Coastal erosion risk multi-dimensional evaluation method based on machine learning

By employing a machine learning-based multidimensional coastal erosion risk assessment method, which utilizes grid cell division and multidimensional feature samples to train the model, the problems of single evaluation indicators and insufficient representativeness of monitoring data in existing technologies are solved, thus achieving higher accuracy in coastal erosion risk identification and early warning.

CN121765480APending Publication Date: 2026-03-31JIANGSU PROVINCIAL TIDE RES CENT (JIANGSU PROVINCIAL MARINE ENVIRONMENT MONITORING & FORECASTING CENT)
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Patent Information

Application Number
CN202610242005.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-02-28
Publication Date
2026-03-31

AI Technical Summary

Technical Problem

Existing methods for assessing coastal erosion risk suffer from limitations such as a single evaluation index, neglect of multi-dimensional coupling effects, insufficient spatial representativeness of monitoring data, low level of assessment refinement, and inadequate evaluation of model accuracy, making it difficult to accurately identify coastal erosion risks.

Method used

A machine learning-based approach was adopted to divide the target coastal area into multiple grid cells, construct multi-dimensional erosion feature samples, and train and test the models using support vector machine, random forest and Xgboost. The accuracy of the models was evaluated by combining indicators such as mean absolute error, weighted Kappa coefficient and high-risk false negative rate, and the best model was selected to predict the coastal erosion level.

Benefits of technology

It improves the accuracy and precision of coastal erosion risk assessment, enabling more accurate identification of coastal erosion levels, providing early warnings, reducing disaster impacts, and providing technical support for risk management and engineering restoration in coastal areas.

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Abstract

The invention provides a coast erosion risk multi-dimensional evaluation method based on machine learning. According to the method, a multi-dimensional and comprehensive evaluation index is adopted as an erosion factor, a coast erosion grade is adopted as a grading label to evaluate the severity of coast erosion, and a nonlinear mapping relation between the erosion factor and the coast erosion grade is established by utilizing machine learning; the coupling influence of interaction between wave energy and human activity factors on coast erosion is fully considered, and the accuracy and the refinement degree of risk judgment are effectively improved. The method further provides a comprehensive model precision evaluation method based on three indexes of mean absolute error, weighted Kappa coefficient and high-risk missing report rate, the optimal machine learning model can be screened out to be suitable for the current scene, and the accuracy of the model is further improved.
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