{"670297":{"#nid":"670297","#data":{"type":"news","title":"Machine Learning Key to Proposed App that Could Help Flood-prone Communities","body":[{"value":"\u003Cp\u003EA scientific machine learning (ML) expert at Georgia Tech is lending a hand in developing an app to identify and help Florida communities most at risk of flooding.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/cse.gatech.edu\/\u0022\u003ESchool of Computational Science and Engineering (CSE)\u003C\/a\u003E Assistant Professor\u0026nbsp;\u003Cstrong\u003EPeng Chen\u003C\/strong\u003E\u0026nbsp;is co-principal investigator of a $1.5 million National Science Foundation grant to develop the CRIS-HAZARD system.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Ca href=\u0022https:\/\/www.stpetersburg.usf.edu\/news\/2023\/nsf-grant-cris-climate-risk-app.aspx\u0022\u003ECRIS-HAZARD\u003C\/a\u003E\u2018s strength derives from integrating geographic information and data mined from community input, like traffic camera videos and social media posts. \u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThis ability helps policymakers identify areas most vulnerable to flooding and address community needs. The app also predicts and assesses flooding in real time to connect victims with first responders and emergency managers.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cSuccessfully deploying CRIS-HAZARD will harness community knowledge through direct and indirect engagement efforts to inform decision-making,\u201d Chen said. \u201cIt will connect individuals to policymakers and serve as a roadmap at helping the most vulnerable communities.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003EChen\u2019s role in CRIS-HAZARD will be to develop new ML models for the app\u2019s prediction capability. These assimilation models integrate the mined data with predictions from current hydrodynamic models.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAlong with making an immediate impact in flood-prone coastal communities, Chen said these models could have broader applications in the future. These include models for improved hurricane prediction and management of water resources.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EThe models Chen will build for CRIS-HAZARD derive from past applications aimed at helping communities.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EChen has crafted similar models for monitoring and mitigating disease spread, including Covid-19. He has also worked on materials science projects to accelerate the design of metamaterials and self-assembly materials.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cScientific machine learning is very broad concept and can be applied to many different fields,\u201d Chen said. \u201cOur group looks at how to accelerate optimization, account for risk, and quantify uncertainty in these applications.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003EUncertainty in CRIS-HAZARD is what brings Chen to the project, headed by University of South Florida researchers. While the app\u2019s novelty lies in its use of heterogenous data, inferring predictions can be challenging since the data comes from different sources in varying formats.\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003ETo overcome this, Chen intends to build new data assimilation models from scratch powered by deep neural networks (DNNs).\u003C\/p\u003E\r\n\r\n\u003Cp\u003EAlong with their ability to find connections between heterogeneous data, DNNs are scalable and inexpensive. This beats the alternative of using supercomputers to make the same calculations.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EDNNs are also fast and can significantly reduce computational time. According to Chen, the efficiency of DNNs can achieve acceleration hundreds of thousands of times greater than classical models.\u003C\/p\u003E\r\n\r\n\u003Cp\u003ELow cost and time make it possible to run DNN-based simulations multiple times. This improves reliability in prediction results in real-time once the DNNs are properly trained.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u201cThe data may not be consistent or compatible since there are different models we\u2019re trying to integrate, making prediction uncertain,\u201d Chen said. \u201cWe can run these ML models many times to quantify the uncertainty and give a probability distribution or a range of predictions.\u201d\u003C\/p\u003E\r\n\r\n\u003Cp\u003ECRIS-HAZARD also exemplifies the power of collaboration across disciplines and universities. In this case, machine learning techniques reach across state boundaries to help people that are vulnerable to flooding or other natural disasters.\u003C\/p\u003E\r\n\r\n\u003Cp\u003EUSF Professor\u0026nbsp;\u003Cstrong\u003EBarnali Dixon\u003C\/strong\u003E\u0026nbsp;leads the project with Associate Professor\u0026nbsp;\u003Cstrong\u003EYi Qiang\u003C\/strong\u003E\u2014 both geocomputation researchers in the School of Geosciences, incorporating data science and artificial intelligence.\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ESubhro Guhathakurta\u003C\/strong\u003E\u0026nbsp;collaborates with Chen from Georgia Tech. Along with being a professor in the School of City \u0026amp; Regional Planning, Guhathkurta is director of Tech\u2019s Master of Science in Urban Analytics program and the Center for Spatial Planning and Analytics and Visualization.\u003C\/p\u003E\r\n","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EA School of Computational Science and Engineering faculty member is co-leading a $1.5M National Science Foundation grant to mitigate flood risks. The team is developing an app to help policymakers identify areas most vulnerable to flooding and address community needs.\u003C\/p\u003E\r\n","format":"limited_html"}],"field_summary_sentence":[{"value":"A School of Computational Science and Engineering faculty member is co-leading a $1.5M National Science Foundation grant to mitigate flood risks."}],"uid":"32045","created_gmt":"2023-10-09 17:06:48","changed_gmt":"2023-10-09 17:18:14","author":"Ben Snedeker","boilerplate_text":"","field_publication":"","field_article_url":"","dateline":{"date":"2023-10-09T00:00:00-04:00","iso_date":"2023-10-09T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"671984":{"id":"671984","type":"image","title":"Peng Chen NSF co-pi.jpeg","body":null,"created":"1696871217","gmt_created":"2023-10-09 17:06:57","changed":"1696871217","gmt_changed":"2023-10-09 17:06:57","alt":"Georgia Tech School of CSE Assistant Professor Peng Chen takes a break from his work for a photo.","file":{"fid":"255158","name":"Peng Chen NSF co-pi.jpeg","image_path":"\/sites\/default\/files\/2023\/10\/09\/Peng%20Chen%20NSF%20co-pi.jpeg","image_full_path":"http:\/\/www.tlwarc.hg.gatech.edu\/\/sites\/default\/files\/2023\/10\/09\/Peng%20Chen%20NSF%20co-pi.jpeg","mime":"image\/jpeg","size":54215,"path_740":"http:\/\/www.tlwarc.hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2023\/10\/09\/Peng%20Chen%20NSF%20co-pi.jpeg?itok=cg2Y_5KU"}}},"media_ids":["671984"],"groups":[{"id":"47223","name":"College of Computing"},{"id":"50877","name":"School of Computational Science and Engineering"}],"categories":[{"id":"42901","name":"Community"},{"id":"135","name":"Research"},{"id":"153","name":"Computer Science\/Information Technology and Security"}],"keywords":[{"id":"187915","name":"go-researchnews"},{"id":"10199","name":"Daily Digest"}],"core_research_areas":[{"id":"39501","name":"People and Technology"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003EBryant Wine, Communications Officer\u003C\/p\u003E\r\n\r\n\u003Cp\u003ESchool of Computational Science \u0026amp; Engineering\u003C\/p\u003E\r\n\r\n\u003Cp\u003Ebryant.wine@cc.gatech.edu\u003C\/p\u003E\r\n","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}