A Case Similarity Calculation Model Based on the Urban Flooding Case with Stratified Data Characteristics
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Xiaoyu Zhu
,
Yuxiang Fan
Abstract
As the pace of urbanization is accelerating, increasing amount of floodplain has been projected as the future cities. Subsequently, urban flooding is being studied by global emergency management exports due to its increasingly significant impact on us. Some existing research on flooding emergency management based on the case-based reasoning (CBR) method have made tremendous progress, but the urban flooding case with its stratified data characteristics is required a new methodology which is different from the ones applied to flash floods. So, based on the case-based reasoning (CBR) method, this paper proposed a CPIE-CBR model with four layers, classification filtration, punctiform similarity, interval similarity and entropy weight method, to calculate the case similarity among the urban flooding case with stratified data characteristics. Then we carry out the numerical simulation with the real data about China and conduct some comparison with original ways so that we observe the validity and efficiency of our model in the end.
Supported by Beijing Natural Science Foundation (9162003)
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Articles in the same Issue
- Return and Volatility Spillovers Effects: Study of Asian Emerging Stock Markets
- Predicting Trend of High Frequency CSI 300 Index Using Adaptive Input Selection and Machine Learning Techniques
- A Case Similarity Calculation Model Based on the Urban Flooding Case with Stratified Data Characteristics
- Policy Resistance Case Study Focused on Government’s Intervention in the Conflict Between Big-Box Stores and Traditional Market in Korea Based on Systems Thinking Approach
- Moving Average Model with an Alternative GARCH-Type Error
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