Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches

Mojgan Bordbar, Aminreza Neshat (), Saman Javadi, Biswajeet Pradhan, Barnali Dixon and Sina Paryani
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Mojgan Bordbar: Islamic Azad University
Aminreza Neshat: Islamic Azad University
Saman Javadi: University of Tehran
Biswajeet Pradhan: University of Technology Sydney
Barnali Dixon: University of South Florida
Sina Paryani: Islamic Azad University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 110, issue 3, No 18, 1799-1820

Abstract: Abstract The main objective of this study is to integrate adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and artificial neural network (ANN) to design an integrated supervised committee machine artificial intelligence (SCMAI) model to spatially predict the groundwater vulnerability to seawater intrusion in Gharesoo-Gorgan Rood coastal aquifer placed in the northern part of Iran. Six hydrological GALDIT parameters (i.e., G groundwater occurrence, A aquifer hydraulic conductivity, L level of groundwater above sea level, D distance from the shore, I impact of the existing status of seawater intrusion in the region, and T thickness of the aquifer) were considered as inputs for each model. In the training step, the values of GALDIT’s vulnerability index were conditioned by using the values of TDS concentration in order to obtain the conditioned vulnerability index (CVI). The CVI was considered as the target for each model. After training the models, each model was tested using a separate TDS dataset. The results indicated that the ANN and ANFIS algorithms performed better than the SVM algorithm. The values of correlation were obtained as 88, 87, and 80% for ANN, ANFIS, and SVM models, respectively. In the testing step of the SCMAI model, the values of RMSE, R2, and r were obtained as 6.4, 0.95, and 97%, respectively. Overall, SCMAI model outperformed other models to spatially predicting vulnerable zones. The result of the SCMAI model confirmed that the western zones along the shoreline had the highest vulnerability to seawater intrusion; therefore, it seems critical to consider emergency protection plans for study area. Graphic abstract

Keywords: Coastal aquifer’s vulnerability; Machine learning; SCMAI; GIS; GALDIT (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05013-z

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