents a promising approach for supporting sustainable coastal ecosystem management and protecting vulnerable marine systems from nutrient-driven degradation.

Item Type:	Article
Identifier:	10.1016/j.jenvman.2026.130652
Keywords:	Water quality; Machine learning; LSSVR; Dragonfly algorithm; Ha long bay; Phosphate; Nitrate
Subjects:	Natural sciences > Environmental sciences Computing > Intelligent systems
Date Deposited:	29 Sep 2026
Dates:	Date Publication status 4 August 2026 Accepted 11 August 2026 Published Online
School, department or research centre:	School of Computing and Engineering
Keywords:	Water quality; Machine learning; LSSVR; Dragonfly algorithm; Ha long bay; Phosphate; Nitrate
URI:	https://repository.uwl.ac.uk/id/eprint/15449
Sustainable Development Goals:	Goal 6: Clean Water and Sanitation	Sustainable Development Goals:	Goal 11: Sustainable Cities and Communities	Sustainable Development Goals:	Goal 13: Climate Action

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