Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling: A case study at Gia Lai province,Vietnam |
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| Institution: | Department of Geography,School of Social Sciences Education,Vinh University,182 Le Duan,Vinh,Nghe An,Vietnam;University of Transport Technology,Hanoi 100000,Viet Nam;Institute of Geological Sciences,Vietnam Academy of Science and Technology(VAST),84 Chua Lang,Dong Da,Hanoi,Viet Nam;Institute for Water and Environment,Hanoi 100000,Viet Nam;Research Institute of the University of Bucharest,90-92 Sos.Panduri,5th District,Bucharest,Romania;f National Institute of Hydrology and Water Management,Bucure?ti-Ploie?ti Road,97E,1st District,013686 Bucharest,Romania;Faculty of Geography,VNU University of Science,Vietnam National University,334 Nguyen Trai,Hanoi 100000,Viet Nam;Department of Watershed&Arid Zone Management,Gorgan University of Agricultural Sciences&Natural Resources,Gorgan 4918943464,Iran;DDG(R)Geological Survey of India,Gandhinagar 382010,India |
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| Abstract: | The groundwater potential map is an important tool for a sustainable water management and land use planning,particularly for agricultural countries like Vietnam. In this article, we proposed new machine learning ensemble techniques namely Ada Boost ensemble(ABLWL), Bagging ensemble(BLWL), Multi Boost ensemble(MBLWL),Rotation Forest ensemble(RFLWL) with Locally Weighted Learning(LWL) algorithm as a base classifier to build the groundwater potential map of Gia Lai province in Vietnam. For this study, eleven conditioning factors(aspect, altitude, curvature, slope, Stream Transport Index(STI), Topographic Wetness Index(TWI), soil, geology,river density, rainfall, land-use) and 134 wells yield data was used to create training(70%) and testing(30%)datasets for the development and validation of the models. Several statistical indices were used namely Positive Predictive Value(PPV), Negative Predictive Value(NPV), Sensitivity(SST), Specificity(SPF), Accuracy(ACC),Kappa, and Receiver Operating Characteristics(ROC) curve to validate and compare performance of models. Results show that performance of all the models is good to very good(AUC: 0.75 to 0.829) but the ABLWL model with AUC = 0.89 is the best. All the models applied in this study can support decision-makers to streamline the management of the groundwater and to develop economy not only of specific territories but also in other regions across the world with minor changes of the input parameters. |
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