使用可解释的机器学习模型预测飓风疏散决策
Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Models
摘要：
气候变化的影响日益严重，飓风易发地区的人口不断增加，使大规模飓风疏散的挑战更加严峻。虽然飓风准备和响应策略在很大程度上依赖于预测家庭疏散决定的准确性和及时性，但目前的研究以心理驱动的线性模型为特征，在实践中存在一些重大局限性。因此，本研究提出了一种新的方法来预测家庭的疏散决定，容易获得的人口和资源相关的预测相比，目前的模型高度依赖于心理因素。与此同时，一种增强的逻辑回归（ELR）模型可以自动考虑非线性（即，单变量和双变量阈值效应），以确保结果的准确性。具体而言，选择低深度决策树进行非线性检测，以识别临界阈值，构建透明的模型结构，并固化鲁棒性。然后，一个经验数据集收集后，卡特里娜和丽塔飓风被雇用来检查新方法的实用性。结果表明，与以往的线性回归模型相比，增强型逻辑回归模型在模型拟合和预测能力上对居民疏散决策的变化具有最强的解释力。该方法为应急管理部门提供了一个新的工具和框架，以提高疏散交通需求的及时性和准确性。
英文摘要：
The aggravating effects of climate change and the growing population in hurricane-prone areas escalate the challenges in large-scale hurricane evacuations. While hurricane preparedness and response strategies vastly rely on the accuracy and timeliness of the predicted households' evacuation decisions, current studies featuring psychological-driven linear models leave some significant limitations in practice. Hence, the present study proposes a new methodology for predicting households' evacuation decisions constructed by easily accessible demographic and resource-related predictors compared to current models with a high reliance on psychological factors. Meanwhile, an enhanced logistic regression (ELR) model that could automatically account for nonlinearities (i.e., univariate and bivariate threshold effects) by an interpretable machine learning approach is developed to secure the accuracy of the results. Specifically, low-depth decision trees are selected for nonlinearity detection to identify the critical thresholds, build a transparent model structure, and solidify the robustness. Then, an empirical dataset collected after Hurricanes Katrina and Rita is hired to examine the practicability of the new methodology. The results indicate that the enhanced logistic regression (ELR) model has the most convincing performance in explaining the variation of the households' evacuation decision in model fit and prediction capability compared to previous linear models. It suggests that the proposed methodology could provide a new tool and framework for the emergency management authorities to improve the estimation of evacuation traffic demands in a timely and accurate manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
