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

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Article Type:
Research/Original Article (دارای رتبه معتبر)
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.

Language:
Persian
Published:
Journal of Water & Wastewater, Volume:31 Issue: 126, 2020
Pages:
118 to 129
https://www.magiran.com/p2101653  
سامانه نویسندگان
  • Faal، Fatemeh
    Corresponding Author (1)
    Faal, Fatemeh
    Phd Student Dept. of Civil Engineering, Shahid Chamram University, اهواز, Iran
  • Ghafouri، Hamid Reza
    Author (2)
    Ghafouri, Hamid Reza
    Professor Computational Hydraulics , Civil Engg Dept, Faculty of Civil Engineering and Architecture, Shahid Chamram University, اهواز, Iran
  • Ashrafi، Seyed Mohammad
    Author (3)
    Ashrafi, Seyed Mohammad
    Associate Professor Department of Civil Engineering, Shahid Chamran University of Ahvaz, Shahid Chamram University, اهواز, Iran
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