干旱区生态环境知识资源中心(Arid): Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran

Arid

DOI	10.1007/s12517-020-05576-4
	Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran
	Forghani, Seyed Javad; Pahlavan-Rad, Mohammad Reza; Esfandiari, Mehrdad; Torkashvand, Ali Mohammadi
通讯作者	Pahlavan-Rad, MR
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2020
卷号	13 期号:13
英文摘要	In the current study, the variations of soil classes at the first and second levels of WRB (World Reference Base for soil resource) soil classification system were investigated by two machine learning including multinomial logistic regression (MLR) and random forest (RF) models in an arid floodplain which covers an area approximately 600 km(2)located in Sistan region, Iran. The model's performance was tested using 10-fold cross-validation by calculation of overall model accuracy and the kappa statistic. Three main Reference Soil Groups (RSGs) including Cambisols, Fluvisols, and Solonchaks at the first level, and 18 WRB soil groups at the second level were identified. Results showed that the overall accuracy at the first level of WRB was 53% and 49% with a kappa of 0.26 and 0.19 for MLR and RF models, respectively. At the second level of WRB, the overall accuracy was 11% and 21% with a kappa of 0 and 0.09 for MLR and RF models, respectively. Also, results showed that the MLR model had better performance (overall accuracy = 53%) at the first level of WRB, but the RF model showed better prediction (overall accuracy = 21%) at the second level of WRB. Multiresolution Valley Bottom Flatness Index (MrVBF), Normalized Difference Salinity Index (NDSI), Multiresolution of Ridge Top Flatness Index (MrRTF), convergence index, and channel network base level were among top covariates used for prediction at two levels of WRB. Results revealed the complexity of soil variations in this floodplain. Using other covariates such as soil texture and salinity maps can improve the prediction power. Increasing the size of sampling is recommended to improve the accuracy of the models in predicting the second level of WRB in this area.
英文关键词	Soil variations Soil classes Sistan Digital soil mapping Hirmand
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000544980700003
WOS关键词	SEMIARID REGION ; GREAT GROUPS ; ORGANIC-MATTER ; CLASSIFICATION ; MAP ; CARBON ; EFFICIENCY ; FRACTIONS ; TAXONOMY
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324909
作者单位	[Forghani, Seyed Javad; Esfandiari, Mehrdad; Torkashvand, Ali Mohammadi] Islamic Azad Univ, Dept Soil Sci, Sci & Res Branch, Tehran, Iran; [Pahlavan-Rad, Mohammad Reza] AREEO, Soil & Water Res Dept, Golestan Agr & Nat Resources Res & Educ Ctr, Gorgan, Golestan, Iran
推荐引用方式 GB/T 7714	Forghani, Seyed Javad,Pahlavan-Rad, Mohammad Reza,Esfandiari, Mehrdad,et al. Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran[J],2020,13(13).
APA	Forghani, Seyed Javad,Pahlavan-Rad, Mohammad Reza,Esfandiari, Mehrdad,&Torkashvand, Ali Mohammadi.(2020).Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran.ARABIAN JOURNAL OF GEOSCIENCES,13(13).
MLA	Forghani, Seyed Javad,et al."Spatial prediction of WRB soil classes in an arid floodplain using multinomial logistic regression and random forest models, south-east of Iran".ARABIAN JOURNAL OF GEOSCIENCES 13.13(2020).