干旱区生态环境知识资源中心(Arid): Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization

Arid

DOI	10.1080/01431161.2021.2009589
	Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization
	Chen, Baili; Zheng, Hongwei; Luo, Geping; Chen, Chunbo; Bao, Anming; Liu, Tie; Chen, Xi
通讯作者	Zheng, HW (corresponding author)，Chinese Acad Sci, Urumqi 830011, Peoples R China.
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2022
卷号	43 期号:3 页码:778-811
英文摘要	Soil salinization endangers the development of ecological agriculture. As soil salinization is often heavily affected by regional environments, difficulties arise when constructing an adaptive multi-regional soil salinity estimation model. In this study, we proposed an extreme gradient boosting (XGBoost) model based on the Tree-structure Parzen Estimator (TPE) optimization algorithm to apply to four study areas with different environments (TPE-XGBoost). The four areas are the Weigan-Kuqa Oasis (Weiku), the Sangong River Basin (Sgr) and the Qitai Oasis in Xinjiang, China, and the middle and lower reaches of the Syr Darya Basin in Kazakhstan. Most previous soil salinity studies did not pay much attention to the impact of feature selection and hyper-parameter tuning on the performance of machine learning models, and the complex dependence and interaction between input features and hyper-parameters. In order to improve the performance of XGBoost model in estimating soil salinity, we proposed for the first time to use TPE algorithm to jointly optimize feature selection and hyper-parameter tuning, and verified it in four areas. Coefficient of determination (R-2) and Root Mean Square Error (RMSE) were used to evaluate the model performance. First, we calculated 55 environmental features from Landsat and terrain data. Then, in order to reduce the computational complexity of the TPE-XGBoost model, we used Pearson correlation analysis between surface soil salinity content (SSC) and features to initially filter out the features that were not significantly related (P > 0.05). Finally, the TPE algorithm was used to jointly optimize the parameter space composed of features and hyper-parameters. The results showed that (1) TPE joint optimization algorithm significantly improved the performance of the XGBoost model, achieving high accuracy in the four areas, and had powerful generalization. R-2 values of test sets for Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin were 0.95, 0.95, 0.80, and 0.81, respectively. (2) There is no universal feature can be applied to soil salinity inversion in different environments. TPE algorithm adaptively selected different types and numbers of features for four areas, 19, 11, 25, and 15 features were selected in Weiku Oasis, Qitai Oasis, Sgr Basin, and the Syr Basin, respectively. This showed that the optimal model parameters should not be fixed parameters, but should be re-determined locally according to different environmental conditions. The TPE algorithm can capture the features that reflect environmental differences. (3) The XGBoost model can provide feature importance ranking, which improves the interpretability of machine learning model. The importance analysis results showed that the features had different contributions in different areas. The TPE-XGBoost model proposed in this study has great potential in multi-regional soil salt estimation research.
英文关键词	Soil salinization multi-regions XGBoost TPE optimization feature selection hyper-parameter tuning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000752059800001
WOS关键词	YELLOW-RIVER DELTA ; XINJIANG PROVINCE ; WET SEASONS ; SALINITY ; PARAMETER ; SELECTION ; INDEXES ; OASIS ; CAPABILITY ; DYNAMICS
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376463
作者单位	[Chen, Baili; Zheng, Hongwei; Luo, Geping; Chen, Chunbo; Bao, Anming; Liu, Tie; Chen, Xi] Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi, Peoples R China; [Chen, Baili; Zheng, Hongwei; Luo, Geping; Chen, Chunbo; Bao, Anming; Liu, Tie; Chen, Xi] Chinese Acad Sci, Res Ctr Ecol & Environm Cent Asia, Urumqi, Peoples R China; [Chen, Baili; Zheng, Hongwei; Luo, Geping; Chen, Chunbo; Bao, Anming; Liu, Tie; Chen, Xi] Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Chen, Baili,Zheng, Hongwei,Luo, Geping,et al. Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization[J],2022,43(3):778-811.
APA	Chen, Baili.,Zheng, Hongwei.,Luo, Geping.,Chen, Chunbo.,Bao, Anming.,...&Chen, Xi.(2022).Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization.INTERNATIONAL JOURNAL OF REMOTE SENSING,43(3),778-811.
MLA	Chen, Baili,et al."Adaptive estimation of multi-regional soil salinization using extreme gradient boosting with Bayesian TPE optimization".INTERNATIONAL JOURNAL OF REMOTE SENSING 43.3(2022):778-811.