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Predicting Soil Erosion in Sloping Hilly Areas of Central Sichuan Based on SVM Model

机译:基于SVM模型的川中丘陵区土壤侵蚀量预测。

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Based on Support Vector Machine (SVM) model, observational data from 1991 to 2000 of the six sloping runoff plots at Suining Soil and Wafer Conservation Experiment Station which located in Hilly Areas of Central Sichuan, China, were used for modeling and predicting the soil erosion. For modeling, five factors (rainfall, rainfall duration, rainfall intensity, vegetation coverage, slope), nine factors (five factors plus early rainfall, early rainfall duration, pre-rainfall intensify and time interval before and after the rain), ten factors (nine factors added to soil and water conservation measures) were as inputs to SVM model, respectively, and the erosion was as output. The results indicated that, the coefficients of efficiency of 5 factors and 9 factors on the soil erosion were 0.52 and 0.55, while that of 10 factors including soil and water conservation measures factor was 0.90. Compared with 5 and 9 factors, the model of 10 factors as input achieved a more satisfied prediction result and could be used for business forecasting. For a particular farming method, the values of soil and water conservation measures factor varied with the slopes, and the values of 15° were 1.46-2.03 times of those of 10°. For a certain farming method, the model with terrain, rainfall, rainfall and the vegetation coverage factors as input factors, could achieve a good effect.
机译:基于支持向量机(SVM)模型,利用四川中部丘陵地区遂宁水土保持试验站6个坡面径流场1991〜2000年的观测数据,对水土流失进行了建模和预测。 。对于建模,五个因子(降雨,降雨持续时间,降雨强度,植被覆盖率,坡度),九个因子(五个因子加上早期降雨,早期降雨持续时间,降雨前强度和降雨前后的时间间隔),十个因子( SVM模型的输入分别是水土保持措施中增加的9个因素)和侵蚀作为输出。结果表明,水土流失的5个因子和9个因子的效率系数分别为0.52和0.55,水土保持措施因子等10个因子的效率系数为0.90。与5个因素和9个因素相比,以10个因素作为输入的模型获得了更为满意的预测结果,可用于业务预测。对于特定的耕作方法,水土保持措施因子的值随坡度而变化,并且15°的值是10°的1.46-2.03倍。对于一定的耕作方式,以地形,降雨,降雨和植被覆盖因子为输入因子的模型可以取得较好的效果。

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