干旱区生态环境知识资源中心(Arid): Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran

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DOI	10.1016/j.catena.2020.104715
	Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran
	Pahlavan-Rad, Mohammad Reza; Dahmardeh, Khodadad; Hadizadeh, Mojtaba; Keykha, Gholamali; Mohammadnia, Nader; Gangali, Mojtaba; Keikha, Mehdi; Davatgar, Naser; Brungard, Colby
通讯作者	Pahlavan-Rad, MR
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2020
卷号	194
英文摘要	Knowledge of the spatial variation of soil infiltration is necessary for managing water conservation, salinity, and precision agriculture in drylands. In this study, the spatial variation of soil infiltration was investigated using digital soil mapping methods in the Sistan plain, an arid, low-relief flood plain in eastern Iran where a large irrigation project is being implemented to irrigate lands. Information about the spatial variation in soil infiltration will assist the planning of this irrigation project. 138 sampling locations were selected using stratified sampling based on existing polygon-based soil maps. Steady state soil infiltration was measured at each sampling location using the double ring infiltrometer method. Twenty-three environmental covariates were derived from digital elevation models and satellite imagery as well as predictive maps of clay, sand, and silt that were derived from kriging the collected soil samples. A simple (multiple linear regression) and a complex (random forests) model were used to link covariates and infiltration measurements. Ten-fold cross-validation was used to determine model accuracy. Measured soil infiltration ranged from 0.29 to 81.7 mm h(-1) with a mean of 13.6 mm h(-1). RMSE of the infiltration rate predictions were 13.4 mm h(-1) for random forest and 13.9 mm h(-1) for multiple linear regression. MAE was 10.5 for random forest and 10.9 for multiple linear regression. The most important covariates were channel networks, sand concentration, normalized difference salinity index (NDSI), and elevation in the random forest model and distance-from-river and sand concentration in the multiple linear regression model. Accuracy metrics for both models were comparable, but the random forest predictions were judged to be closer to reality based on visual review, thus random forests was chosen to make predictive maps of soil infiltration.
英文关键词	Soil water infiltration Environmental variable Digital soil mapping Sistan
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000566699000049
WOS关键词	SPATIAL VARIABILITY ; TEXTURE ; MODEL ; SCALE
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
来源机构	New Mexico State University
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326084
作者单位	[Pahlavan-Rad, Mohammad Reza] AREEO, Golestan Agr & Nat Resources Res & Educ Ctr, Soil & Water Res Dept, Gorgan, Golestan, Iran; [Dahmardeh, Khodadad; Hadizadeh, Mojtaba; Keykha, Gholamali; Mohammadnia, Nader] AREEO, Sistan Agr & Nat Resources Res & Educ Ctr, Soil & Water Res Dept, Zabol, Iran; [Gangali, Mojtaba] AREEO, Sistan Agr & Nat Resources Res & Educ Ctr, Forest Rangeland & Watershed Res Dept, Zabol, Iran; [Keikha, Mehdi] Univ Zabol, Zabol, Iran; [Davatgar, Naser] AREEO, Soil & Water Res Inst, Karaj, Iran; [Brungard, Colby] New Mexico State Univ, Plant & Environm Sci, Las Cruces, NM 88003 USA
推荐引用方式 GB/T 7714	Pahlavan-Rad, Mohammad Reza,Dahmardeh, Khodadad,Hadizadeh, Mojtaba,et al. Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran[J]. New Mexico State University,2020,194.
APA	Pahlavan-Rad, Mohammad Reza.,Dahmardeh, Khodadad.,Hadizadeh, Mojtaba.,Keykha, Gholamali.,Mohammadnia, Nader.,...&Brungard, Colby.(2020).Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran.CATENA,194.
MLA	Pahlavan-Rad, Mohammad Reza,et al."Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, eastern Iran".CATENA 194(2020).