Prediction of Soil Erosion Loss Mass in the Coal Mining Areas of Jilin Province Based on 3S Technology and BP Neural Network

Abstract:

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This paper partitioned five major coal mining areas respectively in central, southern and eastern Jilin Province for case study based on current situation of exploitation and distribution of coal resources through artificial neural network(ANN) and the 3S technology to gain soil erosion loss mass. On the basis of RUSLE equation, BP neural network is fused to gain the rainfall erosion index of higher precision than those of traditional method. By extracting of indices and raster calculation on the platform of ERDAS and ArcGIS software, we made predication of soil erosion loss of the coal mining areas. After verification, the precision of rainfall erosion index is high, and thus improved the predicting accuracy of soil erosion. Comparative analysis shows that the soil erosion in central section of Jilin Province has much lower intensity, and high degree erosion occurred in the east and south mostly.

Info:

Periodical:

Advanced Materials Research (Volumes 225-226)

Edited by:

Helen Zhang, Gang Shen and David Jin

Pages:

1246-1249

DOI:

10.4028/www.scientific.net/AMR.225-226.1246

Citation:

J. Tang and Y. Ji, "Prediction of Soil Erosion Loss Mass in the Coal Mining Areas of Jilin Province Based on 3S Technology and BP Neural Network", Advanced Materials Research, Vols. 225-226, pp. 1246-1249, 2011

Online since:

April 2011

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$35.00

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