Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model

Tran Xuan Bien, Pham The Trinh, Luu Thuy Duong, Tran Van Phong, Vuong Hong Nhat, Hiep Van Le, Dam Duc Nguyen, Indra Prakash, Pham Thanh Tam, Binh Thai Pham
Author affiliations


  • Tran Xuan Bien Hanoi University of Natural Resources and Environment, Hanoi, Vietnam
  • Pham The Trinh 1-Tay Nguyen University, 567 Le Duan, Buon Ma Thuot, DakLak Province, Vietnam; 2-Department of Science and Technology, DakLak Province, Vietnam
  • Luu Thuy Duong Hanoi University of Natural Resources and Environment, Hanoi, Vietnam
  • Tran Van Phong 1-Institute of Geological Sciences, Vietnam Academy of Science and Technology, Hanoi, Vietnam; 2-Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Vuong Hong Nhat Institute of Geography, Vietnam Academy of Science and Technology, Hanoi, Vietnam
  • Hiep Van Le University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam
  • Dam Duc Nguyen University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam
  • Indra Prakash DDG(R) Geological Survey of India, Gandhinagar, Gujarat 382010, India
  • Pham Thanh Tam Thai Nguyen University of Agriculture and Forestry, Thai Nguyen, Vietnam
  • Binh Thai Pham University of Transport Technology, 54 Trieu Khuc, Thanh Xuan, Ha Noi, Vietnam



: Machine learning, groundwater potential mapping, Logistics Model Trees, cascade generalization, Vietnam


In this work, the main aim is to map the potential zones of groundwater in Central Highlands (Vietnam) using a novel ensemble machine learning model, namely CG-LMT, which is a combination of two advanced techniques, namely Cascade Generalization (CG) and Logistics Model Trees (LMT). For this, a total of 501 wells data and a set of twelve affecting factors were gathered and selected to generate training and testing datasets used for building and validating the model. Validation of the models was implemented utilizing various quantitative indices, including ROC curve. Results of the present study indicated that the novel ensemble model performed well for groundwater potential mapping and modeling (AUC = 0.742), and its predictive capability is even better than a single LMT model (AUC = 0.727). Thus, the CG-LMT is a promising tool for accurately predicting potential groundwater areas. In addition, the potential map of groundwater generated from the CG-LMT model is a helpful tool for better-studying water resource management in the area.


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Anh D.T., Pandey M., Mishra V.N., Singh K.K., Ahmadi K., Janizadeh S., Tran T.T., Linh N.T.T., Dang N.M., 2023. Assessment of groundwater potential modeling using support vector machine optimization based on Bayesian multi-objective hyperparameter algorithm. Applied Soft Computing, 132, 109848.

Arabameri A., Pal S.C., Rezaie F., Nalivan O.A., Chowdhuri I., Saha A., Lee S., Moayedi H., 2021. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques. Journal of Hydrology: Regional Studies, 36, 100848.

Bien T.X., Jaafari A., Van Phong T., Trinh P.T., Pham B.T., 2023. Groundwater potential mapping in the Central Highlands of Vietnam using spatially explicit machine learning. Earth Science Informatics, 16, 131–146.

Chatterjee S., Dutta S., 2022. Assessment of groundwater potential zone for sustainable water resource management in south-western part of Birbhum District, West Bengal. Applied Water Science, 12, 40.

Chen Y., Chen W., Chandra Pal S., Saha A., Chowdhuri I., Adeli B., Janizadeh S., Dineva A.A., Wang X., Mosavi A., 2022. Evaluation efficiency of hybrid deep learning algorithms with neural network decision tree and boosting methods for predicting groundwater potential. Geocarto International, 37, 5564–5584.

Costache R., Ali S.A., Parvin F., Pham Q.B., Arabameri A., Nguyen H., Crăciun A., Anh D.T., 2022. Detection of areas prone to flood-induced landslides risk using certainty factor and its hybridization with FAHP, XGBoost and deep learning neural network. Geocarto International, 37, 7303–7338.

Dey B., Abir K.A.M., Ahmed R., Salam M.A., Redowan M., Miah M.D., Iqbal M.A., 2023. Monitoring groundwater potential dynamics of north-eastern Bengal Basin in Bangladesh using AHP-Machine learning approaches. Ecological Indicators, 154, 110886.

Doan V.L., Nguyen C.C., Nguyen C.T., 2024. Effect of time-variant rainfall on landslide susceptibility: A case study in Quang Ngai Province, Vietnam. Vietnam Journal of Earth Sciences.

Gama J., Brazdil P., 2000. Cascade generalization. Machine Learning, 41, 315–343.

Goswami T., Ghosal S., 2022. Understanding the suitability of two MCDM techniques in mapping the groundwater potential zones of semi-arid Bankura District in eastern India. Groundwater for Sustainable Development, 17, 100727.

Hai H.D., Ngo H.T.T., Van P.T., Duc D.N., Avand M., Huu D.N., Amiri M., Van Le H., Prakash I., Thai P.B., 2022. Development and application of hybrid artificial intelligence models for groundwater potential mapping and assessment. Vietnam J. Earth Sci., 44(3), 410–429.

Kotsiantis S.B., 2011. Cascade generalization with reweighting data for handling imbalanced problems. The Computer Journal, 54, 1547–1559.

Kumar R., Dwivedi S.B., Gaur S., 2021. A comparative study of machine learning and Fuzzy-AHP technique to groundwater potential mapping in the data-scarce region. Computers & Geosciences, 155, 104855.

Landwehr N., Hall M., Frank E., 2005. Logistic model trees. Machine Learning, 59, 161-205.

Lee S., Hong S.-M., Jung H.-S., 2018. GIS-based groundwater potential mapping using artificial neural network and support vector machine models: the case of Boryeong city in Korea. Geocarto International, 33, 847–861.

Lee S., Hyun Y., Lee S., Lee M.-J., 2020. Groundwater potential mapping using remote sensing and GIS-based machine learning techniques. Remote Sensing, 12, 1200.

Lee S., Song K.-Y., Kim Y., Park I., 2012. Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model. Hydrogeology Journal, 20, 1511.

Li P., Karunanidhi D., Subramani T., Srinivasamoorthy K., 2021. Sources and consequences of groundwater contamination. Archives of Environmental Contamination and Toxicology, 80, 1–10.

Miraki S., Zanganeh S.H., Chapi K., Singh V.P., Shirzadi A., Shahabi H., Pham B.T., 2019. Mapping groundwater potential using a novel hybrid intelligence approach. Water Resources Management, 33, 281–302.

Morgan H., Madani A., Hussien H.M., Nassar T., 2023. Using an ensemble machine learning model to delineate groundwater potential zones in desert fringes of East Esna-Idfu area, Nile valley, Upper Egypt. Geoscience Letters, 10, 9.

Mosavi A., Sajedi Hosseini F., Choubin B., Goodarzi M., Dineva A.A., Rafiei Sardooi E., 2021. Ensemble boosting and bagging based machine learning models for groundwater potential prediction. Water Resources Management, 35, 23–37.

Naghibi S.A., Ahmadi K., Daneshi A., 2017. Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping. Water Resources Management, 31, 2761–2775.

Naghibi S.A., Pourghasemi H.R., Abbaspour K., 2018. A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theoretical and Applied Climatology, 131, 967-984.

Nguyen H.D., Nguyen V.H., Du Q.V.V., Nguyen C.T., Dang D.K., Truong Q.H., Dang N.B.T., Tran Q.T., Nguyen Q.-H., Bui Q.-T., 2024. Application of hybrid model-based machine learning for groundwater potential prediction in the north central of Vietnam. Earth Science Informatics, 1–21.

Nhu V.-H., Bui T.T., My L.N., Vuong H., Duc H.N., 2022. A new approach based on integration of random subspace and C4. 5 decision tree learning method for spatial prediction of shallow landslides. Vietnam J. Earth Sci., 44(3), 327–342.

Prasad P., Loveson V.J., Kotha M., Yadav R., 2020. Application of machine learning techniques in groundwater potential mapping along the west coast of India. GIScience & Remote Sensing, 57, 735–752.

Sachdeva S., Kumar B., 2021. Comparison of gradient boosted decision trees and random forest for groundwater potential mapping in Dholpur (Rajasthan), India. Stochastic Environmental Research and Risk Assessment, 35, 287–306.

Van Phong T., Pham B.T., 2023. Performance of Naïve Bayes Tree with ensemble learner techniques for groundwater potential mapping. Physics and Chemistry of the Earth, Parts A/B/C, 132, 103503.

Yariyan P., Avand M., Omidvar E., Pham Q.B., Linh N.T.T., Tiefenbacher J.P., 2022. Optimization of statistical and machine learning hybrid models for groundwater potential mapping. Geocarto International, 37, 3877–3911.




How to Cite

Tran Xuan, B., Pham The, T., Luu Thuy, D., Tran Van, P., Vuong Hong, N., Van Le, H., Duc Nguyen, D., Prakash, I., Pham Thanh, T., & Pham Thai, B. (2024). Groundwater potential zoning using Logistics Model Trees based novel ensemble machine learning model. Vietnam Journal of Earth Sciences, 46(2), 272–281.




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