基于机器学习的美国选定城市热指数预测
Machine Learning-Based Prediction of Heat Index in Selected U.S. Cities
AI总结：
本文利用随机森林和GRU模型预测美国四城市次日热指数，结果显示模型在多数地点准确率超过80%，GRU模型在三个地点准确率达95%以上，性能差异可能源于不同地区热浪成因差异。
摘要：
热应力对美国社区产生有害影响，尤其当高温伴随高湿度时更为显著。热指数（HI）综合了温度和湿度的影响。当前最先进的数值天气预测模型在24小时内预测温度和湿度时往往存在偏差。本研究探讨了机器学习（ML）模型在四个美国地点准确预测次日热指数的能力，使用随机森林和单层门控循环单元（GRU）模型。研究发现，随机森林和GRU模型在所有四个地点表现良好。平均绝对HI误差范围为4.5至6.6华氏度。所有模型版本在三个地点中，成功预测极端热日的准确率超过80%，这由高下午HI值指示。GRU模型在这些三个地点的准确率超过95%。模型性能细节因地点而异。在明尼阿波利斯和波特兰，高HI值天数较少，模型准确性高，但召回率和精确率通常很低。相反，达拉斯，高HI天数较多，显示出中等高准确性，同时具有极高的召回率和精确率。这些差异可能源于美国不同气候区热浪的不同成因，如随机森林模型输出的特征重要性分数所示。本文设计的ML模型可用于辅助本地热指数预测和极端热预警，计算成本极低。
英文摘要：
Heat stress has harmful effects that impact communities across the Unitedt States, particularly when high temperatures are accompanied by high humidity. The combined impact of temperature and humidity can be summarized by the heat index (HI). Current state-of-the-art numerical weather prediction models are often biased when forecasting temperature and humidity even within a 24-hour forecast lead time. This study explores the ability of machine learning (ML) models to accurately predict the next-day heat index using Random Forest and single-layer Gated Recurrent Unit (GRU) models in four locations across the United States. We find that Random Forest and GRU models perform reasonably well at all four selected locations. Mean absolute HI error ranges from 4.5 to 6.6 °F. All model versions have an accuracy rate exceeding 80% in three of the four locations in terms of successfully forecasting an extreme heat day, as indicated by a high afternoon HI. The GRU model achieves over 95% accuracy in these three locations. Model performance details vary by location. In Minneapolis and Portland, which have relatively few days with high HI values, models' accuracy is high, but the recall and precision are generally very low. In contrast, Dallas, a location with many high HI days, shows moderately high accuracy, as well as extremely high recall and precision. These differences are likely due to distinct causes of heatwaves in different climatological regions of the United States, as reflected in the feature importance scores output by Random Forest models. The ML models designed in this study can be used to assist with local heat index forecasting and extreme heat warning issuance at minimal computational cost.
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