Abstract
Time series models based on an artificial neural network (ANN) and support vector machine (SVM) were designed to predict the temporal variation of the upper and lower freshwater-saltwater interface level (FSL) at a groundwater observatory on Jeju Island, South Korea. Input variables included past measurement data of tide level (T), rainfall (R), groundwater level (G) and interface level (F). The T-R-G-F type ANN and SVM models were selected as the best performance model for the direct prediction of the upper and lower FSL, respectively. The recursive prediction ability of the T-R-G type SVM model was best for both upper and lower FSL. The average values of the performance criteria and the analysis of error ratio of recursive prediction to direct prediction (RP-DP ratio) show that the SVM-based time series model of the FSL prediction is more accurate and stable than the ANN at the study site.
Highlights
Monitoring and forecasting of temporal changes of the freshwater-saltwater interface level (FSL)in coastal areas is necessary for the early detection of saltwater intrusion and the management of coastal aquifers
3D-SUTRA model to a coastal aquifer in Hawaii for the simulation of the saltwater intrusion; Werner and Gallagher [5] characterized seawater intrusion in coastal aquifers of the Pioneer Valley, Australia using MODHMS model; Guo and Langevin [6] developed SEAWAT, a variable-density finite-difference groundwater flow mode and Rozell and Wong [7] applied it to Shelter Island, USA for assessing effects of climate change on the groundwater resources; Yechieli et al [8] examined the response of the Mediterranean and Dead Sea coastal aquifers using FEFLOW model
Using the observed FSL data, we designed time series models models based on artificial neural networks and support vector machines for the prediction of FSL
Summary
Monitoring and forecasting of temporal changes of the freshwater-saltwater interface level (FSL). In the field of hydrology and hydrogeology, research on the application of time series models—based on machine learning techniques such as an artificial neural network (ANN) and a models—based on machine learning techniques such as an artificial neural network (ANN) and support vector machine (SVM) to prediction of water resources variations—have been increased;. ForFor coastal aquifer management, time series have been developed to predict groundwater groundwater level fluctuations using machine learning methods [22,23,24]. Using the observed FSL data, we designed time series models models based on artificial neural networks and support vector machines for the prediction of FSL based on artificial neural networks and support vector machines for the prediction of FSL fluctuations.
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