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Application of the novel state-of-the-art soft computing techniques for groundwater potential assessment

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Abstract

Groundwater is one of the important ecological elements which are now considered as vulnerable resources. The absence and shortage of this precious resource can be grown into an ecologically fragile condition. So, identification of this nature of groundwater resources can be useful for proper concern about this resource and its associated measures. The accurate and meaningful prediction of groundwater potential is a very important need for management of water resources in arid and semi-arid regions of the world. Though others have modeled predictions of groundwater distribution using various statistical and machine learning methods, this study tested alternative decision tree (ADTree) and the credal decision tree (CDT) as standalone models as well as in ensembles with dagging, bagging, and decorate. Eighteen groundwater potential conditioning factors (lithology, convergence index, drainage density, elevation, distance to fault, fault density, height above nearest drainage (HAND), distance to surface, train surface texture, topographical wetness index (TWI), land use/land cover, stream transport index (STI), topographical position index (TPI), multi-resolution index of valley bottom flatness (MRVBF), profile curvature (PrC), plan curvature (PC), slope angle, and rainfall) were measured and compiled for 188 spring locations and 188 non-spring locations in the Tabriz Plain of East Azerbaijan Province, Iran. The conditioning factors were tested for multi-collinearity and there was none. The conditioning factors were examined and weighted for their importance for predicting groundwater potential and the data were modeled. The models were trained using 70% of the database and tested with the data for the remaining 30% of the locations. The results indicate that the six ensembles for both decision tree models surpassed the standalone decision trees in terms of success and accuracy. Running the training dataset, the models’ success rates were 72% for ADTree, 90% for ADTree-Bagging, 85.6% for ADTree-Dagging, and 89.7% for ADTree-Decorate and were 73.2% for CDT, 83.9% for CDT-Bagging, 83.5% for CDT-Dagging, and 81.7% for CDT-Decorate. The accuracies were 79.1%, 92.9%, 88.7%, and 85.5%, and were 68%, 85.8%, 84%, and 81.2%, respectively. Using the testing dataset, the success rates were 85% for ADTree, 96.3% for ADTree-Bagging, 94.0% for ADTree-Dagging, and 91.6% for ADTree-Decorate and 61.2% for CDT, 93.3% for CDT-Bagging, 92.2% for CDT-Dagging, and 91.8% for CDT-Decorate The decision tree-bagging ensembles were in all cases. These ensembles could be very effective, efficient approaches to develo** meaningful sources of information for sustainable groundwater use and management.

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Authors and Affiliations

  1. Department of Geomorphology, Tarbiat Modares University, 14117-13116, Tehran, Iran

    Alireza Arabameri

  2. School of Earth Sciences and Resources, China University of Geosciences Bei**g, Bei**g, China

    M. Santosh

  3. Department of Earth Sciences, University of Adelaide, Adelaide, South Australia, Australia

    M. Santosh

  4. Informetrics Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  5. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  6. Department of Geography, Texas State University, San Marcos, TX, 78666, USA

    John P. Tiefenbacher

  7. Department of Geography, The University of Burdwan, Burdwan, West Bengal, India

    Subodh Chandra Pal & Rabin Chakrabortty

  8. Department of Watershed Management, Gorgan University of Agricultural Sciences and Natural Resources (GUASNR), Gorgan, Iran

    Omid Asadi Nalivan

  9. Research Institute of the University of Bucharest, 90-92 Sos. Panduri, 5th District, 050663, Bucharest, Romania

    Romulus Costache

  10. National Institute of Hydrology and Water Management, București-Ploiești Road, 97E, 1st District, 013686, Bucharest, Romania

    Romulus Costache

  11. Department of Geography and Environment, Jagannath University, Dhaka, 1100, Bangladesh

    Naser Ahmed & Muhammad Al-Amin Hoque

  12. Centre for Advanced Modelling and Geospatial Information Systems, Faculty of Engineering and IT, The University of Technology Sydney, Ultimo, NSW, 2007, Australia

    Muhammad Al-Amin Hoque

  13. Soil Erosion and Degradation Research Group, Departament de Geografia, Universitat de València, Blasco Ibàñez, 28, 46010, Valencia, Spain

    Artemi Cerda

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  1. Alireza Arabameri
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  2. M. Santosh
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  3. Hossein Moayedi
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  4. John P. Tiefenbacher
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  5. Subodh Chandra Pal
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  6. Omid Asadi Nalivan
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  7. Romulus Costache
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  8. Naser Ahmed
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  10. Rabin Chakrabortty
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  11. Artemi Cerda
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Correspondence to Alireza Arabameri.

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Arabameri, A., Santosh, M., Moayedi, H. et al. Application of the novel state-of-the-art soft computing techniques for groundwater potential assessment. Arab J Geosci 15, 929 (2022). https://doi.org/10.1007/s12517-021-09005-y

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