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Erschienen in:

09.09.2021

Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping

verfasst von: Duong Hai Ha, Phong Tung Nguyen, Romulus Costache, Nadhir Al-Ansari, Tran Van Phong, Huu Duy Nguyen, Mahdis Amiri, Rohit Sharma, Indra Prakash, Hiep Van Le, Hanh Bich Thi Nguyen, Binh Thai Pham

Erschienen in: Water Resources Management | Ausgabe 13/2021

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Abstract

In this study, the AdaBoost, MultiBoost and RealAdaBoost methods were combined with the Quadratic Discriminant Analysis method to develop three new GIS-based Machine Learning ensemble models, i.e., ABQDA, MBQDA, and RABQDA for groundwater potential mapping in the Dak Nong Province, Vietnam. In total, 227 groundwater wells and 12 conditioning factors (infiltration, rainfall, river density, topographic wetness index, sediment transport index, stream power index, elevation, aspect, curvature, slope, soil, and land use) were used for this study. Performance of the models was evaluated using the Area Under the Receiver Operating Characteristics Curve AUC (AUC) and several other performance metrics. The results showed that the ABQDA model that achieved AUC = 0.741 was superior to the other models in producing an accurate map of groundwater potential for the Dak Nong Province. The models and potential maps produced here can help policymakers and water resources managers to preserve an optimal exploit from these vital resources.

Vorheriger Artikel Implementation of Strategies for the Management of Dams with Sedimented Reservoirs

Nächster Artikel Regional Frequency Analysis Based on Precipitation Regionalization Accounting for Temporal Variability and a Nonstationary Index Flood Model

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Titel: Quadratic Discriminant Analysis Based Ensemble Machine Learning Models for Groundwater Potential Modeling and Mapping
verfasst von: Duong Hai Ha
Phong Tung Nguyen
Romulus Costache
Nadhir Al-Ansari
Tran Van Phong
Huu Duy Nguyen
Mahdis Amiri
Rohit Sharma
Indra Prakash
Hiep Van Le
Hanh Bich Thi Nguyen
Binh Thai Pham
Publikationsdatum: 09.09.2021
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 13/2021
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-021-02957-6

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