Short-term forecasting method for high-tide flood in coastal area based on probability statistics

By constructing a short-term forecasting model for high tide floods in coastal areas using probabilistic statistical methods, this approach solves the problems of computational complexity and high cost in existing technologies, achieving high-precision short-term flood forecasting and possessing openness and forecasting skills.

CN122175103APending Publication Date: 2026-06-09NAT MARINE DATA & INFORMATION SERVICE
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Patent Information

Application Number
CN202610644935.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-12
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

In the prediction of high tide floods in coastal areas, existing technologies rely on numerical models based on ocean dynamics, which are computationally complex and costly, and are difficult to quantify the uncertainty of non-tidal factors. Statistical models based on historical data lack short-term flood prediction capabilities and cannot meet the requirements of high accuracy and timeliness.

Method used

Using a probabilistic statistical approach, historical hourly water level observation data from coastal tide gauge stations were obtained, preprocessed, and a climatological probability distribution model was constructed. Combined with a decay persistence prediction model, the daily cumulative flood probability was calculated, and a warning probability threshold was set to mark high-risk days for high tide floods.

Benefits of technology

It enables rapid application to any coastal station with sufficient observation time, providing high-precision short-term forecasts of high tide floods. It is open and can access advanced sea level anomaly numerical, statistical, or machine learning forecast results to improve forecasting skills.

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Abstract

The invention discloses a coastal area high-tide flood short-term forecasting method based on probability statistics. Comprising the steps that historical hourly water level observation data of a target coastal area tide level station are acquired and preprocessed; constructing a climate state probability distribution model to obtain corresponding climate state distribution parameters; constructing an attenuation continuity prediction model, and calculating an observation abnormal value of a future month according to the observation abnormal value of the current month; the climate state distribution parameters are adjusted through the observation abnormal values of the future month; calculating a daily accumulated flood probability based on the adjusted climate state distribution parameters; and calculating the daily accumulated flood probability day by day to obtain a high-tide flood probability sequence of the coastal city in several months in the future, setting an early warning probability threshold, and marking the date higher than the early warning probability threshold in the high-tide flood probability sequence as a high-tide flood high-risk date. The high-tide flood prediction better meets the requirement of disaster risk assessment, the information amount is larger, seasonal-scale prediction can be provided, and time is reserved for medium and long-term planning and emergency preparation.
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