{"id":17377,"date":"2022-11-29T23:08:20","date_gmt":"2022-11-30T04:08:20","guid":{"rendered":"https:\/\/d3.harvard.edu\/platform-digit\/?post_type=hck-submission&p=17377"},"modified":"2022-11-29T23:08:21","modified_gmt":"2022-11-30T04:08:21","slug":"floodmapp-real-time-emergency-flood-data","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/floodmapp-real-time-emergency-flood-data\/","title":{"rendered":"FloodMapp – Real Time Emergency Flood Data"},"content":{"rendered":"\n\n\n

The Company<\/strong><\/p>\n\n\n\n

FloodMapp is a Brisbane, AU based startup, using AI-powered models to equip government agencies, utilities, logistics companies and insurers in Australia and the United States with real-time flood inundation modelling that can achieve large scale, rapid modelling, as well as forecast inundation depth down to an individual street address. The company seeks to replace traditional approaches to hydrology and predictive analytics to help emergency managers and citizens know when and how floods are coming.<\/p>\n\n\n\n

The Issue<\/strong><\/p>\n\n\n\n

Flood cause widespread damage to neighborhoods, infrastructure, communities, and, of course, people. Flooding is predicted to displace 50 million people by 2100. The U.S. government estimates<\/a> that floods in recent decades (exclusive of hurricanes and tropical storms) have caused an estimated $160 USD billion in damage and killed hundreds of people. A Deloitte study from 2017 showed natural disasters cost Australia $13.2 AUD billion a year in economic damages<\/a>. This is predicted to triple to $39.3 AUD billion by 2050, even without considering climate change. Of this, flooding costs the economy $8.8 AUD billion a year.<\/p>\n\n\n\n

The Solution and Value Creation<\/strong><\/p>\n\n\n\n

Existing solutions rely on our understanding of physics and hydrology. Given the topology of a location, one can predict water flow. However, the computational costs associated with doing so are incredibly high. FloodMapp uses a completely data-based approach that takes advantage of widely available techniques in machine learning to make those calculations much more palatable, a new top down approach versus the traditional bottoms up methodology.<\/p>\n\n\n\n

This approach led to the creation of DASH <\/strong>(Dynamic Automated Scalable Hydroinformatic), the startup\u2019s real-time flood model. DASH creates reports on current, future, and past flood conditions within an area, called \u2018NowCasts\u2019, \u2018ForeCasts\u2019 and \u2018PostCasts\u2019. FloodMapp achieves this by first collecting valuable data, by being to every river and rain gauge and digital elevation model across Australia. This includes collecting forecast and observed rainfall, river height at the target gauge and its upstream network, as well as past flood data. All of these data points are fed into their machine learning model.<\/p>\n\n\n\n

NowCast<\/strong><\/p>\n\n\n\n

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FloodMapp\u2019s modeling of real time flooding (NowCast) near Brisbane, AU. Image Credits:<\/strong> FloodMapp<\/p>\n\n\n\n

ForeCast<\/strong><\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

FloodMapp\u2019s modeling of forecasted flooding, with colors showing severity by flood level (ForeCast) near Brisbane, AU. Image Credits:<\/strong> FloodMapp<\/p>\n\n\n\n

PostCast<\/strong><\/p>\n\n\n\n

\"\"<\/a><\/figure>\n\n\n\n

FloodMapp\u2019s modeling of post flood damage (PostCast) near Sydney, AU. Image Credits:<\/strong> FloodMapp<\/p>\n\n\n\n

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Customers<\/strong><\/p>\n\n\n\n

While FloodMapp plans on servicing a variety of industries as a data provider, it currently focuses on providing emergency service providers, like the Queensland Fire and Emergency Services<\/a> and as the cities of Norfolk and Virginia Beach in Virginia, to generate extra lead time and post flood damage reporting to interested parties (regulators, management, etc).<\/p>\n\n\n\n

Floodmap provides customers a data layer, which customers pay a subscription fee for access to. This data layer typically folds into or compliments FloodMapp\u2019s customers existing systems, such as providing warnings or road closures which a fire department may forward onto citizens. This is usually done through geographic information systems (GIS) that can be combined with other data streams to provide situational awareness to emergency response and recovery personnel.<\/p>\n\n\n\n

FloodMapp hopes to expand to other industries, such as players with large investments in physical assets: including retail banks and clothiers, telecom and power companies, or infrastructure or mining companies. Australia\u2019s large mining industry can benefit by getting a picture of potential impact to their site before a flood happens, preventing damage to equipment, minimize lost production, and mitigate business interruption. That could include relocating machinery and equipment to higher ground, bringing in more pumping infrastructure, or shipping more product before critical rail infrastructure is closed. There is also hope that the SEC will mandate further climate change financial disclosures<\/a>, pushing companies to improve their risk profile to climate and flooding disasters.<\/p>\n\n\n\n

FloodMapp has also been looking to expand through partnerships, notably through integration with Waze by rerouting drivers away from flooded areas. The real time information generated by DASH (fed by rainfall, tidal, and riverine data) is layered with Norfolk\u2019s citywide road network \u00a0and sent to Waze. Drivers who then use Waze receive pop-up icons and audio alerts to warn them about flooded streets along their route and help them avoid property and life-threatening hazards. Drivers can confirm flooding in the app, which helps validate FloodMapp\u2019s technology and makes future Waze alerts more accurate.<\/p>\n\n\n\n

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FloodMapp Waze integration example in Virginia, USA. Image Credits:<\/strong> FloodMapp<\/p>\n\n\n\n

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Challenges and Recommendations<\/strong><\/p>\n\n\n\n

As FloodMapp continues to expand and improve its products and services, they will likely have a few challenges to surmount or mitigate:<\/p>\n\n\n\n

  1. Data Quality \u2013