Machine learning-based identification for the main influencing factors of alluvial fan development in the Lhasa River Basin,Qinghai-Tibet Plateau

作     者：CHEN Tongde WEI Wei JIAO Juying ZHANG Ziqi LI Jianjun 陈同德;魏伟;焦菊英;张子琦;李建军

作者机构：State Key Laboratory of Soil Erosion and Dryland Farming on the Loess PlateauInstitute of Soil and Water ConservationNorthwest A&F UniversityYangling 712100ShaanxiChina School of AutomationNorthwestern Polytechnical UniversityXi'an 710072China Institute of Soil and Water ConservationChinese Academy of Sciences and Ministry of Water ResourcesYangling 712100ShaanxiChina 

出 版 物：《Journal of Geographical Sciences》 (地理学报（英文版）)

年 卷 期：2022年第32卷第8期

页      面：1557-1580页

核心收录：

学科分类：08[工学] 081502[工学-水力学及河流动力学] 0815[工学-水利工程] 

基　　金：The Strategic Priority Research Program of Chinese Academy of Sciences,No.XDA20040202 The Second Tibetan Plateau Scientific Expedition and Research Program (STEP),No.2019QZKK0603 

主　　题：alluvial fan machine learning feature importance XGBoost Lhasa River Basin 

摘      要：Alluvial fans are an important land resource in the Qinghai-Tibet Plateau with the expansion of human activities. However, the factors of alluvial fan development are poorly understood. According to our previous investigation and research, approximately 826 alluvial fans exist in the Lhasa River Basin(LRB). The main purpose of this work is to identify the main influencing factors by using machine learning. A development index(Di) of alluvial fan was created by combining its area, perimeter, height and gradient. The 72% of data, including Di, 11 types of environmental parameters of the matching catchment of alluvial fan and 10 commonly used machine learning algorithms were used to train and build *** 18% of data were used to validate models. The remaining 10% of data were used to test the model accuracy. The feature importance of the model was used to illustrate the significance of the 11 types of environmental parameters to Di. The primary modelling results showed that the accuracy of the ensemble models, including Gradient Boost Decision Tree,Random Forest and XGBoost, are not less than 0.5(R^(2)). The accuracy of the Gradient Boost Decision Tree and XGBoost improved after grid research, and their R^(2) values are 0.782 and 0.870, respectively. The XGBoost was selected as the final model due to its optimal accuracy and generalisation ability at the sites closest to the LRB. Morphology parameters are the main factors in alluvial fan development, with a cumulative value of relative feature importance of 74.60% in XGBoost. The final model will have better accuracy and generalisation ability after adding training samples in other regions.