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The similarity analysis of heavy rainfall and flood processes refers to the prediction of the future development trends of current heavy rainfall and floods by mining historical rainfall and flood data, thereby supporting current flood management operations. Existing similarity analysis models primarily employ methods such as fuzzy mathematics, data mining, and machine learning to extract and analyze feature information, including the center of rainfall, rainfall amounts, and rainfall time intervals from numerical rainfall data. These models can effectively provide auxiliary decision-making support for flood risk forecasting. However, with the advancement of information technology in data collection, the volume of numerical rainfall data has experienced explosive growth in both temporal and spatial scales, leading to decreased efficiency in the data mining process and making the search for similar rainfall processes increasingly difficult. Rainfall patterns, as a form of unstructured rainfall data, can represent the rainfall amounts in specific areas through various color blocks. The information of rainfall patterns plays a crucial role in analyzing similar rainfall events. To fully leverage the advantages of rainfall pattern data, this paper proposes the rainfall pattern similarity analysis algorithm based on image processing and similarity matching techniques (RPSA-IPSM), providing new methods for identifying similar rainfall and flood processes. Specifically, the RPSA-IPSM algorithm processes the input prototype rainfall pattern data by first performing color segmentation to obtain different color blocks. Subsequently, the pixel values of each color block are reassigned, with larger pixel values being assigned to blocks that represent higher rainfall amounts. The same operations are applied to each rainfall pattern in the historical rainfall database. At the same time, the mean squared error is utilized to calculate the similarity between the prototype rainfall pattern and the historical rainfall patterns. Experimental results indicate that the RPSA-IPSM method can effectively retrieve similar rainfall events.

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Rainfall Pattern Similarity Analysis Based on Image Processing and Similarity Matching Techniques

  • 会议论文

  • 发表日期：

    2025年

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  • Xindai An,
  • Di Wu,
  • Hongyu Hou,
  • Rongrong Li,
  • Liupeng He,
  • Chengzhen Jiang,
  • Shuning Wu

摘要

The similarity analysis of heavy rainfall and flood processes refers to the prediction of the future development trends of current heavy rainfall and floods by mining historical rainfall and flood data, thereby supporting current flood management operations. Existing similarity analysis models primarily employ methods such as fuzzy mathematics, data mining, and machine learning to extract and analyze feature information, including the center of rainfall, rainfall amounts, and rainfall time intervals from numerical rainfall data. These models can effectively provide auxiliary decision-making support for flood risk forecasting. However, with the advancement of information technology in data collection, the volume of numerical rainfall data has experienced explosive growth in both temporal and spatial scales, leading to decreased efficiency in the data mining process and making the search for similar rainfall processes increasingly difficult. Rainfall patterns, as a form of unstructured rainfall data, can represent the rainfall amounts in specific areas through various color blocks. The information of rainfall patterns plays a crucial role in analyzing similar rainfall events. To fully leverage the advantages of rainfall pattern data, this paper proposes the rainfall pattern similarity analysis algorithm based on image processing and similarity matching techniques (RPSA-IPSM), providing new methods for identifying similar rainfall and flood processes. Specifically, the RPSA-IPSM algorithm processes the input prototype rainfall pattern data by first performing color segmentation to obtain different color blocks. Subsequently, the pixel values of each color block are reassigned, with larger pixel values being assigned to blocks that represent higher rainfall amounts. The same operations are applied to each rainfall pattern in the historical rainfall database. At the same time, the mean squared error is utilized to calculate the similarity between the prototype rainfall pattern and the historical rainfall patterns. Experimental results indicate that the RPSA-IPSM method can effectively retrieve similar rainfall events.