Locally weighted learning based hybrid intelligence models for groundwater potential mapping and modeling:A case study at Gia Lai province, Vietnam

• 作者: Hoang Phan Hai Yen Binh Thai Pham Tran Van Phong Duong Hai Ha Romulus Costache Hiep Van Le Huu Duy Nguyen Mahdis Amiri Nguyen Van Tao Indra Prakash
• 来源期刊: 地学前缘（英文版）
• 年，卷（期）: 2021,（5）
• 所属期刊栏目: Research Paper
• 页码范围: 54-68
• 页数: 15页
• 语种: 英文

摘要

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 AdaBoost 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. Re-sults 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 re-gions across the world with minor changes of the input parameters.