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

Bordbar, M; Neshat, A; Javadi, S; Pradhan, B; Dixon, B; Paryani, S

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

Bordbar, M Neshat, A Javadi, S Pradhan, B

Dixon, B Paryani, S

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Publisher:: SPRINGER
Publication Type:: Journal Article
Citation:: Natural Hazards, 2022, 110, (3), pp. 1799-1820
Issue Date:: 2022-02-01

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Field	Value	Language
dc.contributor.author	Bordbar, M
dc.contributor.author	Neshat, A
dc.contributor.author	Javadi, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Dixon, B
dc.contributor.author	Paryani, S
dc.date.accessioned	2022-03-16T22:30:57Z
dc.date.available	2022-03-16T22:30:57Z
dc.date.issued	2022-02-01
dc.identifier.citation	Natural Hazards, 2022, 110, (3), pp. 1799-1820
dc.identifier.issn	0921-030X
dc.identifier.issn	1573-0840
dc.identifier.uri	http://hdl.handle.net/10453/155287
dc.description.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: [Figure not available: see fulltext.].
dc.language	English
dc.publisher	SPRINGER
dc.relation.ispartof	Natural Hazards
dc.relation.isbasedon	10.1007/s11069-021-05013-z
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0401 Atmospheric Sciences, 0406 Physical Geography and Environmental Geoscience, 1701 Psychology
dc.subject.classification	Strategic, Defence & Security Studies
dc.title	Improving the coastal aquifers’ vulnerability assessment using SCMAI ensemble of three machine learning approaches
dc.type	Journal Article
utslib.citation.volume	110
utslib.for	0401 Atmospheric Sciences
utslib.for	0406 Physical Geography and Environmental Geoscience
utslib.for	1701 Psychology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access	*
dc.date.updated	2022-03-16T22:30:56Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	110
utslib.citation.issue	3

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: [Figure not available: see fulltext.].

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http://hdl.handle.net/10453/155287