摘要
由机器人与机器学习每日新闻的新闻记者兼工作人员新闻编辑-调查人员讨论人工智能的新发现。根据NewsRx编辑在泰国清迈的新闻报道，研究表明，“洪水对环境、经济和人类造成了灾难性的破坏。如果在灾前时期实施适当的管理，洪水损失可以减少。”新闻记者引用清迈大学的一篇研究文章:“洪水灾害图包括地理信息图上显示的灾害风险信息和一个地区可能发生的洪水事件,本文提出了一个时空洪水灾害图框架,利用时空数据生成洪水灾害图,该框架包括三个过程:(1)时间预测,利用LSTM技术预测下一次的水位和降雨量；(2)空间插值，利用IDW技术估计e值；(3)地图生成，利用CNN技术预测洪水事件，生成洪水灾害图，研究区域为泰国清迈省，生成的灾害图覆盖20107.有14个水位测报站和16个雨量站。
Abstract
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.”
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