干旱区生态环境知识资源中心(Arid): Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions

Arid

DOI	10.1016/j.pedsph.2022.06.009
	Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions
	Khosravi, Khabat; Ngo, Phuong T. T.; Barzegar, Rahim; Quilty, John; Aalami, Mohammad T.; Bui, Dieu T.
通讯作者	Khosravi, K
来源期刊	PEDOSPHERE
ISSN	1002-0160
EISSN	2210-5107
出版年	2022
卷号	32 期号:5 页码:718-732
英文摘要	Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) (i.e., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) (i.e., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random: 70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE = 0.75 cm, MAE = 0.59 cm, NSE = 0.98), while the SCS model had the lowest performance (RMSE = 4.77 cm, MAE = 2.64 cm, NSE = 0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.
英文关键词	additive regression hybrid algorithms empirical model soil water infiltration weighted instances handler wrapper
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000855123900006
WOS关键词	SEQUENTIAL MINIMAL OPTIMIZATION ; ADDITIVE REGRESSION ; RANDOM FOREST ; ENSEMBLE ; PERFORMANCE ; TREES ; WATER ; FLOW ; SVM ; IMPLEMENTATION
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393915
推荐引用方式 GB/T 7714	Khosravi, Khabat,Ngo, Phuong T. T.,Barzegar, Rahim,et al. Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions[J],2022,32(5):718-732.
APA	Khosravi, Khabat,Ngo, Phuong T. T.,Barzegar, Rahim,Quilty, John,Aalami, Mohammad T.,&Bui, Dieu T..(2022).Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions.PEDOSPHERE,32(5),718-732.
MLA	Khosravi, Khabat,et al."Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions".PEDOSPHERE 32.5(2022):718-732.