Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

Flood risk in urban areas: modelling, management and adaptation to climate change. A review

[Abstract:] The modelling and management of flood risk in urban areas are increasingly recognized as global challenges. The complexity of these issues is a consequence of the existence of several distinct sources of risk, including not only fluvial, tidal and coastal flooding, but also exposure to urban runoff and local drainage failure, and the various management strategies that can be proposed. The high degree of vulnerability that characterizes such areas is expected to increase in the future due to the effects of climate change, the growth of the population living in cities, and urban densification. An increasing awareness of the socio-economic losses and environmental impact of urban flooding is clearly reflected in the recent expansion of the number of studies related to the modelling and management of urban flooding, sometimes within the framework of adaptation to climate change. The goal of the current paper is to provide a general review of the recent advances in flood-risk modelling and management, while also exploring future perspectives in these fields of research

TriggerCit: Early Flood Alerting using Twitter and Geolocation - A Comparison with Alternative Sources

Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021.Comment: 12 pages Keywords Social Media, Disaster management, Early Alertin

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications

A Cloud-Based Global Flood Disaster Community Cyber-Infrastructure: Development and Demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-of-the- art cloud computing and crowdsourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters

A cloud-based global flood disaster community cyber-infrastructure: Development and demonstration

Flood disasters have significant impacts on the development of communities globally. This study describes a public cloud-based flood cyber-infrastructure (CyberFlood) that collects, organizes, visualizes, and manages several global flood databases for authorities and the public in real-time, providing location-based eventful visualization as well as statistical analysis and graphing capabilities. In order to expand and update the existing flood inventory, a crowdsourcing data collection methodology is employed for the public with smartphones or Internet to report new flood events, which is also intended to engage citizen-scientists so that they may become motivated and educated about the latest developments in satellite remote sensing and hydrologic modeling technologies. Our shared vision is to better serve the global water community with comprehensive flood information, aided by the state-ofthe- art cloud computing and crowd-sourcing technology. The CyberFlood presents an opportunity to eventually modernize the existing paradigm used to collect, manage, analyze, and visualize water-related disasters

Hydrolink 4/2022. Citizen science

Topic: Citizen Scienc

Exploring the data needs and sources for severe weather impact forecasts and warnings : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Figures 2.4 & 2.5 are re-used with permission.The journal articles in Appendices J, L & M are republished under respective Creative Commons licenses. Appendix K has been removed from the thesis until 1 July 2022 in accordance with the American Meteorological Society Copyright Policy, but is available open access at https://doi.org/10.1175/WCAS-D-21-0093.1Early warning systems offer an essential, timely, and cost-effective approach for mitigating the impacts of severe weather hazards. Yet, notable historic severe weather events have exposed major communication gaps between warning services and target audiences, resulting in widespread losses. The World Meteorological Organization (WMO) has proposed Impact Forecasts and Warnings (IFW) to address these communication gaps by bringing in knowledge of exposure, vulnerability, and impacts; thus, leading to warnings that may better align with the position, needs, and capabilities of target audiences. A gap was identified in the literature around implementing IFWs: that of accessing the required knowledge and data around impacts, vulnerability, and exposure. This research aims to address this gap by exploring the data needs of IFWs and identifying existing and potential data sources to support those needs. Using Grounded Theory (GT), 39 interviews were conducted with users and creators of hazard, impact, vulnerability, and exposure (HIVE) data within and outside of Aotearoa New Zealand. Additionally, three virtual workshops provided triangulation with practitioners. In total, 59 people participated in this research. Resulting qualitative data were analysed using GT coding techniques, memo-writing, and diagramming. Findings indicate a growing need for gathering and using impact, vulnerability, and exposure data for IFWs. New insight highlights a growing need to model and warn for social and health impacts. Findings further show that plenty of sources for HIVE data are collected for emergency response and other uses with relevant application to IFWs. Partnerships and collaboration lie at the heart of using HIVE data both for IFWs and for disaster risk reduction. This thesis contributes to the global understanding of how hydrometeorological and emergency management services can implement IFWs, by advancing the discussion around implementing IFWs as per the WMO’s guidelines, and around building up disaster risk data in accordance with the Sendai Framework Priorities. An important outcome of this research is the provision of a pathway for stakeholders to identify data sources and partnerships required for implementing a hydrometeorological IFW system