›› 2006, Vol. 27 ›› Issue (8): 1425-1428.

• Fundamental Theroy and Experimental Research • Previous Articles     Next Articles

Study of slope rain drops splash-erosion based on rough neural network

DING Jia-ming1, 2, WANG Yong-he1, DING Li-xing1   

  1. 1. School of Civil Engineering and Architecture, Central South University, Changsha 410075, China; 2. School of Highway Engineering, Changsha University of Science & Technology, Changsha 410076, China
  • Received:2004-11-04 Online:2006-08-10 Published:2013-11-26

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Abstract: It is presented to predict slope splash-erosion for rain drops based on rough neural network. The redundancy information is reducted by the relative dependability between condition attribute and decision attribute of rough sets. The test indexes such as water depth and flux are deleted. The 2−5−1 rough neural network is established in which input parameters are gradient and rain intensity while output parameter is splash-erosion quantity. The frame of neural network is predigested. The train time of neural network is decreased. The reduced sloping field rain drops splash-erosion linear regression correlation coefficient of prediction and experiment is larger than that had not been reduced. The constringency speed is faster than that had not been reduced. The example calculation indicates that the rough neural network is an efficient and feasible algorithm to forecast sloping field rain drops splash-erosion.

Key words: rough sets, neural network, rain drops, splash-erosion

CLC Number: 

  • U 416.1+4
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
  • Please send e-mail to pingzhou3@126.com if you would like to read full paper in English for free. Parts of our published papers have English translations.
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