Next Article in Journal
Control of the Surrounding Rock of a Goaf-Side Entry Driving Heading Mining Face
Previous Article in Journal
Artificial Neural Networks to Estimate the Influence of Vehicular Emission Variables on Morbidity and Mortality in the Largest Metropolis in South America
Previous Article in Special Issue
Possibilities of Controlling the River Outlets by Weirs on the Example of Noteć Bystra River
Open AccessArticle

Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling

Vietnam Academy for Water Resources, Hanoi 100000, Vietnam
Institute for Water and Environment, Hanoi 100000, Vietnam
Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi 100000, Vietnam
Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam
Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden
University of Transport Technology, Hanoi 100000, Vietnam
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(7), 2622;
Received: 10 February 2020 / Revised: 23 March 2020 / Accepted: 24 March 2020 / Published: 26 March 2020
(This article belongs to the Special Issue Advances and Challenges in the Sustainable Water Management)
Groundwater is one of the most important sources of fresh water all over the world, especially in those countries where rainfall is erratic, such as Vietnam. Nowadays, machine learning (ML) models are being used for the assessment of groundwater potential of the region. Credal decision trees (CDT) is one of the ML models which has been used in such studies. In the present study, the performance of the CDT has been improved using various ensemble frameworks such as Bagging, Dagging, Decorate, Multiboost, and Random SubSpace. Based on these methods, five hybrid models, namely BCDT, Dagging-CDT, Decorate-CDT, MBCDT, and RSSCDT, were developed and applied for groundwater potential mapping of DakLak province of Vietnam. Data of 227 groundwater wells of the study area were utilized for the construction and validation of the models. Twelve groundwater potential conditioning factors, namely rainfall, slope, elevation, river density, Sediment Transport Index (STI), curvature, flow direction, aspect, soil, land use, Topographic Wetness Index (TWI), and geology, were considered for the model studies. Various statistical measures, including area under receiver operating characteristic (AUC) curve, were applied to validate and compare the performance of the models. The results show that performance of the hybrid CDT ensemble models MBCDT (AUC = 0.770), BCDT (AUC = 0.731), Dagging-CDT (AUC = 0.763), Decorate-CDT (AUC = 0.750), and RSSCDT (AUC = 0.766) improved significantly in comparison to the single CDT (AUC = 0.722) model. Therefore, these developed hybrid models can be applied for better ground water potential mapping and groundwater resources management of the study area as well as other regions of the world. View Full-Text
Keywords: Groundwater potential mapping; Machine learning; Ensemble Frameworks; Vietnam Groundwater potential mapping; Machine learning; Ensemble Frameworks; Vietnam
Show Figures

Figure 1

MDPI and ACS Style

Nguyen, P.T.; Ha, D.H.; Nguyen, H.D.; Van Phong, T.; Trinh, P.T.; Al-Ansari, N.; Le, H.V.; Pham, B.T.; Ho, L.S.; Prakash, I. Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling. Sustainability 2020, 12, 2622.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop