A multi-objective simulation-optimisation model for managing saltwater intrusion in a coastal aquifer of the Pacific Island of Vanuatu

• Saltwater intrusion in Vanautu is affecting groundwater. • Ensemble models predict better than standalone surrogate models. • Simulation-optimisation management model manages saltwater intrusion. Groundwater resources in Pacific Island countries face threats from climate change, natural disasters, and human activities, with saltwater intrusion (SWI) being a significant issue in coastal aquifers like Vanuatu’s Tagabe coastal aquifer. This study developed numerical and machine learning models to manage SWI in the Tagabe aquifer. The SEAWAT was used to create a numerical groundwater model, calibrated and validated with salt concentration data from various monitoring locations (MLs). The model generated input (pumping values) and output (salt concentrations) datasets, which were used to train and test twelve machine learning models or surrogate models. These models were ranked using performance criteria and decision theory, and the top five were used to form ensemble models to predict salt concentrations at four MLs. The Pearson coefficient, Index of Agreement, Kling-Gupta Efficiency and Nash Sutcliffe Efficiency coefficient of four ensemble models at ML1 = 0.745, 0.738, 0.497 and 0.384; ML2 = 0.946, 0.950, 0.753 and 0.848; ML3 = -0.028, 0.166, −0.369 and −0.070; ML4 = 0.415, 0.369, −0.135 and −4.032, respectively. The ensemble models performed better than individual surrogate models, with higher predictive accuracy across different MLs. These models were integrated with the Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) to create a simulation-optimisation (S-O) management model, providing Pareto optimal solutions for managing SWI. Verification using the SEAWAT-based numerical model showed that the ensemble models’ predictive accuracy was within 7% of the complex numerical model. This study is novel in its use of limited datasets to train twelve machine learning models and the application of ensemble surrogate and S-O management models in Vanuatu, demonstrating their global applicability for managing SWI in coastal aquifers and aiding policymakers in formulating effective groundwater use policies.

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Publication Details

Journal
Journal of Hydrology
Published
2026-02-15
DOI
https://doi.org/10.1016/j.jhydrol.2026.135128
Primary Topic
Groundwater and Isotope Geochemistry
Type
article
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article

A multi-objective simulation-optimisation model for managing saltwater intrusion in a coastal aquifer of the Pacific Island of Vanuatu

Ashneel Sharan, Alvin Lal, Dilip Kumar Roy, Bithin Datta
Journal of Hydrology
Groundwater and Isotope Geochemistry
article

A multi-objective simulation-optimisation model for managing saltwater intrusion in a coastal aquifer of the Pacific Island of Vanuatu

Ashneel Sharan, Alvin Lal, Dilip Kumar Roy, Bithin Datta
article en

Abstract

• Saltwater intrusion in Vanautu is affecting groundwater. • Ensemble models predict better than standalone surrogate models. • Simulation-optimisation management model manages saltwater intrusion. Groundwater resources in Pacific Island countries face threats from climate change, natural disasters, and human activities, with saltwater intrusion (SWI) being a significant issue in coastal aquifers like Vanuatu’s Tagabe coastal aquifer. This study developed numerical and machine learning models to manage SWI in the Tagabe aquifer. The SEAWAT was used to create a numerical groundwater model, calibrated and validated with salt concentration data from various monitoring locations (MLs). The model generated input (pumping values) and output (salt concentrations) datasets, which were used to train and test twelve machine learning models or surrogate models. These models were ranked using performance criteria and decision theory, and the top five were used to form ensemble models to predict salt concentrations at four MLs. The Pearson coefficient, Index of Agreement, Kling-Gupta Efficiency and Nash Sutcliffe Efficiency coefficient of four ensemble models at ML1 = 0.745, 0.738, 0.497 and 0.384; ML2 = 0.946, 0.950, 0.753 and 0.848; ML3 = -0.028, 0.166, −0.369 and −0.070; ML4 = 0.415, 0.369, −0.135 and −4.032, respectively. The ensemble models performed better than individual surrogate models, with higher predictive accuracy across different MLs. These models were integrated with the Controlled Elitist Multiple Objective Genetic Algorithm (CEMOGA) to create a simulation-optimisation (S-O) management model, providing Pareto optimal solutions for managing SWI. Verification using the SEAWAT-based numerical model showed that the ensemble models’ predictive accuracy was within 7% of the complex numerical model. This study is novel in its use of limited datasets to train twelve machine learning models and the application of ensemble surrogate and S-O management models in Vanuatu, demonstrating their global applicability for managing SWI in coastal aquifers and aiding policymakers in formulating effective groundwater use policies.

Journal of HydrologyVol. 669
Hydro Tasmania (AU), University of Newcastle Australia (AU), Bangladesh Agricultural Research Institute (BD), James Cook University (AU)
Climate action
Openalex Percentile: Top 58%
Groundwater and Isotope Geochemistry
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