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

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ID: 111356
2020
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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.
Reference Key
nguyen2020internationalgroundwater Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors 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;Nguyen, Phong Tung;Ha, Duong Hai;Jaafari, Abolfazl;Nguyen, Huu Duy;Van Phong, Tran;Al-Ansari, Nadhir;Prakash, Indra;Le, Hiep Van;Pham, Binh Thai;
Journal International journal of environmental research and public health
Year 2020
DOI
10.3390/ijerph17072473
URL
https://www.mdpi.com/1660-4601/17/7/2473
Keywords
Machine learning spatial modeling groundwater potential mapping ensemble modeling Machine learning spatial modeling groundwater potential mapping ensemble modeling National Center for Biotechnology Information NCBI NLM MEDLINE pubmed abstract nih national institutes of health national library of medicine research support non-u.s. gov't pmid:32260438 pmc7177275 doi:10.3390/ijerph17072473 phong tung nguyen duong hai ha binh thai pham

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