SCC: Landslide Risk Management in Remote Communities: Integrating Geoscience, Data Science, and Social Science in Local Context

SCC：偏远社区的山体滑坡风险管理：在当地环境中整合地球科学、数据科学和社会科学

• 批准号: 1831770
• 负责人: Robert Lempert
• 金额: $ 210.1万
• 依托单位: Rand Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831770&HistoricalAwards=false
• 关键词: SCC; Landslide; Risk; Management; Remote

Communities worldwide, including many throughout the United States, struggle to predict and manage significant landslide risk. Landslides prove difficult to predict because they are infrequent and their occurrence may depend strongly on the specific soil, rain, and wind conditions in each location. Effective warning proves hard to disseminate because community members have different risk perceptions and tolerances, and even the best scientific predictions of landslide risk are often imprecise. In this project, a team of geo, information, and social science research institutions, the Sitka Sound Science Center, and the Sitka Tribe of Alaska, will design a novel landslide risk warning system for Sitka, Alaska, a small, diverse coastal town of 9,000 pressed against the steep, landslide-prone slopes of the Tongass National Forest. Working with local students and other residents acting as citizen scientists, the project will deploy small, inexpensive, networked moisture sensors on the slopes above Sitka that, when combined with new methods for integrating diverse data streams, will improve landslide prediction. The project will map Sitka's social networks and residents' understanding of risk and will then use this information, along with new influence maximization methods, which identify well-connected 'key influencers' in each social network, to design effective dissemination channels for landslide warning. The project will use decision support tools to facilitate community deliberations and workshops with government officials on the appropriate design of the physical and social components of a warning system that will best balance timely warning with reduced incidence of disruptive false alarms. While focused on Sitka, this project's results should be widely applicable worldwide, especially in other small or remote towns or communities with landslide risk.This project will advance geoscience, social science, information science, and risk management through innovative incorporation of multiple data streams from sources such as historical records and imagery, hydrologic sensors, and social networks. The project will advance information science by showing how diverse sources of data (of disparate time scales, dimensionalities, and levels of noise) can be integrated to improve decision-making and policy-making in highly uncertain environments. These diverse streams of data will allow us to utilize both existing machine learning methodologies, as well as novel influence maximization models for communicating natural hazard risk. The project will advance geoscience by improving predictive models through direct measurement of landslide triggering conditions and region-specific threshold calibration, and by testing how a vast increase in the number of in-situ sensors affects the design, implementation, and performance of landslide early warning systems. The project will advance social science through an improved understanding of risk perception and communication in social and cultural contexts. It will be among the first to study how network influence maximization can improve community education and natural hazard response. By linking an understanding of social networks and cultural frames of risk perception with a participatory, quantitative decision support system, this project will improve understanding of how data can be used to facilitate a fair, accountable, integrative, and transparent risk management process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

世界各地的社区，包括美国各地的许多社区，都在努力预测和管理重大的山体滑坡风险。滑坡很难预测，因为它们很少发生，而且它们的发生可能在很大程度上取决于每个地点的特定土壤，雨水和风条件。事实证明，有效的预警很难传播，因为社区成员对风险的认识和承受能力各不相同，即使是最好的滑坡风险科学预测也往往不准确。在这个项目中，一个由地理、信息和社会科学研究机构、锡特卡声音科学中心和阿拉斯加锡特卡部落组成的团队将为阿拉斯加锡特卡设计一个新颖的滑坡风险预警系统，锡特卡是一个拥有9,000人口的多元化沿海小镇，紧靠着汤加斯国家森林公园陡峭、易滑坡的斜坡。该项目将与当地学生和其他居民合作，作为公民科学家，在锡特卡上方的斜坡上部署小型，廉价，联网的湿度传感器，当与整合不同数据流的新方法相结合时，将改善滑坡预测。该项目将绘制锡特卡的社交网络和居民对风险的理解，然后将利用这些信息，沿着新的影响最大化方法，确定每个社交网络中连接良好的“关键影响者”，为滑坡预警设计有效的传播渠道。该项目将利用决策支持工具，促进社区审议，并与政府官员举办讲习班，讨论如何适当设计预警系统的物质和社会组成部分，以最佳方式兼顾及时预警和减少扰乱性假警报的发生。该项目以锡特卡为重点，其成果应在全球范围内得到广泛应用，特别是在其他有滑坡风险的偏远小镇或社区。该项目将通过创新性地整合来自历史记录和图像、水文传感器和社交网络等来源的多种数据流，推进地球科学、社会科学、信息科学和风险管理。该项目将通过展示如何整合不同的数据源（不同的时间尺度，维度和噪声水平）来促进信息科学，以改善高度不确定环境中的决策和政策制定。这些不同的数据流将使我们能够利用现有的机器学习方法，以及新的影响最大化模型来传达自然灾害风险。该项目将通过直接测量滑坡触发条件和特定区域阈值校准来改进预测模型，并通过测试现场传感器数量的大量增加如何影响滑坡预警系统的设计，实施和性能，从而推动地球科学的发展。该项目将通过更好地了解社会和文化背景下的风险认知和沟通来推进社会科学。它将率先研究网络影响力最大化如何改善社区教育和自然灾害应对。通过将对风险认知的社交网络和文化框架的理解与参与性定量决策支持系统联系起来，该项目将提高对如何利用数据促进公平、负责、综合、该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响进行评估，被认为值得支持审查标准。

项目成果

A Community-Partnered Approach to Social Network Data Collection for a Large and Partial Network

大型和局部网络的社交网络数据收集的社区合作方法

• DOI: 10.1177/1525822x221074769
• 发表时间: 2022
• 期刊: Field Methods
• 影响因子: 1.7
• 作者: Izenberg, Maxwell;Brown, Ryan;Siebert, Cora;Heinz, Ron;Rahmattalabi, Aida;Vayanos, Phebe
• 通讯作者: Vayanos, Phebe

Exploring Algorithmic Fairness in Robust Graph Covering Problems

• 发表时间: 2020-06
• 期刊: ArXiv
• 作者: Aida Rahmattalabi;P. Vayanos;Anthony Fulginiti;E. Rice;Bryan Wilder;A. Yadav;Milind Tambe

Debris flow initiation in postglacial terrain: Insights from shallow landslide initiation models and geomorphic mapping in Southeast Alaska

冰后地形中的泥石流引发：来自阿拉斯加东南部浅层滑坡引发模型和地貌测绘的见解

• DOI: 10.1002/esp.5336
• 发表时间: 2022
• 期刊: Earth Surface Processes and Landforms
• 影响因子: 3.3
• 作者: Patton, Annette I.;Roering, Joshua J.;Orland, Elijah
• 通讯作者: Orland, Elijah

Efforts to end a stalemate in landslide insurance availability through inclusive policymaking: A case study in Sitka, Alaska

通过包容性政策制定来结束山体滑坡保险供应方面的僵局：阿拉斯加州锡特卡的案例研究

• DOI: 10.1016/j.ijdrr.2022.103202
• 发表时间: 2022
• 期刊: International Journal of Disaster Risk Reduction
• 影响因子: 5
• 作者: Izenberg, Max;Clark-Ginsberg, Aaron;Clancy, Noreen;Busch, Lisa;Schmidt, Jacyn;Dixon, Lloyd
• 通讯作者: Dixon, Lloyd