摘要: |
【目的】耕地质量定级评价工作是准确把握耕地效益水平,有效保护耕地和科学管理耕地的必要前提。【方法】以襄阳市区耕地为研究对象,从自然要素、社会经济要素、区位因素三方面建立耕地质量评价体系,使用BP神经网络作为耕地质量的评价方法,通过训练建立网络模型,并以仿真练习得出襄阳市区耕地质量评价结果。【结果】(1)BP神经网络模型的精度达到0.93左右,建立的网络模型具有一定的可行性和精确性。(2)襄阳市城区耕地质量总体水平良好,其中二级地和三级地最多,面积占比分别为 33.56%和46.19%;耕地数量分布状况为中心少、四周多;耕地质量的水平则是由东北向西南递减。【结论】BP神经网络的方法可以运用于耕地质量评价工作,且具有可行性和精确性,可为以后的耕地质量评价工作提供一定借鉴与参考。 |
关键词: 耕地质量 BP神经网络模型 耕地定级 襄阳市区 |
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基金项目:国家自然科学基金项目(41401232);华中师范大学中央高校基本科研业务费(CCNU18TS002) |
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Evaluation and Grading of Cultivated Land Quality Based on BP Neural Network Method——A Case Study at Xiangyang Urban area |
zhangzhaohui,NieYan,MaZeyue
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The College of Urban and Environmental Science, Central China Normal University
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Abstract: |
[Purpose]The evaluation and grading of cultivated land quality is the necessary premise for accurately grasping the level of cultivated land benefits, effectively protecting cultivated land and scientifically managing cultivated land.[Methods]Taking cultivated land in Xiangyang urban area as the research object, and establishes the evaluation system of cultivated land quality from three aspects: natural factors, socio-economic factors and location factors. BP neural network is used as the evaluation method of cultivated land quality, and the network model is established through training. Simulation exercises yielded the results of the evaluation of cultivated land quality in Xiangyang urban area. [Result]The results showed as follows:firstly, The accuracy of the BP neural network model is about 0.93, and the established network model has certain feasibility and accuracy.Secondly,the overall quality of cultivated land in Xiangyang urban area is good, with the second and third grades being the largest, accounting for 33.56% and 46.19% respectively; the distribution of cultivated land is less in the urban area center and more in suburbs; the quality of cultivated land is declining from northeast to southwest.[Conclusion]This research proved that The BP neural network method can be applied to the evaluation of cultivated land quality, and it is feasible and accurate. It can provide some reference for the future evaluation of cultivated land quality. |
Key words: cultivated land quality BP neural network model cultivated land grading Xiangyang urban area |