资源科学 ›› 2018, Vol. 40 ›› Issue (10): 2085-2098.doi: 10.18402/resci.2018.10.16

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随机森林算法在农村居民点适宜性评价中的应用

徐枫(), 王占岐(), 张红伟, 柴季   

  1. 中国地质大学(武汉)公共管理学院,武汉430074
  • 收稿日期:2018-04-27 修回日期:2018-08-28 出版日期:2018-10-25 发布日期:2018-10-20
  • 作者简介:

    作者简介:徐枫,男,湖北武汉人,博士生,主要从事土地经济与土地利用规划研究。E-mail:whcugxf@163.com

  • 基金资助:
    国家自然科学基金项目(71673258)

Application of random forest algorithm in suitability evaluation of rural residential land

Feng XU(), Zhanqi WANG(), Hongwei ZHANG, Ji CHAI   

  1. School of Public Administration, China University of Geosciences, Wuhan 430074, China
  • Received:2018-04-27 Revised:2018-08-28 Online:2018-10-25 Published:2018-10-20

摘要:

农村居民点适宜性评价是优化农村土地利用布局的基础性工作之一,解决该研究领域存在的问题,提高评价结果的科学合理性,使之更好地服务于农村土地资源的转型、优化利用,事关乡村振兴战略的实施。本文引入机器学习方法,通过对小规模已知样本的学习实现大规模未知数据的高精度分类与评价,从方法可行性、数模结合、优化策略和预测实现等方面进行探讨,应用随机森林算法对湖北省房县农村居民点利用的适宜性问题展开实证研究。结果显示:① 房县农村居民点适宜性受其可通达能力、海拔高度、地块面积及当地农业生产活跃程度的影响较大,也与农民生活、收入水平紧密相关;② 训练模型的性能受特征(评价因子)规模变化的影响较小,其中,16维特征集合模型的测试精度可达83.54%;③ 不同训练模型的预测结果均反映出房县的非适宜型居民点规模较大,该算法的鲁棒性强,评价结果稳定可靠。研究表明,机器学习方法能较好地支撑农村居民点适宜性评价工作,评价结果能为优化农村土地利用和基层治理提供数据基础。

关键词: 机器学习方法, 随机森林算法, 适宜性评价, 农村居民点, 分类和预测, 房县

Abstract:

Suitability evaluation of rural residential land is a foundation for optimizing the layout of rural land use. To solve the problems existed in this research area and promote the rationality of evaluation results, it is essential to better serve the transition and optimization of rural land use and benefit to implementation of the strategy for the promotion of rural development. Machine learning technique was introduced in this study to achieve a higher precision classification of large-scale unknown data by learning small-scale known samples. The feasibility of the method, combination of data and model, optimization strategies, and prediction implementation were explored in an empirical study on suitability of rural residential land use in in Fang County, Hubei Province by using random forest algorithm. The results indicated that: (1) The suitability of rural residential land in Fang County was mainly influenced by the accessibility, altitude, area of land, and the local agricultural production. Also, Villagers' living and income levels were closely related to the rural residence. (2) The performance of models was less affected by changes in the size of the feature sets (evaluation factors), in which a model with a 16-dimensional-feature set reached test accuracy at as high as 83.54%. and (3) The prediction results of different models demonstrated that the potential of unsuitable residential land locally was extremely large, the algorithm was robust and the results were stable and reliable. This research illustrated that the machine learning technique could better support the suitability evaluation of rural resident land. The evaluation results could provide data foundation for land use optimization and grassroots governance in rural areas.

Key words: machine learning technique, random forest algorithm, suitability evaluation, rural residential land, classification and prediction, Fang County