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| 1 | 基于随机森林模型的泥石流易发性评价--以汶川地震重灾区为例显示文摘在区域泥石流易发性研究中,科学确定泥石流易发性主控因子及其贡献率既是关键科学问题,也是区域泥石流预警预报和风险管理的重要基础。本研究选取汶川地震重灾区,引入随机森林算法,以小流域为评价单元,集合多元因子指标体系,建立泥石流易发性评价模型,定量分析了汶川地震重灾区内泥石流关键影响因子及贡献率,并探讨了研究区泥石流易发性的空间分布特征。本文初选了63项评价指标,以模型AUC值变化为基础,筛选出35项指标构成易发性评价指标体系,并用于区域内泥石流易发性主控因子的识别,结果表明:流域高差、流域平均坡度、流域内滑坡面积、平均降雨天数是区域内泥石流易发性主控因子,另外,沟长比降、大于10°积温、年均温、人口密度、村落个数、低覆盖度土地利用方式等在泥石流易发性评价中也发挥着重要作用;易发性评价结果显示,极高易发区占比达到了22.94%,主要分布于研究区西部,泥石流易发性较高的小流域主要分布在青藏高原向四川盆地过渡的地形急变带,同时也与地震带、断裂带、干旱河谷区域密切相关。模型验证结果表明,平均AUC值达0.84,模型具有很高的稳定性和准确性,说明随机森林算法非常适用于区域泥石流易发性评价研究,机器学习算法结合小流域为单元的方法对区域泥石流易发性评价有效果良好,可为区域尺度灾害易发性及风险评估提供更为有效的方法参考。 | 刘永垚 第宝锋 詹宇 Constantine A.Stamatopoulos | 2018 | 山地学报2018，36，5： | 37 |
| 2 | 灾变山地环境影响下小流域脆弱性评价研究显示文摘流域脆弱性评价中,如何有效结合自然及社会等多方面因素开展较大区域尺度评价是目前研究的难点之一,特别是结合灾变山地环境影响,开展小流域脆弱性评价,对于山区流域防灾减灾及可持续发展具有积极的指导意义。采用栅格单元与小流域单元相结合的评价方法,综合自然与社会因素,并引入活跃灾害事件对小流域脆弱性的影响,从灾害危险度与社会易损性角度构建评价体系,应用3S技术,选取彭州龙门山区开展典型案例研究,开展灾变山地环境影响下的小流域脆弱性评价。最终将研究区小流域脆弱性划分为轻度、中度、高度及重度4个等级,各级含小流域个数分别为12、21、27及17个,相应面积占整个研究区面积的比例分别为11.53%,38.63%,40.46%及9.38%。针对区域内重大灾害事件,对评价结果进行验证表明,综合自然和社会因素,并引入灾害事件构建评价体系,并以栅格与小流域单元相结合的评价方法,对灾变山地环境下较大区域尺度流域脆弱性评价具有可行性,可为区域尺度小流域脆弱性评价研究提供一定参考。 | 张梦 第宝锋 Constantine A.Stamatopoulos 宋微曦 王雅潞 | 2015 | 长江流域资源与环境2015，24，6： | 4 |
| 3 | Classification and detection of dominant factors in geospatial patterns of traditional settlements in China显示文摘The geospatial distribution pattern in traditional Chinese settlements(TCSs)reflects the traditional harmony between humans and nature,which has been learned over centuries.However,TCSs have experienced serious disturbances by urbanization and migration.It is crucial to explore the local wisdom of geospatial patterns and dominant factors for TCSs at the national scale in China.This study sought to determine the geospatial wisdom of traditional settlements to enrich our future settlement development with the aim of establishing Chinese settlement values for modern living.Herein,a dataset of 4000 TCSs were analyzed and clustered for environmental factors that affect their geospatial patterns by machine learning algorithms.We concluded that(1)five geospatial patterns of TCSs were clustered on a national scale,and the threshold of environmental factors of TCS groups was detected.(2)Environmental conditions and settlement concepts interacted and determined the similarities and differences among TCS groups.(3)The key boundary for TCSs and the dominant factors for each zone were determined,and topographical conditions and hydrologic resources played significant roles in all five TCS zones.This study provides a better understanding of the adaptability of the environment in relation to the TCSs and aids in planning TCS conservation and rural revitalization. | WU Shaolin DI Baofeng Susan L.USTIN Constantine A.STAMATOPOULOS LI Jierui ZUO Qi WU Xiao AI Nanshan | 2022 | Journal of Geographical Sciences2022，32，5： | 0 |
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