Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the...Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors(LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index(TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production(GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve(AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps(LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.展开更多
运用预设预测规则的遗传算法(GARP)和最大熵(Max Ent)两种生态位预测模型,以及受试者工作曲线(ROC)分析方法,预测石蒜属石蒜潜在适生区。结果表明,GARP和Max Ent模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0....运用预设预测规则的遗传算法(GARP)和最大熵(Max Ent)两种生态位预测模型,以及受试者工作曲线(ROC)分析方法,预测石蒜属石蒜潜在适生区。结果表明,GARP和Max Ent模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.988,Max Ent模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于石蒜的适生区预测。对环境变量进行刀切法表明,在所有环境变量中,最冷季度平均温度对于石蒜分布的影响(贡献)最大,其次是年均温、最冷月的最低温度和最暖季度降水量,而海拔、降水量变化方差对石蒜分布的影响比较小。预测结果显示,石蒜在世界范围内主要分布在亚洲东部以及亚洲中部一小部分,另外北美洲的东部,欧洲南部一小部分地区也适合其生长。在中国范围内主要分布在云南、贵州、福建、江苏、浙江、安徽、江西、重庆、湖北、湖南等省,以及山东、河南、陕西、甘肃等省南部;四川东部和广东、广西(除了南部沿海地区)均预测为适生区,海南、台湾、西藏部分地区也是适生区。展开更多
薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘...薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘菊在中国的适生区进行预测,并运用受试者工作曲线(receiver operating characteristic,ROC)分析方法对2种模型的预测结果进行分析,选出最优模型进行预测,同时对环境变量进行刀切法分析,判断环境变量对薇甘菊分布的影响。结果表明,GARP和MaxEnt模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.971,MaxEnt模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于薇甘菊的适生区预测;对环境变量进行刀切法表明,海拔和季节性降水量方差对薇甘菊的分布影响最小,年温变化范围、年降水量、最湿月份降水量、最湿季度降水量、温度变化方差这5个环境变量对薇甘菊适生区预测影响最大;预测结果显示薇甘菊在中国大陆的适生区主要集中在海南、广东、广西、香港、澳门、云南、福建、西藏、贵州等省,其中西藏东南部和西南部、贵州西南部、福建中南部等地区应该加强监测及预警。展开更多
基金Science and Research Branch, Islamic Azad University
文摘Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors(LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index(TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production(GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve(AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps(LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.
文摘运用预设预测规则的遗传算法(GARP)和最大熵(Max Ent)两种生态位预测模型,以及受试者工作曲线(ROC)分析方法,预测石蒜属石蒜潜在适生区。结果表明,GARP和Max Ent模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.988,Max Ent模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于石蒜的适生区预测。对环境变量进行刀切法表明,在所有环境变量中,最冷季度平均温度对于石蒜分布的影响(贡献)最大,其次是年均温、最冷月的最低温度和最暖季度降水量,而海拔、降水量变化方差对石蒜分布的影响比较小。预测结果显示,石蒜在世界范围内主要分布在亚洲东部以及亚洲中部一小部分,另外北美洲的东部,欧洲南部一小部分地区也适合其生长。在中国范围内主要分布在云南、贵州、福建、江苏、浙江、安徽、江西、重庆、湖北、湖南等省,以及山东、河南、陕西、甘肃等省南部;四川东部和广东、广西(除了南部沿海地区)均预测为适生区,海南、台湾、西藏部分地区也是适生区。
文摘薇甘菊(Mikania micrantha H B K.)是一种危害极大的外来入侵农林杂草。为了预测薇甘菊在中国的适生区,该文运用预设预测规则的遗传算法(genetic algorithm for rule-set production,GARP)和最大熵(Maximum Entropy,MaxEnt)模型对薇甘菊在中国的适生区进行预测,并运用受试者工作曲线(receiver operating characteristic,ROC)分析方法对2种模型的预测结果进行分析,选出最优模型进行预测,同时对环境变量进行刀切法分析,判断环境变量对薇甘菊分布的影响。结果表明,GARP和MaxEnt模型ROC曲线下面的面积AUC(area under the ROC curve)均值分别为0.910和0.971,MaxEnt模型的AUC值更大,预测结果更准确,运行速度更快,更适合用于薇甘菊的适生区预测;对环境变量进行刀切法表明,海拔和季节性降水量方差对薇甘菊的分布影响最小,年温变化范围、年降水量、最湿月份降水量、最湿季度降水量、温度变化方差这5个环境变量对薇甘菊适生区预测影响最大;预测结果显示薇甘菊在中国大陆的适生区主要集中在海南、广东、广西、香港、澳门、云南、福建、西藏、贵州等省,其中西藏东南部和西南部、贵州西南部、福建中南部等地区应该加强监测及预警。
