IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i7p2473-d341541.html
   My bibliography  Save this article

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Author

Listed:
  • Phong Tung Nguyen

    (Vietnam Academy for Water Resources, Hanoi 100000, Vietnam)

  • Duong Hai Ha

    (Institute for Water and Environment, Hanoi 100000, Vietnam)

  • Abolfazl Jaafari

    (Research Institute of Forests and Rangelands, Agricultural Research, Education, and Extension Organization (AREEO), P.O. Box 64414-356 Tehran, Iran)

  • Huu Duy Nguyen

    (Faculty of Geography, VNU University of Science, Vietnam National University, 334 Nguyen Trai, Hanoi 100000, Vietnam)

  • Tran Van Phong

    (Institute of Geological Sciences, Vietnam Academy of Sciences and Technology, 84 Chua Lang Street, Dong da, Hanoi 100000, Vietnam)

  • Nadhir Al-Ansari

    (Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 971 87 Lulea, Sweden)

  • Indra Prakash

    (Department of Science & Technology, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Government of Gujarat, Gandhinagar 382002, India)

  • Hiep Van Le

    (Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam)

  • Binh Thai Pham

    (University of Transport Technology, Hanoi 100000, Vietnam)

Abstract

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

Suggested Citation

  • Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2473-:d:341541
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/7/2473/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/7/2473/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Binh Thai Pham & Ataollah Shirzadi & Himan Shahabi & Ebrahim Omidvar & Sushant K. Singh & Mehebub Sahana & Dawood Talebpour Asl & Baharin Bin Ahmad & Nguyen Kim Quoc & Saro Lee, 2019. "Landslide Susceptibility Assessment by Novel Hybrid Machine Learning Algorithms," Sustainability, MDPI, vol. 11(16), pages 1-25, August.
    2. Amarasinghe, Upali A. & Smakhtin, Vladimir, 2014. "Global water demand projections: past, present and future," IWMI Reports 201006, International Water Management Institute.
    3. Viet-Tien Nguyen & Trong Hien Tran & Ngoc Anh Ha & Van Liem Ngo & Al-Ansari Nadhir & Van Phong Tran & Huu Duy Nguyen & Malek M. A. & Ata Amini & Indra Prakash & Lanh Si Ho & Binh Thai Pham, 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam," Sustainability, MDPI, vol. 11(24), pages 1-24, December.
    4. Binh Thai Pham & Trung Nguyen-Thoi & Hai-Bang Ly & Manh Duc Nguyen & Nadhir Al-Ansari & Van-Quan Tran & Tien-Thinh Le, 2020. "Extreme Learning Machine Based Prediction of Soil Shear Strength: A Sensitivity Analysis Using Monte Carlo Simulations and Feature Backward Elimination," Sustainability, MDPI, vol. 12(6), pages 1-29, March.
    5. Amarasinghe, Upali A. & Smakhtin, Vladimir., 2014. "Global water demand projections: past, present and future," IWMI Research Reports H046577, International Water Management Institute.
    6. Phong Tung Nguyen & Duong Hai Ha & Huu Duy Nguyen & Tran Van Phong & Phan Trong Trinh & Nadhir Al-Ansari & Hiep Van Le & Binh Thai Pham & Lanh Si Ho & Indra Prakash, 2020. "Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling," Sustainability, MDPI, vol. 12(7), pages 1-28, March.
    7. Binh Thai Pham & Indra Prakash & Wei Chen & Hai-Bang Ly & Lanh Si Ho & Ebrahim Omidvar & Van Phong Tran & Dieu Tien Bui, 2019. "A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping," Sustainability, MDPI, vol. 11(22), pages 1-30, November.
    8. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    9. Saeid Janizadeh & Mohammadtaghi Avand & Abolfazl Jaafari & Tran Van Phong & Mahmoud Bayat & Ebrahim Ahmadisharaf & Indra Prakash & Binh Thai Pham & Saro Lee, 2019. "Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran," Sustainability, MDPI, vol. 11(19), pages 1-19, September.
    10. Shaghayegh Miraki & Sasan Hedayati Zanganeh & Kamran Chapi & Vijay P. Singh & Ataollah Shirzadi & Himan Shahabi & Binh Thai Pham, 2019. "Mapping Groundwater Potential Using a Novel Hybrid Intelligence Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 281-302, January.
    11. Dong Van Dao & Hojjat Adeli & Hai-Bang Ly & Lu Minh Le & Vuong Minh Le & Tien-Thinh Le & Binh Thai Pham, 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
    12. Vorpahl, Peter & Elsenbeer, Helmut & Märker, Michael & Schröder, Boris, 2012. "How can statistical models help to determine driving factors of landslides?," Ecological Modelling, Elsevier, vol. 239(C), pages 27-39.
    13. Binh Thai Pham & Chongchong Qi & Lanh Si Ho & Trung Nguyen-Thoi & Nadhir Al-Ansari & Manh Duc Nguyen & Huu Duy Nguyen & Hai-Bang Ly & Hiep Van Le & Indra Prakash, 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    2. Neslihan Beden & Nazire Göksu Soydan-Oksal & Sema Arıman & Hayatullah Ahmadzai, 2023. "Delineation of a Groundwater Potential Zone Map for the Kızılırmak Delta by Using Remote-Sensing-Based Geospatial and Analytical Hierarchy Processes," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    3. Ahmed Madani & Burhan Niyazi, 2023. "Groundwater Potential Mapping Using Remote Sensing and Random Forest Machine Learning Model: A Case Study from Lower Part of Wadi Yalamlam, Western Saudi Arabia," Sustainability, MDPI, vol. 15(3), pages 1-15, February.
    4. Yang, Zhixue & Ren, Zhouyang & Li, Hui & Pan, Zhen & Xia, Weiyi, 2024. "A review of tidal current power generation farm planning: Methodologies, characteristics and challenges," Renewable Energy, Elsevier, vol. 220(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Phong Tung Nguyen & Duong Hai Ha & Huu Duy Nguyen & Tran Van Phong & Phan Trong Trinh & Nadhir Al-Ansari & Hiep Van Le & Binh Thai Pham & Lanh Si Ho & Indra Prakash, 2020. "Improvement of Credal Decision Trees Using Ensemble Frameworks for Groundwater Potential Modeling," Sustainability, MDPI, vol. 12(7), pages 1-28, March.
    2. Hai-Bang Ly & Tien-Thinh Le & Huong-Lan Thi Vu & Van Quan Tran & Lu Minh Le & Binh Thai Pham, 2020. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams," Sustainability, MDPI, vol. 12(7), pages 1-34, March.
    3. Hyung-Sup Jung & Saro Lee & Biswajeet Pradhan, 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations," Sustainability, MDPI, vol. 12(6), pages 1-6, March.
    4. Binh Thai Pham & Chongchong Qi & Lanh Si Ho & Trung Nguyen-Thoi & Nadhir Al-Ansari & Manh Duc Nguyen & Huu Duy Nguyen & Hai-Bang Ly & Hiep Van Le & Indra Prakash, 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    5. Caldera, Upeksha & Breyer, Christian, 2020. "Strengthening the global water supply through a decarbonised global desalination sector and improved irrigation systems," Energy, Elsevier, vol. 200(C).
    6. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    7. Md. Uzzal Mia & Tahmida Naher Chowdhury & Rabin Chakrabortty & Subodh Chandra Pal & Mohammad Khalid Al-Sadoon & Romulus Costache & Abu Reza Md. Towfiqul Islam, 2023. "Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer," Land, MDPI, vol. 12(4), pages 1-26, April.
    8. Liu, Jing & Hertel, Thomas & Lammers, Richard & Prusevich, Alexander & Baldos, Uris Lantz & Grogan, Danielle & Frolking, Steve, 2016. "Achieving Sustainable Irrigation Water Withdrawals: Global Impacts on Food Production and Land Use," Conference papers 332691, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    9. Viet-Tien Nguyen & Trong Hien Tran & Ngoc Anh Ha & Van Liem Ngo & Al-Ansari Nadhir & Van Phong Tran & Huu Duy Nguyen & Malek M. A. & Ata Amini & Indra Prakash & Lanh Si Ho & Binh Thai Pham, 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam," Sustainability, MDPI, vol. 11(24), pages 1-24, December.
    10. Mohamed Abdelkareem & Abbas M. Mansour, 2023. "Risk assessment and management of vulnerable areas to flash flood hazards in arid regions using remote sensing and GIS-based knowledge-driven techniques," 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. 117(3), pages 2269-2295, July.
    11. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.
    12. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    13. Weiyu Yu & Nicola A Wardrop & Robert E S Bain & Victor Alegana & Laura J Graham & Jim A Wright, 2019. "Mapping access to domestic water supplies from incomplete data in developing countries: An illustrative assessment for Kenya," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-19, May.
    14. Nassima Amiri & Rachid Lahlali & Said Amiri & Moussa EL Jarroudi & Mohammed Yacoubi Khebiza & Mohammed Messouli, 2021. "Development of an Integrated Model to Assess the Impact of Agricultural Practices and Land Use on Agricultural Production in Morocco under Climate Stress over the Next Twenty Years," Sustainability, MDPI, vol. 13(21), pages 1-23, October.
    15. Liu, Jing & Hertel, Thomas W. & Lammers, Richard & Prusevich, Alexander & Baldos, Uris Lantz C. & Grogan, Danielle S. & Frolking, Steve, 2017. "Achieving Sustainable Irrigation Water Withdrawals: Global Impacts on Food Security and Land Use," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258118, Agricultural and Applied Economics Association.
    16. Shinyoung Kwag & Daegi Hahm & Minkyu Kim & Seunghyun Eem, 2020. "Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    17. Xiao-Jun Wang & Jian-Yun Zhang & Shamsuddin Shahid & Wei Xie & Chao-Yang Du & Xiao-Chuan Shang & Xu Zhang, 2018. "Modeling domestic water demand in Huaihe River Basin of China under climate change and population dynamics," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 20(2), pages 911-924, April.
    18. Viet-Ha Nhu & Ayub Mohammadi & Himan Shahabi & Baharin Bin Ahmad & Nadhir Al-Ansari & Ataollah Shirzadi & John J. Clague & Abolfazl Jaafari & Wei Chen & Hoang Nguyen, 2020. "Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment," IJERPH, MDPI, vol. 17(14), pages 1-23, July.
    19. David D. J. Antia, 2016. "ZVI (Fe 0 ) Desalination: Stability of Product Water," Resources, MDPI, vol. 5(1), pages 1-47, March.
    20. A. Narayanamoorthy & N. Devika & M. Bhattarai, 2016. "More Crop and Profit per Drop of Water: Drip Irrigation for Empowering Distressed Small Farmers," IIM Kozhikode Society & Management Review, , vol. 5(1), pages 83-90, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:17:y:2020:i:7:p:2473-:d:341541. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.