Predicting Saltwater Intrusion into Coastal Aquifers Using Support Vector Regression Surrogate Models

Document Type : Research Paper

Authors

1 PhD Student, Dept. of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Prof., Dept. of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

3 Assist. Prof., Dept. of Civil Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

The prediction of the intrusion of saline water into coastal aquifers as a result of changing the amount of groundwater extractions is a prerequisite for managing groundwater. This study investigates the capability of different types of Support Vector Regression (SVR) models to predict salinity concentrations at the selected well in the small coastal aquifer under different groundwater abstraction conditions. SVR models were trained and tested using input (random transient pumping from the production wells) derived from Latin Hypercube Sampling and output (salinity concentration at the selected well) datasets. The trained and tested models were then used to predict salinity concentrations at the selected well for new pumping datasets. The models ability for predicting and generalizing compared with commonly used artificial neural network (ANN) model was evaluated using different performance criteria. The results of the performance evaluation of the models showed that the predictive capability of the polynomial SVR model is superior to other models. Also, comparing different performance criteria for all SVR models, except for linear SVR model, proved their acceptable predictive performance. The prediction and generalisation ability of polynomial SVR, recommends using these models to connect to the optimization algorithm for a surrogate model based simulation-optimization approach in sustainable management of coastal aquifers.

Keywords

References

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Journal of Water and Wastewater; Ab va Fazilab ( in persian )
Volume 31, Issue 2 - Serial Number 126
May and June 2020
Pages 118-129

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