An APPSO–SVM approach building the monitoring model of dam safety

Data-based dam monitoring model can predict behavior and provide scientific basis for risk assessment decision-making of engineering. Traditional safety models established by multiple regression, stepwise gray theory, other statistics-based methods have poor robustness generalization. For this reason, an optimized support-vector machine (SVM) whose novelty lies in simple implementation, self-adaptive hyperparameter selection with aid adaptive position particle swarm optimization (APPSO), retention influence factor combination is presented constructing the model. A real-world concrete gravity engineering used to verify approach, results indicate built APPSO–SVM outperforms benchmark terms fitting prediction accuracy.