基于遗传算法和云模型的公路沿线泥石流灾害危险性区划

 公路沿线泥石流灾害危险性区划可以明确不同地区灾害防治的轻重缓急和设防标准，为宏观政策的制定提供理论基础。从地形地貌、降水、岩土和植被4方面开展公路沿线泥石流灾害危险源辨识，根据潜在公路沿线泥石流灾害调查结果，采用遗传算法提取危险性评价指标，通过云模型改进AHP算法计算各指标权重，基于ArcGIS软件绘制各评价指标的基础图件并进行公路沿线泥石流灾害危险性空间分析，以公路沿线泥石流灾害危险度为主导依据，编制中国公路沿线泥石流灾害危险性区划方案。结果表明：中国公路沿线泥石流灾害危险度为1.000～7.900，危险度较高的区域为浙闽东南山区、台湾山区、昆仑山脉、天山山脉、太行山、黄土高原、横断山脉和青藏高原东部等；中国划分为低度危险、中度危险、重度危险和极重度危险4个等级、14个危险区，其中极重危险区为黄土高原-秦巴山区、武夷山-台湾山区、川滇山区和天山-昆仑山区。

Hazard regionalization of debris flow disasters along highways based on genetic algorithm and cloud model

Hazard regionalization of debris flow disasters along highways clarifies the priorities of disaster prevention and protection standards for different parts of China，and provides a theoretical basis for macro policy formulation. Hazard sources of debris flow disasters along highways were identified from four aspects of the topography and landscape，precipitation，rock and soil materials，and vegetation. The hazard assessment indexes were extracted using the genetic algorithm according to the survey results of the potential debris flow disasters along highways. The weight of each index was calculated through the AHP algorithm improved by the cloud model. The assessment index maps were rendered and the spatial hazard analysis of debris flow disasters along highways was made with ArcGIS. Hazard regionalization scheme of debris flow disasters along highways in China was worked out mainly based on the hazardous degree. The hazardous degree of debris flow disasters along highways in China is from 1.000 to 7.900. High hazardous areas are the mountainous areas in the southeast of Zhejiang and Fujian province，the mountain areas of Taiwan，Kunlun Mountains，Tianshan Mountains，Taihang Mountains，Loess Plateau，Hengduan Mountains and eastern Tibetan Plateau. China is divided into regions of low hazard，moderate hazard，severe hazard and extremely severe hazard. The extremely severe hazard areas are the Loess Plateau-Qinba mountain areas，Wuyishan-Taiwan mountain areas，Sichuan-Yunnan mountain areas and Tianshan-Kunlun mountain areas.