基于随机森林算法的耕地质量定级指标体系研究

    Research on the index system of cultivated land quality grading based on random forest algorithm

    • 摘要:
      目的  分析研究区域内的耕地质量差异,优化耕地利用与布局,为耕地保护提供参考依据。
      方法  以青海省共和县、都兰县和乌兰县的耕地为研究对象,根据历史及现有文献收集耕地质量的影响因素,采用随机森林算法和相关性分析筛选定级指标并确认权重,通过加权求和法计算定级指数并划分级别,得到定级结果。与常用的特尔菲法定级成果进行比较分析。
      结果  随机森林算法得到的变量重要性(I)范围在0.03~11.94,相关性分析结果显示,大部分影响因素间相关性不显著,有8个为显著相关,综合I值和相关性分析结果将30个影响因素收敛为4个纬度下的14个定级指标,其中影响研究区域耕地质量的主要因素为生态系统脆弱性、生长季平均降水和年总太阳辐射量,权重分别为0.11、0.10和0.09,随机森林算法评价结果与实际情况相符。
      结论  与常用的特尔菲法比较,随机森林算法稳定性更好,级别指数变幅区间更小,更有利于构建省级空间尺度的耕地级别可比序列。

       

      Abstract:
      Objective  To analyze the difference of cultivated land quality in the study region, optimize the use and layout of cultivated land, and provide a reference for cultivated land protection.
      Method  Taking the cultivated land in Gonghe County, Dulan County and Wulan County in Qinghai Province as the research object, the influencing factors of cultivated land quality were collected based on the history and existing literature, and the random forest algorithm and correlation analysis were used to screen the grading indicators and confirm the weight. We calculated the grading index and divided the levels by weighted sum method to get the grading result. We compared the results with the grading results of commonly used Delphi method.
      Result  The value of variable importance I obtained by random forest algorithm ranged from 0.03 to 11.94. Correlation analysis showed that the correlation between most influencing factors was not significant, eight of which were significant correlation. The 14 rating indicators under four dimensions were astringed from 30 influencing factors. The main factors influencing the quality of cultivated land in the study area were ecosystem vulnerability, mean precipitation of growing season and annual solar radiation amount, with the weights of 0.11, 0.10 and 0.09, respectively.
      Conclusion  Compared with Delphi method, the random forest algorithm has better stability and smaller level of index variation interval, which is more conducive to construct a comparable sequence of cultivated land levels at provincial spatial scale.

       

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