JOURNAL OF NATURAL RESOURCES ›› 2018, Vol. 33 ›› Issue (4): 669-683.doi: 10.11849/zrzyxb.20161178

• Resource Evaluation • Previous Articles     Next Articles

Simulation of Soil Erosion Intensity in the Three Gorges Reservoir Area Using BP Neural Network

LIU Ting1, SHAO Jing-an1,2   

  1. 1.College of Geography and Tourism, Chongqing Normal University, Chongqing 400047, China;
    2. Key Laboratory of Surface Process and Environment Remote Sensing in the Three Gorges Reservoir Area, Chongqing 400047, China
  • Received:2016-10-31 Revised:2017-07-15 Online:2018-04-10 Published:2018-04-10
  • Supported by:
    Chongqing Research Program of Basic Research and Frontier Technology, No. cstc2017jcyjB0317.

Abstract: Soil erosion is one of the most important factors affecting the fragility of the ecological environment in the Three Gorges Reservoir area. The change of rainfall erosivity is a complex process and its variation has certain stochastic fluctuation. Understanding the evolution of soil erosion intensity and its future trends are the key scientific issues, which need to be resolved in the process of ecological civilization construction in the Three Gorges Reservoir area. Moreover, it is of great significance to build an appropriate ecological production paradigm, and to formulate measures to prevent and control soil erosion. Based on the characteristics of rainfall erosion in the Three Gorges Reservoir area in 1990, this paper simulated and verified the rainfall erosivity at 75 stations in 2010 using BP neural network. On this basis, the rainfall erosivity at 75 stations in 2030 was predicted. The forecast results of rainfall erosivity at 27 stations located around the Three Gorges Reservoir area were selected and interpolated with Kriging method. Combined with the simulated land use in the Three Gorges Reservoir area in the natural growth and ecological protection scenarios in 2030, the soil erosion intensity in 2030 was calculated using the revised soil loss equation (RUSLE). The results were as follows: In 2010, the relative error of rainfall erosivity simulation was 15%, the relative error of tested samples was 14.67%, the relative error of prediction was 19.65%, and the NE coefficient was 0.85, which indicated that BP neural network had a good result of rainfall erosivity simulation in the Three Gorges Reservoir area. In 2010, the Kappa index of soil erosion intensity in the Three Gorges Reservoir area was 0.75, and the overall calculation results could meet the needs of simulation and prediction. When land use does not change in the Three Gorges Reservoir area till 2030, the areas of slight and moderate erosion will both increase, the areas of micro erosion and above intensity erosion will decrease. About 58% of the change in erosion intensity happen between adjacent erosion intensity types, and less change happen across erosion grade types. Under the condition of constant rainfall erosivity, the soil erosion caused by future land use change in both natural growth scenario and ecological protection scenario has decreasing tendency, while the tendency in the latter scenario is more obvious. If both rainfall erosivity and land use change, soil erosion in both scenarios show downward trends. However, there will be a certain degree of deterioration in areas with less erosion. Therefore, the policies of “controlling severe erosion, preventing slight erosion” should be taken. It was worth noting that simulated results only show the possibility of rainfall erosivity change which are not completely deterministic.

Key words: BP neural network, RUSLE, soil erosion intensity, the Three Gorges Reservoir area

CLC Number: 

  • S157