Assessing the performance of decision tree and neural network models in mapping soil properties

doi:10.1007/s11629-019-5409-8

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > Journal of Mountain Science > Journal of Mountain Science-2019 > Vol16 No.8

	Assessing the performance of decision tree and neural network models in mapping soil properties
	Hateffard, Fatemeh; Dolati, Payam; Heidari, Ahmad; Zolfaghari, Ali Asghar
刊名	JOURNAL OF MOUNTAIN SCIENCE
	2019
卷号	16 期号:8 页码:1833-1847
关键词	Digital soil mapping soil properties environmental variables Artificial Neural Network Decision Tree
ISSN号	1672-6316
DOI	10.1007/s11629-019-5409-8
文献子类	Article
英文摘要	To build any spatial soil database, a set of environmental data including digital elevation model (DEM) and satellite images beside geomorphic landscape description are essentials. Such a database, integrates field observations and laboratory analyses data with the results obtained from qualitative and quantitative models. So far, various techniques have been developed for soil data processing. The performance of Artificial Neural Network (ANN) and Decision Tree (DT) models was compared to map out some soil attributes in Alborz Province, Iran. Terrain attributes derived from a DEM along with Landsat 8 ETM+, geomorphology map, and the routine laboratory analyses of the studied area were used as input data. The relationships between soil properties (including sand, silt, clay, electrical conductivity, organic carbon, and carbonates) and the environmental variables were assessed using the Pearson Correlation Coefficient and Principle Components Analysis. Slope, elevation, geomforms, carbonate index, stream network, wetness index, and the band's number 2, 3, 4, and 5 were the most significantly correlated variables. ANN and DT did not show the same accuracy in predicting all parameters. The DT model showed higher performances in estimating sand (R-2=0.73), silt (R-2=0.70), clay (R-2=0.72), organic carbon (R-2=0.71), and carbonates (R-2=0.70). While the ANN model only showed higher performance in predicting soil electrical conductivity (R-2=0.95). The results showed that determination the best model to use, is dependent upon the relation between the considered soil properties with the environmental variables. However, the DT model showed more reasonable results than the ANN model in this study. The results showed that before using a certain model to predict variability of all soil parameters, it would be better to evaluate the efficiency of all possible models for choosing the best fitted model for each property. In other words, most of the developed models are site-specific and may not be applicable to use for predicting other soil properties or other area.
电子版国际标准刊号	1993-0321
语种	英语
WOS记录号	WOS:000478892900008
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/46554]
专题	Journal of Mountain Science_Journal of Mountain Science-2019_Vol16 No.8
推荐引用方式 GB/T 7714	Hateffard, Fatemeh,Dolati, Payam,Heidari, Ahmad,et al. Assessing the performance of decision tree and neural network models in mapping soil properties[J]. JOURNAL OF MOUNTAIN SCIENCE,2019,16(8):1833-1847.
APA	Hateffard, Fatemeh,Dolati, Payam,Heidari, Ahmad,&Zolfaghari, Ali Asghar.(2019).Assessing the performance of decision tree and neural network models in mapping soil properties.JOURNAL OF MOUNTAIN SCIENCE,16(8),1833-1847.
MLA	Hateffard, Fatemeh,et al."Assessing the performance of decision tree and neural network models in mapping soil properties".JOURNAL OF MOUNTAIN SCIENCE 16.8(2019):1833-1847.