Comprehensive assessment of flood risk using the classification and regression tree method

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• 作者：Zhonghui Ji ; Ning Li ; Wei Xie ; Jidong Wu…
• 关键词：Flood ; Risk evaluation ; CART ; Meteorological conditions ; RDLS ; Social vulnerability
• 刊名：Stochastic Environmental Research and Risk Assessment (SERRA)
• 出版年：2013
• 出版时间：December 2013
• 年：2013
• 卷：27
• 期：8
• 页码：1815-1828
• 全文大小：629KB
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• 作者单位：Zhonghui Ji (1) (2) (3)
  Ning Li (1) (2) (3)
  Wei Xie (1) (2) (3)
  Jidong Wu (1) (2) (3)
  Yang Zhou (1) (2) (3)
  
  1. State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing, 100875, China
  2. Key Laboratory of Environmental Change and Natural Disaster, Ministry of Education of China, Beijing Normal University, Beijing, 100875, China
  3. Academy of Disaster Reduction and Emergency Management, Ministry of Civil Affairs & Ministry of Education, Beijing, 100875, China
  
• ISSN：1436-3259

文摘

The Evaluation of flood risk is a difficult task due to its numerous and complex impact factors. This article built a classification and regression tree (CART) model for the flood risk assessment with the available data of Hunan Province. This model is able to extract the major impact factors from many complex variables, determine the factors-thresholds, and evaluate the levels of flood risk objectively. To construct the model, 18 explanatory variables were selected as the influential factors, including meteorological conditions, surface conditions and social vulnerability. Economic loss density from flood was chosen as the response variable for the quantitative and comprehensive evaluation of flood risk. The final model showed that meteorological conditions have the most significant influence on flood risk. Additionally, the relationship between meteorological factors and flood risk is rather complex. The variability of rainstorm days during the seasonal alternate period from the end of spring (May) to the early summer (June) is the source of the highest flood risk. In addition, the regional embankment density and population density as social vulnerability indicators and the relief degree of land surface as a surface condition indicator were also included in the flood risk assessment for Hunan. A region with dense dams appeared at a relatively higher risk. Densely inhabited areas with greater topographical relief also demonstrated a higher flood risk in the study area. The conditions obtained from the final tree for different levels of risk demonstrate the objectivity of selecting impact factors and a reduction of complexity for the risk evaluation process. Furthermore, the evaluation of high-level risk using the proposed method requires fewer conditions, which allows for a rapid risk assessment of serious floods. The CART method shows a decreased root mean squared error compared with that of a multiple linear regression model. In addition, the cross-validation error was improved for the high-risk levels that represent the most important classes in risk management. The verification with the available historical records showed that the output of the model is reliable. In summary, the CART method is feasible for extracting the main impact factors and their associated thresholds for the comprehensive assessment of regional flood risk.