基于随机森林的大坝潜在风险预测方法

大坝潜在风险预测在降低大坝溃坝概率、减少水库大坝失事事件方面发挥着重要的作用。本文基于随机森林方法构建基于数据驱动的大坝潜在风险预测模型，减少在建模过程中的人工干预，实现风险预测高效化、智能化。首先预处理大坝基础数据资料，构建训练数据集和测试数据集，然后构建大坝风险预测模型并利用训练数据训练模型，利用GridSearch和Cross-validation确定模型最优情况的参数，并通模型评价指标和多种算法对比结果全面评估模型性能。实验结果表明：基于随机森林的风险预测模型在测试数据上的准确度率为90.54%，模型准确度相较于ANN、KNN、SVM算法高出4.87%、18.59%、37.93%，满足实际应用的需求。

Prediction method of dam potential risk based on random forest

Dam potential risk prediction plays an important role in reducing dam failure probability and reservoir dam failure events. In this paper, a data-driven reservoir dam potential risk prediction model is constructed to reduce manual intervention and realize efficient and intelligent safety assessment based on the random forest algorithm. Firstly, the basic data of reservoir dams are preprocessed to construct training datasets and test datasets. Then, the reservoir dam infrastructure safety assessment model is constructed and the training data is used to train the model. GridSearch and Cross-validation are used to determine the parameters of the optimal model. And comprehensively evaluate model performance through model evaluation indicators and comparison results of multiple algorithms. The experimental results show that the accuracy rate of the random forest-based security assessment model on the test data is 90.54%, and the model accuracy is 4.87%, 18.59%, and 37.93% higher than that of ANN, KNN, and SVM algorithms. The model meets the needs of practical applications.