Stacking Integration Algorithm-Based Water Damage Prediction in Riprap Revetments
Riprap revetments are prone to water-induced deterioration during severe circumstances,hence presenting risks to both human life and property.The present work employs flume experiments in order to gather a dataset consis-ting of 496 samples.In this study,the selection of six essential feature attributes is performed using mutual information(MI).Subsequently,several machine learning methods,including support vector regression(SVR),generalized regres-sion neural network(GRNN),and random forest(RF),are utilized to construct several prediction models.The afore-mentioned models perform as foundational learners,with a back-propagation neural network(BPNN)operating as a me-ta-learner.The construction of a prediction model for assessing the degree of damage to riprap revetments is achieved u-sing the Stacking ensemble learning approach.The evaluation of model performance involves the utilization of metrics,such as the coefficient of determination(R2),root mean square error(RRMSE),and mean absolute error(MMAE).The findings indicate that the Stacking model produces an average R2 value of 0.98,root mean square error(RRMSE)of 0.02,and mean absolute error(MMAE)of 0.03 when predicting the height,length,and range of revetment damage.When comparing the performance of the Stacking model to individual models such as support vector regression(SVR),General-ized regression neural network(GRNN),and random forest(RF),it is observed that the Stacking model achieves the lowest root mean squared error(RRMSE)and mean absolute error(MMAE),while also achieving the greatest r-squared(R2)value.The Stacking model,when integrated,demonstrates enhanced precision and consistency in forecasting the magnitude of water-induced deterioration in riprap revetments.
riprap revetmentswater damageStacking integrated algorithmprediction research
NETL
NSTL
万方数据
维普
