随机森林算法在农村居民点适宜性评价中的应用

doi:10.18402/resci.2018.10.16

摘要/Abstract

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

徐枫, 王占岐, 张红伟, 柴季. 随机森林算法在农村居民点适宜性评价中的应用[J]. 资源科学, 2018, 40(10): 2085-2098.

Feng XU, Zhanqi WANG, Hongwei ZHANG, Ji CHAI. Application of random forest algorithm in suitability evaluation of rural residential land[J]. Resources Science, 2018, 40(10): 2085-2098.

图/表 6

图1

表1

居民点利用适宜性评价影响因素与特征"

准则层	指标层因子（变量）	描述/单位	数据来源
土地区位条件	距最近道路距离（Dis_Rod）	地块中心到邻近道路网络的最小图上距离/m	房县2016年土地利用现状数据^[38],遥感数据^[40]解译补充,空间分析处理后采集
	距最近居民点距离（Dis_Rld）	地块中心到邻近居民点中心的最小图上距离/m	房县2016年土地利用现状数据^[38]空间分析处理后采集
	距最近耕地距离（Dis_Cld）	地块中心到邻近耕地地块中心的最小图上距离/m	同上
	距乡镇中心距离（Dis_Tct）	地块中心到所属乡镇政府大院中心的图上距离/m	同上
	地块面积（Lnd_Are）	地块图上面积/m²	同上
农业生产条件	灌溉保证率（Pro_Irr）	距地块最近耕地的全年灌溉率/无灌溉条件=1,一般满足=2,基本满足=3,充分满足=4	房县2016年耕地质量评价数据^[41]空间分析处理后采集
	土壤pH值（Ph_Vlu）	土壤酸碱度	同上
	土壤有机质含量（Org_Mat）	每千克土壤中所有有机质重量和/g	同上
	表层土壤质地（Sur_Soi）	土壤表面成分的物理特性/砾质土=1,砂土=2,黏土=3,壤土=4	同上
	亩产能力（Ann_Yld）	年均粮食亩产量/kg	2016房县统计年鉴^[42]数据二次计算采集
发展与生活条件	城镇化率（Urb_Rat）	非农村居民人数占总人口比例	同上
	播种面积（Swn_Are）	人均农作物播种土地面积/hm²	同上
	劳务收入（Lab_Inc）	人均劳务性收入/元	同上
	政策性补贴（Plc_Sub）	人均政策性补贴/元	同上
	用电量（Ele_Cns）	人均用电量/kW·h	同上
	牲畜存栏量（Liv_Slg）	人均主要牲畜（包括耕牛、牲猪、家禽等）年末存栏量/头	同上
地理条件	坡度（Lnd_Slp）	地块中心的坡度值/度	SRTM数字高程数据^[43]采集
	海拔（Lnd_Elv）	地块中心的海拔高度/m	同上

表1

图2

图3

图4

图5

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