Lightgbm and hydrological and hydrodynamic model based rapid flood prediction method for coastal cities

By combining LightGBM and hydrodynamic models, optimizing hyperparameters, and constructing a flood prediction model, the problems of long computation time and high data requirements in existing technologies are solved, enabling rapid and accurate prediction of urban floods.

CN116090625BActive Publication Date: 2026-05-05ZHENGZHOU UNIV
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Patent Information

Application Number
CN202310000088.6
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2023-01-01
Publication Date
2026-05-05
Estimated Expiration
2043-01-01

AI Technical Summary

Technical Problem

Existing hydrological and hydrodynamic models are computationally time-consuming and data-intensive in urban flood prediction, making it difficult to meet real-time prediction needs. Data-driven models are limited in application in areas lacking real-world data, and existing combined methods consume a lot of memory and have slow training speeds.

Method used

A method combining LightGBM and hydrodynamic models was adopted. A flood simulation model was constructed using PCSWMM, feature variables were extracted and LightGBM model was trained, and hyperparameters were optimized using K-fold cross-validation and grid search to achieve rapid prediction of urban floods.

Benefits of technology

It achieves accurate and rapid flood forecasting over large spatial areas or high-resolution regions, reduces memory consumption and improves training speed, and is suitable for real-time flood forecasting.

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Abstract

The application discloses a coastal city flood rapid prediction method based on LightGBM and a hydrological and hydrodynamic model, and comprises the following steps: 1, obtaining sample data, including: designing multiple rainfall-tide level combination scenarios; constructing a flood simulation model of a target region based on a PCSWMM hydrological and hydrodynamic model; simulating the maximum water depth of each selected flood point under different rainfall-tide level combination scenarios by using the flood simulation model; extracting rainfall-related characteristic variables and tide-related characteristic variables, and taking the flood point position and the maximum water depth as sample data; 2, constructing and training a city flood rapid prediction model based on LightGBM; 3, predicting the maximum water depth of a submerged point in city flood by using the trained city flood prediction model. The application has excellent performance and calculation efficiency, and can realize accurate and rapid prediction of city flood. The application can also realize efficient and accurate prediction of a city region with a large space range or high spatial resolution.
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