{"id":28819,"date":"2023-01-13T10:16:44","date_gmt":"2023-01-13T10:16:44","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=28819"},"modified":"2023-01-13T10:16:44","modified_gmt":"2023-01-13T10:16:44","slug":"monitoring-climate-induced-natural-hazards-machine-learning","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/monitoring-climate-induced-natural-hazards-machine-learning\/28819\/","title":{"rendered":"Monitoring climate-induced natural hazards with Machine Learning"},"content":{"rendered":"
Over the last few decades, rising global temperatures have caused many natural hazards like hurricanes, snowstorms, floods<\/a>, and wildfires to grow in intensity and frequency. Although these natural phenomena cannot be prevented by humans, the rapidly increasing number of satellites that orbit the Earth from space provides a great opportunity to monitor their evolution.\u00a0<\/span>\u00a0<\/span><\/p>\n Research presented last month at the annual meeting of the American Geophysical Union<\/a> has indicated that satellites can be combined with Machine Learning<\/a> to prepare for these ever-increasing natural hazards. This would allow people in the area to make informed decisions, improving the effectiveness of local disaster response and management.<\/span>\u00a0<\/span><\/p>\n \u201cPredicting the future is a pretty difficult task, but by using remote sensing and Machine Learning, our research aims to help create a system that will be able to monitor these climate-induced hazards in a manner that enables a timely and informed disaster response,\u201d said CK Shum, co-author of the study and a professor at the Byrd Polar Research Center and in earth sciences at The Ohio State University.\u00a0<\/span>\u00a0<\/span><\/p>\n To study climate-induced natural hazards, Shum\u2019s research focused on geodesy \u2013 the science of measuring the planet\u2019s size, shape, and orientation in space. The team conducted several case studies using geodetic data gathered from various space agency satellites to test whether a mix of remote sensing and deep Machine Learning analytics could accurately monitor abrupt natural hazards, including floods, droughts, and storm surges.\u00a0<\/span>\u00a0<\/span><\/p>\n These methods were utilised in one of the team\u2019s experiments to determine whether radar signals from Earth\u2019s Global Navigation Satellite System (GNSS), which were reflected over the ocean and received by GNSS receivers located at towns offshore in the Gulf of Mexico, could be used to track hurricane evolution by measuring rising sea levels after landfall.\u00a0<\/span>\u00a0<\/span><\/p>\n The team studied how seven storms between 2020 and 2021, including Hurricane Hana and Hurricane Delta, affected coastal sea levels before they made landfall in the Gulf of Mexico. Through monitoring these complex changes, the team found a positive correlation between higher sea levels and how intense storm surges were.<\/span>\u00a0<\/span><\/p>\nThe team tested a range of methods for natural hazard monitoring <\/span>\u00a0<\/span><\/h3>\n