Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks

CORC > 生态环境研究中心 > 中国科学院生态环境研究中心 > 城市与区域生态国家重点实验室

	Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks
	Li Yunkai; Tian Yingjie; Ouyang Zhiyun; Wang Lingyan; Xu Tingwu; Yang Peiling; Zhao Huanxun
刊名	ENVIRONMENTAL EARTH SCIENCES
	2010-06
卷号	60 期号:7 页码:1559-1568
关键词	Soil erosion Particle swarm algorithm Support vector machine Small watershed Characteristics extraction
中文摘要	Sand production by soil erosion in small watershed is a complex physical process. There are few physical models suitable to describe the characteristics of the intense erosion in domestic loess plateau. Introducing support vector machine (SVM) oriented to small sample data and possessing good extension property can be an effective approach to predict soil erosion because SVM has been applied in hydrological prediction to some extent. But there are no effective methods to select the rational parameters for SVM, which seriously limited the practical application of SVM. This paper explored the application of intelligence-based particle swarm optimization (PSO) algorithm in automatic selection of parameters for SVM, and proposed a prediction model by linking PSO and SVM for small sample data analysis. This method utilized the high efficiency optimization property and swarm paralleling property of PSO algorithm and the relatively strong learning and extending capacity of SVM. For an example of Huangfuchuan small watershed, its intensive fragmentation and intense erosion earn itself the name of "worst erosion in the world". Using four characteristics selection algorithms of correlation feature selection, the primary affecting factors for soil erosion in this small watershed were determined to be the channel density, ravine area, sand rock proportion, and the total vegetation coverage. Based on the proposed PSO-SVM algorithm, the soil erosion modulus in the small watershed was predicted. The accuracy of the simulation and prediction was good, and the average error was 3.85%. The SVM predicting model was based on the monitoring data of sand production. The construction of the SVM erosion modulus prediction model for the small watershed comprehensively reflected the complex mechanism of soil erosion and sand production. It had certain advantage and relatively high practical value in small sample prediction in the discipline of soil erosion.
WOS记录号	WOS:000277956000021
内容类型	期刊论文
源URL	[http://ir.rcees.ac.cn/handle/311016/20951]
专题	生态环境研究中心_城市与区域生态国家重点实验室
推荐引用方式 GB/T 7714	Li Yunkai,Tian Yingjie,Ouyang Zhiyun,et al. Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks[J]. ENVIRONMENTAL EARTH SCIENCES,2010,60(7):1559-1568.
APA	Li Yunkai.,Tian Yingjie.,Ouyang Zhiyun.,Wang Lingyan.,Xu Tingwu.,...&Zhao Huanxun.(2010).Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks.ENVIRONMENTAL EARTH SCIENCES,60(7),1559-1568.
MLA	Li Yunkai,et al."Analysis of soil erosion characteristics in small watersheds with particle swarm optimization, support vector machine, and artificial neuronal networks".ENVIRONMENTAL EARTH SCIENCES 60.7(2010):1559-1568.