干旱区生态环境知识资源中心(Arid): AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring

Arid

DOI	10.1080/01431161.2020.1718239
	AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring
	Xu, Hongtao 1,2,3; Chen, Chunbo 1,2,3; Zheng, Hongwei 1,2,3; Luo, Geping1,2,3 ; Yang, Liao 1,2,3; Wang, Weisheng 1,2,3; Wu, Shixin 1,2,3; Ding, Jianli4
通讯作者	Zheng, Hongwei
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2020
卷号	41 期号:12 页码:4470-4495
英文摘要	Salinization is a major eco-environmental threat in arid and semi-arid regions. Machine learning combined with salinization-related factors extracted from satellite images and Digital Elevation Model (DEM) data to monitor salinization is popular in recent years. The hyper-parameters referring to the parameters prior to fitting the model to the data and the features referring to the factors used for model establishment are vital to modelling accuracy, while the optimization of the above two was not given enough attention. In this study, we proposed a novel approach for simultaneously identifying the input features and hyper-parameters of Support Vector Regression (SVR) based on Adaptive Genetic Algorithm (AGA) for quantitative assessment of salinization in Weigan-Kuqa river delta oasis (Wei-Ku oasis), Sangong River Basin, and Qitai oasis of Xinjiang. First, a total of 41 salinization-related factors of 7 categories were extracted from Landsat 5 TM and DEM data. In each sub-region, the Pearson's correlation analysis was developed between Soil Salt Content (SSC) and salinization-related factors, and the factors significantly correlated with SSC were arranged in descending order of absolute correlation coefficient to form the Candidate Feature Variables (CFVs). The ration of Coefficient of Determination (R-2) and Root Mean Square Error (RMSE) multiplied by 1000 was considered as the fitness function of Genetic Algorithm (GA) and AGA. The CFVs and hyper-parameters were combined together and binary coded, then brought into the AGA and GA for simultaneous feature selection and hyper-parameters optimization of SVR, and established salinization monitoring models (AGA-SVR, GA-SVR). In order to highlight the importance of identifying the input features and hyper-parameter to modelling accuracy, the GS-SVR with all the CFVs as input was established using Grid Search (GS) algorithm to optimize the hyper-parameters. Finally, the salinization maps predicted by three models were compared. The results showed that the sensitivity of salinization-related factors to SSC varied with regions, and 25, 16, 24 salinization-related factors were selected as CFVs in Wei-Ku oasis, Sangong River Basin, and Qitai oasis, respectively. Compared with GS-SVR, the GA-SVR and AGA-SVR got more accurate salinization monitoring with fewer features as input, and fitness generated by AGA-SVR increased by 25.968% in Wei-Ku oasis, 25.159% in Sangong River Basin, 27.568% in Qitai oasis, respectively. Both nature and human factors lead to salinization. The difference of Land Surface Temperature (LST) was the main contributor of different salinization between Wei-Ku oasis of southern Xinjiang and the other two sub-regions of northern Xinjiang. The differences in soil texture, irrigation methods, and livestock carrying capacity were the main factors resulting in differences of salinization in Qitai oasis and Sangong River Basin. Our study shows that the proposed approach can provide technical support for accurate salinization monitoring.
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000516866800001
WOS关键词	SALINITY
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
来源机构	中国科学院新疆生态与地理研究所 ; 新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/319341
作者单位	1.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China; 2.Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China; 3.Univ Chinese Acad Sci, Beijing, Peoples R China; 4.Xinjiang Univ, Minist Educ, Key Lab Oasis Ecol, Coll Resources & Environm Sci, Urumqi, Peoples R China
推荐引用方式 GB/T 7714	Xu, Hongtao,Chen, Chunbo,Zheng, Hongwei,et al. AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring[J]. 中国科学院新疆生态与地理研究所, 新疆大学,2020,41(12):4470-4495.
APA	Xu, Hongtao.,Chen, Chunbo.,Zheng, Hongwei.,Luo, Geping.,Yang, Liao.,...&Ding, Jianli.(2020).AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring.INTERNATIONAL JOURNAL OF REMOTE SENSING,41(12),4470-4495.
MLA	Xu, Hongtao,et al."AGA-SVR-based selection of feature subsets and optimization of parameter in regional soil salinization monitoring".INTERNATIONAL JOURNAL OF REMOTE SENSING 41.12(2020):4470-4495.