期刊
SCIENCE OF THE TOTAL ENVIRONMENT
卷 634, 期 -, 页码 853-867出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.scitotenv.2018.04.055
关键词
Groundwater spring potential; Machine learning; Ensemble model; GIS; China
资金
- China Postdoctoral Science Foundation [2017M613168, 2017M623327XB]
- Shaanxi Province Postdoctoral Science Foundation [2017BSHYDZZ07]
- National Science Foundation of China [41472234]
- Open Fund of Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources [ZZ2016-1]
- Research Cultivation Fund of Xi'a University of Science and Technology [201607, 201608, 201721]
- Universiti Teknologi Malaysia (UTM) based on a Research University Grant [Q.J130000.2527.17H84]
The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of- evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers. (C) 2018 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据