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. 2020 Apr 4;17(7):2473.
doi: 10.3390/ijerph17072473.

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

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Free PMC article

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

Phong Tung Nguyen et al. Int J Environ Res Public Health. .
Free PMC article

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.

Keywords: ensemble modeling; groundwater potential mapping; machine learning; spatial modeling.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Location map of the study area.
Figure 2
Figure 2
Hydrogeological map of the study area.
Figure 3
Figure 3
Maps of groundwater influencing factors: (a) infiltration, (b) rainfall, (c) river density, (d) Stream Power Index (SPI), (e) Sediment Transport Index (STI), (f) Topography Wetness Index (TWI), (g) elevation, (h) aspect, (i) curvature, (j) slope, (k) soil, and (l) land use.
Figure 3
Figure 3
Maps of groundwater influencing factors: (a) infiltration, (b) rainfall, (c) river density, (d) Stream Power Index (SPI), (e) Sediment Transport Index (STI), (f) Topography Wetness Index (TWI), (g) elevation, (h) aspect, (i) curvature, (j) slope, (k) soil, and (l) land use.
Figure 3
Figure 3
Maps of groundwater influencing factors: (a) infiltration, (b) rainfall, (c) river density, (d) Stream Power Index (SPI), (e) Sediment Transport Index (STI), (f) Topography Wetness Index (TWI), (g) elevation, (h) aspect, (i) curvature, (j) slope, (k) soil, and (l) land use.
Figure 4
Figure 4
Flowchart of the modeling methodology (Wherein: SPI, stream power index; STI, sediment transport index; topography wetness index, TWI, ANN, artificial neural network; and RABANN, the ensemble model of RAB and ANN).
Figure 5
Figure 5
Receiver Operating Characteristic (ROC) curves and AUC values of the models: (a) training and (b) validation datasets.
Figure 6
Figure 6
Groundwater potential maps produced using (a) ANN (Artificial Neural Networks) and (b) RABANN (the ensemble model of RAB and ANN).
Figure 6
Figure 6
Groundwater potential maps produced using (a) ANN (Artificial Neural Networks) and (b) RABANN (the ensemble model of RAB and ANN).
Figure 7
Figure 7
Analysis of the groundwater potential maps.

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References

    1. Carrard N., Foster T., Willetts J. Groundwater as a Source of Drinking Water in Southeast Asia and the Pacific: A Multi-Country Review of Current Reliance and Resource Concerns. Water. 2019;11:1605 doi: 10.3390/w11081605. - DOI
    1. Grönwall J., Oduro-Kwarteng S. Groundwater as a strategic resource for improved resilience: A case study from peri-urban Accra. Environ. Earth Sci. 2018;77:6. doi: 10.1007/s12665-017-7181-9. - DOI
    1. Magesh N., Chandrasekar N., Soundranayagam J.P. Delineation of groundwater potential zones in Theni district, Tamil Nadu, using remote sensing, GIS and MIF techniques. Geosci. Front. 2012;3:189–196. doi: 10.1016/j.gsf.2011.10.007. - DOI
    1. Amarasinghe U.A., Smakhtin V. Global Water Demand Projections: Past, Present and Future. Volume 156 IWMI; Colombo, Sri Lanka: 2014.
    1. Vo P. Urbanization and water management in Ho Chi Minh City, Vietnam-issues, challenges and perspectives. GeoJournal. 2008;70:75–89. doi: 10.1007/s10708-008-9115-2. - DOI
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