The objective of this research is to explore the temporal importance of community-scale human activity features for rapid assessment of flood impacts. Ultimate flood impact data, such as flood inundation maps and insurance claims, becomes available only weeks and months after the floods have receded. Crisis response managers, however, need near-real-time data to prioritize emergency response. This time lag creates a need for rapid flood impact assessment. Some recent studies have shown promising results for using human activity fluctuations as indicators of flood impacts. Existing studies, however, used mainly a single community-scale activity feature for the estimation of flood impacts and have not investigated their temporal importance for indicating flood impacts. Hence, in this study, we examined the importance of heterogeneous human activity features in different flood event stages. Using four community-scale big data categories we derived ten features related to the variations in human activity and evaluated their temporal importance for rapid assessment of flood impacts. Using multiple random forest models, we examined the temporal importance of each feature in indicating the extent of flood impacts in the context of the 2017 Hurricane Harvey in Harris County, Texas. Our findings reveal that 1) fluctuations in human activity index and percentage of congested roads are the most important indicators for rapid flood impact assessment during response and recovery stages; 2) variations in credit card transactions assumed a middle ranking; and 3) patterns of geolocated social media posts (Twitter) were of low importance across flood stages. The results of this research could rapidly forge a multi-tool enabling crisis managers to identify hotspots with severe flood impacts at various stages then to plan and prioritize effective response strategies.

中文翻译：

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揭示社区规模人类活动特征对洪水影响快速评估的时间重要性

本研究的目的是探索社区尺度人类活动特征对快速评估洪水影响的时间重要性。最终洪水影响数据，例如洪水淹没地图和保险索赔，仅在洪水退去数周和数月后才可用。然而，危机响应管理人员需要近乎实时的数据来确定紧急响应的优先级。这个时间滞后产生了对快速洪水影响评估的需要。最近的一些研究显示，使用人类活动波动作为洪水影响指标的结果很有希望。然而，现有的研究主要使用单一社区规模的活动特征来估计洪水影响，并没有调查它们在指示洪水影响方面的时间重要性。因此，在本研究中，我们研究了不同洪水事件阶段中异质人类活动特征的重要性。使用四个社区规模的大数据类别，我们得出了与人类活动变化相关的十个特征，并评估了它们对快速评估洪水影响的时间重要性。我们使用多个随机森林模型检查了每个特征在指示 2017 年德克萨斯州哈里斯县哈维飓风背景下洪水影响程度方面的时间重要性。我们的研究结果表明：1) 人类活动指数和拥堵道路百分比的波动是响应和恢复阶段快速洪水影响评估的最重要指标；2) 信用卡交易的变化居中；3) 地理定位社交媒体帖子 (Twitter) 的模式在洪水阶段的重要性较低。这项研究的结果可以迅速形成一个多工具，使危机管理人员能够识别在各个阶段具有严重洪水影响的热点，然后规划和优先考虑有效的响应策略。