基于强化学习的海南岛山洪灾害易发性评估

采用多重共线性分析和极致梯度提升模型确定海南岛山洪灾害影响因子，提出了基于SAC强化学习算法的海南岛山洪灾害易发性评估模型。采用敏感性、特异性等指标和受试者工作特征曲线，将强化学习模型评估结果与卷积神经网络和深度神经网络模型评估结果进行对比。结果表明：强化学习模型具有较高的准确性和可靠性，测试数据集AUC指标达到0.931,高于卷积神经网络和深度神经网络模型的评估结果，可为海南岛山洪灾害风险评估提供科学依据。

Evaluation of mountain torrent disaster vulnerability in Hainan Island based on reinforcement learning

The multi-collinearity analysis and the extreme gradient lifting model were used to determine the impact factors of mountain torrent disaster vulnerability of Hainan Island, and a reinforcement learning (RL) evaluation model of mountain torrent disasters was proposed based on the SAC (soft actor-critic) RL algorithm. The RL model results were compared with those of the convolution neural network (CNN) and deep neural network(DNN) models based on the sensitivity and specificity, as well as the receiver operating characteristic curve. Results show that the RL model has higher accuracy and reliability, and its area under curve (AUC) index of the test data set reaches 0.931, which is higher than those of the CNN and DNN models. The RL model can provide scientific basis for mountain torrent disaster risk assessment in Hainan Island.