干旱区生态环境知识资源中心(Arid): Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models

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DOI	10.1016/j.geoderma.2020.114793
	Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models
	Taghizadeh-Mehrjardi, Ruhollah; Schmidt, Karsten; Toomanian, Norair; Heung, Brandon; Behrens, Thorsten; Mosavi, Amirhosein; Band, Shahab S.; Amirian-Chakan, Alireza; Fathabadi, Aboalhasan; Scholten, Thomas
通讯作者	Taghizadeh-Mehrjardi, R
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2021
卷号	383
英文摘要	The low potential of agricultural productivity in the majority of central Iran is mainly attributed to high levels of soil salinity. To increase agricultural productivity, while preventing any further salinization, and implement effective soil reclamation programs, precise information about the spatial patterns and magnitude of soil salinity is essential. In this study, soil salinity was predicted and mapped using machine learning (ML) and digital soil mapping approaches. Specifically, support vector regression (SVR) was combined with wavelet transformation (W-SVR) of a wide range of environmental covariates derived from a digital elevation model, remote sensing, and climatic data. Predictions of soil salinity were carried out for six standard depth increments (0-5, 5-15, 15-30, 30-60, 60-100, 100-200 cm). Cross-validation was carried out by partitioning the data into 70% used for training the model and 30% for testing the model. Uncertainty of the ML algorithms was quantified using the uncertainty estimation based on local errors and clustering (UNEEC) method. The results indicated that W-SVR performed better in predicting soil salinity for all six depth increments. The differences were most apparent for the lowest soil depth increments where W-SVR resulted in -1.4 times higher correlation coefficient when compared to the SVR. At lower soil depths increments, covariate importance analysis indicated that topographic derivatives were the most relevant covariates in the models. For topsoil salinity, remote sensing covariates were the most relevant predictors of soil salinity. Regardless of soil depth, climatic predictors were the most important predictors. Uncertainty analysis also indicated that for all depth increments, the estimated prediction interval for SVR obtained by the UNEEC method was wider than that of W-SVR and further indicating the higher performance of W-SVR in comparison to the SVR. The predicted salinity maps showed the highest salinity for soils in the eastern parts of central Iran, which was consistent with the Agro-climatic Zoning of Isfahan Province.
英文关键词	Digital soil mapping Machine learning Soil salinity Support vector regression Wavelet transformation
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000590747800029
WOS关键词	FUZZY K-MEANS ; CONTINUOUS DEPTH FUNCTIONS ; SALT-AFFECTED SOILS ; VEGETATION INDEX ; ORGANIC-CARBON ; ELECTRICAL-CONDUCTIVITY ; UNCERTAINTY ANALYSIS ; QUANTILE REGRESSION ; WATER ; CLASSIFICATION
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/2XILL650/328008
作者单位	[Taghizadeh-Mehrjardi, Ruhollah; Behrens, Thorsten; Scholten, Thomas] Univ Tubingen, Dept Geosci Soil Sci & Geomorphol, Rumelinstr 19-23, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah] Ardakan Univ, Fac Agr & Nat Resources, Ardakan, Iran; [Schmidt, Karsten] Univ Tubingen, eSci Ctr, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah; Schmidt, Karsten; Behrens, Thorsten; Scholten, Thomas] Univ Tubingen, DFG Cluster Excellence Machine Learning, Tubingen, Germany; [Toomanian, Norair] AREEO, Soil & Water Res Dept, Isfahan Agr & Nat Resources Res & Educ Ctr, Esfahan, Iran; [Heung, Brandon] Dalhousie Univ, Fac Agr, Dept Plant Food & Environm Sci, Halifax, NS, Canada; [Mosavi, Amirhosein] Thuringian Inst Sustainabil & Climate Protect, D-07743 Jena, Germany; [Mosavi, Amirhosein] Norwegian Univ Life Sci, Sch Econ & Business, N-1430 As, Norway; [Band, Shahab S.] Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam; [Band, Shahab S.] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Coll F...
推荐引用方式 GB/T 7714	Taghizadeh-Mehrjardi, Ruhollah,Schmidt, Karsten,Toomanian, Norair,et al. Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models[J],2021,383.
APA	Taghizadeh-Mehrjardi, Ruhollah.,Schmidt, Karsten.,Toomanian, Norair.,Heung, Brandon.,Behrens, Thorsten.,...&Scholten, Thomas.(2021).Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models.GEODERMA,383.
MLA	Taghizadeh-Mehrjardi, Ruhollah,et al."Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models".GEODERMA 383(2021).