干旱区生态环境知识资源中心(Arid): Multi-algorithm comparison for predicting soil salinity

Arid

DOI	10.1016/j.geoderma.2020.114211
	Multi-algorithm comparison for predicting soil salinity
	Wang, Fei 1; Shi, Zhou 2; Biswas, Asim 3; Yang, Shengtian 1; Ding, Jianli1
通讯作者	Ding, Jianli
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2020
卷号	365
英文摘要	Soil salinization is one of the most predominant processes responsible for land degradation globally. However, monitoring large areas presents significant challenges due to strong spatial and temporal variability. Environmental covariates show promise in predicting salinity over large areas provided a reasonable relationship is developed with field measured salinity at few points. While simple regression-based approaches to complex data mining methods have been used in the prediction, a comprehensive comparison of their performances has not been explored, leading to uncertainty in which algorithms to select. This study compares thirteen popularly and non-popularly used algorithms and their performances following four criteria in predicting soil salinity from environmental covariates from Kuqa Oasis from Xinjiang, China. The environmental covariates used for the prediction include principal components of Landsat satellite images at multiple spectral bands, climate factors (referring to land surface temperature), vegetation indices, salinity and soil-related indices, soil moisture indices, DEM derived indices, land use, landform and soil type and categorized them under parameter categories of the SCORPAN (S, soils; C, climate; O, organisms, biotic factor; R, relief; P, parent material; A, age; and N, space) model. The predictive relationships were developed using the algorithms including some previously used ones such as Multiple Linear regression (MLR), Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN), Stochastic Gradient Treeboost (SGT), M5 Model Tree (M5), Multivariate Adaptive Regression Splines (MARS), Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Regression (SVR) and some that have not previously been used in predicting salinity such as Alternating Model Tree (ATM), Gaussian Processes Regression (GPR), Gaussian Radial Basis Functions (GRBF), Least Median Squared Linear Regression (LMSLR), and Reduced Error Pruning Tree (REPTree). Here, 5-fold cross-validation and an independent dataset (30% overall samples) at three depths, 010 cm, 1030 cm, 3050 cm, were used for parameter optimization and evaluating the performance of algorithms. The performances of these algorithms were compared against multiple criteria, including the parameterization, error level/fitting accuracy (determination coefficient, R2; root mean squared error, RMSE), stability (based on the Pearson correlation coefficient, R; mean absolute percent error, MAPE; root mean squared error, RMSE; Lins concordance correlation coefficient, LCCC) and computational efficiency of the algorithms. Finally, the result showed that CSRI is most important parameter for the prediction of soil salinity at the 010 cm and 1030 cm depths, whereas for the 3050 cm depth interval, VD was the most important predictor. For depths of 010 cm, 1030 cm and 3050 cm across all models, the model R2 values ranged from 0.60 to 0.74, 0.15 to 0.31, and 0.30 to 0.47, and the RMSE values ranged from 18.87 to 23.49 dS m-1, 9.94 to 13.48 dS m-1 and 3.79 to 7.11 dS m-1. The optimal algorithms at three depths of 010 cm, 1030 cm and 3050 cm are RF, M5 and GRBF with considering accuracy and stability. After a comprehensive assessment of algorithm performance, we recommend RF for mapping salinity in an arid environment such as that of Xinjiang and elsewhere globally. However, there is no algorithm that can perform ideally for all datasets. Therefore, we suggest that the algorithm should be carefully chosen according to the purposes of the study.
英文关键词	Soil salinization Predictive mapping Environmental covariates Algorithms Random forest
类型	Article
语种	英语
国家	Peoples R China ; Canada
收录类别	SCI-E
WOS记录号	WOS:000518707700002
WOS关键词	STOCHASTIC GRADIENT TREEBOOST ; MACHINE-LEARNING TECHNIQUES ; MODIS TIME-SERIES ; LAND-USE ; SPATIAL PREDICTION ; SOUTHERN XINJIANG ; NEURAL-NETWORKS ; RIVER-BASIN ; REGRESSION ; SALINIZATION
WOS类目	Soil Science
WOS研究方向	Agriculture
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314590
作者单位	1.Xinjiang Univ, Coll Resource & Environm Sci, Xinjiang Common Univ Key Lab Smart City & Environ, Urumqi 830046, Peoples R China; 2.Zhejiang Univ, Inst Appl Remote Sensing & Informat, Hangzhou 310058, Peoples R China; 3.Univ Guelph, Sch Environm Sci, Guelph, ON N1G 2W1, Canada
推荐引用方式 GB/T 7714	Wang, Fei,Shi, Zhou,Biswas, Asim,et al. Multi-algorithm comparison for predicting soil salinity[J]. 新疆大学,2020,365.
APA	Wang, Fei,Shi, Zhou,Biswas, Asim,Yang, Shengtian,&Ding, Jianli.(2020).Multi-algorithm comparison for predicting soil salinity.GEODERMA,365.
MLA	Wang, Fei,et al."Multi-algorithm comparison for predicting soil salinity".GEODERMA 365(2020).