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| 黑龙江省黑土区拉林河流域土壤侵蚀强度评价方法比较 | |
| 引用本文： | 周宁,满秀玲,李超.黑龙江省黑土区拉林河流域土壤侵蚀强度评价方法比较[J].中国水土保持科学,2013,11(3):73-77. |
| 作者姓名： | 周宁 满秀玲 李超 |
| 作者单位： | 1. 东北林业大学,150040,哈尔滨;黑龙江省水土保持科学研究所,150070,哈尔滨 2. 东北林业大学,150040,哈尔滨 3. 北京林业大学,100083,北京 |
| 基金项目： | 水利部公益性行业科技项目"东北黑土区水土保持措施效益评估及防治技术" |
| 摘 要： | 为了保护水土资源、改善生态环境,进行区域土壤侵蚀强度评价,以黑龙江省黑土区拉林河流域为研究区,选取坡度、坡向、土壤类型、土地利用状况和标准化植被指数等5项评价指标,分别采用逻辑回归和广义回归神经网络模型,在ArcGIS平台上进行土壤侵蚀强度评价。应用受试者工作特征曲线对2种方法的评价结果进行对比。结果表明:逻辑回归模型和广义回归神经网络模型的受试者工作特征曲线下面积值分别为0.857和0.881,与实际的土壤侵蚀强度情况基本吻合;2种模型的评价结果可以相互校验,广义回归神经网络模型评价结果的精度较高。 |
| 关 键 词： | 土壤侵蚀强度 逻辑回归 广义回归神经网络 |
Comparison of the soil erosion intensity evaluation method in Lalin River Watershed of Heilongjiang black earth region |
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| Zhou Ning , Man Xiuling , Li Chao.Comparison of the soil erosion intensity evaluation method in Lalin River Watershed of Heilongjiang black earth region[J].Science of Soil and Water Conservation,2013,11(3):73-77. | |
| Authors: | Zhou Ning Man Xiuling Li Chao |
| Institution: | 1.Northeast Forestry University,150040,Harbin;2.Heilongjiang Soil and Water Conservation Science Institute,150070,Harbin; 3.Beijing Forestry University,100083,Beijing： China) |
| Abstract: | In order to protect water and soil resources and the environment,evaluate the regional soil erosion intensity,this paper took the Lalin River Watershed of Heilongjiang black earth region as the studied area.Slope angle,aspect,soil types,landuse and normalized vegetation index were chosen as evaluation indexes,and the logistic regression model and generalized regression neural network model were adopted to assess soil erosion intensity with the ArcGIS platform.Then,the results of two models were evaluated by the receiver-operating characteristic curve（i.e.ROC curve）.Analyses results showed that areas under the curve（i.e.AUC） of logistic regression model and generalized regression neural network model were respectively 0.857 and 0.881,consistent with the actual soil erosion intensity.The evaluation results of two models were established for mutual and the results of generalized regression neural network model were more accurate. |
| Keywords: | soil erosion intensity logistic regression general regression neural network |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |