• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Bulletin of engineering geology and the environment >Sinkhole susceptibility mapping using logistic regression in KarapA +/- nar (Konya, Turkey)
【24h】

Sinkhole susceptibility mapping using logistic regression in KarapA +/- nar (Konya, Turkey)

机译：在KarapA +/- nar（土耳其科尼亚）中使用逻辑回归进行污水坑敏感性分析

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Globally, sinkholes cause hundreds of millions of dollars in damage and hundreds of deaths or injuries each year. To mitigate the damage caused by sinkholes, it is necessary to determine the susceptible or hazardous areas. The purpose of this study is to produce a sinkhole susceptibility map based on a logistic regression method within a geographic information system environment. A field survey for this investigation identified the locations of 182 sinkholes in the study area. Many geologic, geomorphologic, hydrogeological and anthropogenic factors that influence sinkhole development were identified in the KarapA +/- nar Region. In this study, 30 sinkhole-influencing factors were selected and used in the analysis. The coefficients of the predictor variables were estimated using binary logistic regression analysis and were used to calculate the sinkhole susceptibility for the entire study area. The area value of the receiver operating characteristics curve model was 0.814. The final map indicates that most of the observed sinkholes are predicted in the high or very high sinkhole susceptibility classes. These results indicate that this model is a good estimator of sinkhole susceptibility in the study area. The sinkhole susceptibility map shows that areas with no or very low, low, moderate, high and very high sinkhole susceptibility classes are 605 km(2) (25.6 %), 310.8 km(2) (13.1 %), 531.2 km(2) (22.5 %), 487.7 km(2) (20.6 %), and 429.0 km(2) (18.1 %), respectively. Interpretation of the susceptibility map shows that sinkhole formation decreased with increasing slope angle, cover thickness, electrical conductivity, and the concentration of calcium, magnesium, sodium, and potassium ions in groundwater. However sinkhole formation increased with drainage density, fault density, upper levels of karstic formations, decline in groundwater level, and well density. This map will serve to help citizens, urban planners and design engineers prevent damage caused by existing sinkholes as well as sinkholes that develop in the future.
机译：在全球范围内，下水道每年造成数亿美元的损失，并造成数百人死亡或受伤。为了减轻下水道造成的损害，有必要确定易受影响或危险的区域。这项研究的目的是基于地理信息系统环境中的逻辑回归方法，制作一个下沉敏感性图。这项调查的现场调查确定了研究区域中182个污水坑的位置。在KarapA +/- nar地区发现了许多影响下沉坑发育的地质，地貌，水文地质和人为因素。在这项研究中，选择了30个影响下沉的因素，并将其用于分析。预测变量的系数使用二元逻辑回归分析进行估算，并用于计算整个研究区域的下沉敏感性。接收器工作特性曲线模型的面积值为0.814。最终图表明，大多数观测到的沉陷都是在高或非常高的沉陷敏感性等级中预测的。这些结果表明，该模型是研究区域中塌陷敏感性的良好估计。塌陷敏感性图显示，没有或非常低，低，中等，高和非常高的塌陷敏感性等级的区域分别为605 km（2）（25.6％），310.8 km（2）（13.1％），531.2 km（2） （22.5％），487.7 km（2）（20.6％）和429.0 km（2）（18.1％）。磁化率图的解释表明，随着倾斜角，覆盖层厚度，电导率以及地下水中钙，镁，钠和钾离子浓度的增加，下沉坑的形成减少。但是，随着排水密度，断层密度，岩溶岩层的较高水平，地下水位的下降和井密度的增加，下陷的形成会增加。该地图将帮助市民，城市规划人员和设计工程师防止现有的污水坑以及将来出现的污水坑造成的破坏。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号