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Impounding surface waters in reservoirs is a major mechanism for providing water for human consumption, including potable water, hydroelectric power, and industrial uses. Building reservoirs incurs environmental and social costs, and therefore safeguarding their effectiveness and longevity is a concern of clear public interest. One factor that affects the longevity of reservoirs is sedimentation, a process exacerbated by land use conversion in upstream watershed areas. Despite the economic importance of preventing sedimentation in existing reservoirs, few consumers are aware of the natural features that provide sediment retention services and the relevance of their conservation in their daily lives. Moreover, managing for landscape level sediment retention services is challenging due to a lack of clarity regarding supply and demand flows that transcend watershed boundaries and jurisdictions. Our study seeks to bridge these gaps by characterizing the flow of sediment retention services to reservoirs and link these services to the specific consumers that benefit using a socio-ecological network (SEN) framing. We conducted this study on the island of Puerto Rico (PR), the population of which is heavily reliant on reservoirs as a primary water resource, while experiencing severe and chronic reservoir sedimentation problems. Our study models avoided sediment export, and the costs were averted thanks to this service. We characterized protection as opposed to vulnerability of these sediment retention services by estimating the proportion of natural areas under some form of legal conservation status and the level of landscape fragmentation. We frame these services as an SEN by using water distribution lines as links to estimate the number of beneficiaries and their location relative to the reservoir’s water source. Our results identify watersheds with conservation needs, their beneficiaries, and where within those watersheds to prioritize conservation efforts to safeguard access to clean water in PR. More broadly, our study provides a model case study for establishing supply and demand service flows of water purification services and demonstrating the utility of mapping socio-ecological networks of service flows in order to justify conservation policies based on ecosystem services.

The abundance of available information on social media can provide invaluable insights into people’s responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter- and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users in Washington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets in Washington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts’ messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweets that requested readers take action, such as hand washing.

Despite the increasingly prominent role of social media in disaster events, studies analyzing its use in rescue operations remain scanty. Hurricane Harvey hit Texas with unprecedented rainfall and flooding in 2017 and was marked by widespread use of social media for rescue requests. We conducted a survey of 195 Twitter users in Houston and surrounding communities who had requested for rescue during Harvey. The objective was to investigate our targeted group's socioeconomic and flood exposure characteristics, report the effectiveness of Twitter, and highlight lessons learnt and suggestions made for its use in future rescue missions. Survey revealed that those requesting rescue on Twitter were better educated, employed (80%), and homeowners (81%). Majority of them were flooded (87%), but remained satisfied with current location and did not consider moving. Calling relatives and friends for rescue was most responsive and yielded higher assistance-provided rate than using Twitter. Our respondents found Twitter helpful, but identified issues such as not knowing when volunteers received their requests or whether they would send help. They suggested promoting Twitter accounts and hashtags that accept emergency requests. This study provides baseline information and actionable suggestions for first responders, community managers, and resilience practitioners to improve future rescue missions.

Hurricane Harvey in 2017 marked an important transition where many disaster victims used social media rather than the overloaded 911 system to seek rescue. This article presents a machine-learning-based detector of rescue requests from Harvey-related Twitter messages, which differentiates itself from existing ones by accounting for the potential impacts of ZIP codes on both the preparation of training samples and the performance of different machine learning models. We investigate how the outcomes of our ZIP code filtering differ from those of a recent, comparable study in terms of generating training data for machine learning models. Following this, experiments are conducted to test how the existence of ZIP codes would affect the performance of machine learning models by simulating different percentages of ZIP-code-tagged positive samples. The findings show that (1) all machine learning classifiers except K-nearest neighbors and Naïve Bayes achieve state-of-the-art performance in detecting rescue requests from social media; (2) using ZIP code filtering could increase the effectiveness of gathering rescue requests for training machine learning models; (3) machine learning models are better able to identify rescue requests that are associated with ZIP codes. We thereby encourage every rescue-seeking victim to include ZIP codes when posting messages on social media. This study is a useful addition to the literature and can be helpful for first responders to rescue disaster victims more efficiently.

AI fairness is tasked with evaluating and mitigating bias in algorithms that may discriminate towards protected groups. This paper examines if bias exists in AI algorithms used in disaster management and in what manner. We consider the 2017 Hurricane Harvey when flood victims in Houston resorted to social media to request for rescue. We evaluate a Random Forest regression model trained to predict Twitter rescue request rates from social-environmental data using three fairness criteria (independence, separation, and sufficiency). The Social Vulnerability Index (SVI), its four sub-indices, and four variables representing digital divide were considered sensitive attributes. The Random Forest regression model extracted seven significant predictors of rescue request rates, and from high to low importance they were percent of renter occupied housing units, percent of roads in flood zone, percent of flood zone area, percent of wetland cover, percent of herbaceous, forested and shrub cover, mean elevation, and percent of households with no computer or device. Partial Dependence plots of rescue request rates against each of the seven predictors show the non-linear nature of their relationships. Results of the fairness evaluation of the Random Forest model using the three criteria show no obvious biases for the nine sensitive attributes, except that a minor imperfect sufficiency was found with the SVI Housing and Transportation sub-index. Future AI modeling in disaster research could apply the same methodology used in this paper to evaluate fairness and help reduce unfair resource allocation and other social and geographical disparities.