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

Author

Listed:
  • Mojgan Bordbar

    (Islamic Azad University)

  • Aminreza Neshat

    (Islamic Azad University)

  • Saman Javadi

    (University of Tehran)

  • Biswajeet Pradhan

    (University of Technology Sydney
    Universiti Kebangsaan Malaysia)

  • Barnali Dixon

    (University of South Florida)

  • Sina Paryani

    (Islamic Azad University)

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

Suggested Citation

  • Mojgan Bordbar & Aminreza Neshat & Saman Javadi & Biswajeet Pradhan & Barnali Dixon & Sina Paryani, 2022. "Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(3), pages 1799-1820, February.
    • Handle: RePEc:spr:nathaz:v:110:y:2022:i:3:d:10.1007_s11069-021-05013-z
    DOI: 10.1007/s11069-021-05013-z
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    References listed on IDEAS

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    1. Haoyuan Hong & Himan Shahabi & Ataollah Shirzadi & Wei Chen & Kamran Chapi & Baharin Bin Ahmad & Majid Shadman Roodposhti & Arastoo Yari Hesar & Yingying Tian & Dieu Tien Bui, 2019. "Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 96(1), pages 173-212, March.
    2. Aminreza Neshat & Biswajeet Pradhan, 2015. "An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 76(1), pages 543-563, March.
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