首页|New Data from Chiang Mai University Illuminate Research in Machine Learning (Spa tiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand)
New Data from Chiang Mai University Illuminate Research in Machine Learning (Spa tiotemporal Flood Hazard Map Prediction Using Machine Learning for a Flood Early Warning Case Study: Chiang Mai Province, Thailand)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators discuss new findings in artificial intelligence. According to news reporting out of Chiang Mai, Thailand , by NewsRx editors, research stated, “Floods cause disastrous damage to the env ironment, economy, and humanity. Flood losses can be reduced if adequate managem ent is implemented in the pre-disaster period.” The news correspondents obtained a quote from the research from Chiang Mai Unive rsity: “Flood hazard maps comprise disaster risk information displayed on geo-lo cation maps and the potential flood events that occur in an area. This paper pro poses a spatiotemporal flood hazard map framework to generate a flood hazard map using spatiotemporal data. The framework has three processes: (1) temporal pred iction, which uses the LSTM technique to predict water levels and rainfall for t he next time; (2) spatial interpolation, which uses the IDW technique to estimat e values; and (3) map generation, which uses the CNN technique to predict flood events and generate flood hazard maps. The study area is Chiang Mai Province, Th ailand. The generated hazard map covers 20,107 km2. There are 14 water-level tel emetry stations and 16 rain gauge stations.”
Chiang Mai UniversityChiang MaiThail andAsiaCyborgsEmerging TechnologiesMachine Learning
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