教師資料查詢 | 類別: 期刊論文 | 教師: 張麗秋 LI-CHIU CHANG (瀏覽個人網頁)

標題:Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things
學年
學期
出版(發表)日期2020/05/31
作品名稱Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things
作品名稱(其他語言)
著者Shun-Nien Yang; Li-Chiu Chang
單位
出版者
著錄名稱、卷期、頁數Water 12(6), 1578
摘要Natural disasters have tended to increase and become more severe over the last decades. A preparation measure to cope with future floods is flood forecasting in each particular area for warning involved persons and resulting in the reduction of damage. Machine learning (ML) techniques have a great capability to model the nonlinear dynamic feature in hydrological processes, such as flood forecasts. Internet of Things (IoT) sensors are useful for carrying out the monitoring of natural environments. This study proposes a machine learning-based flood forecast model to predict average regional flood inundation depth in the Erren River basin in south Taiwan and to input the IoT sensor data into the ML model as input factors so that the model can be continuously revised and the forecasts can be closer to the current situation. The results show that adding IoT sensor data as input factors can reduce the model error, especially for those of high-flood-depth conditions, where their underestimations are significantly mitigated. Thus, the ML model can be on-line adjusted, and its forecasts can be visually assessed by using the IoT sensors’ inundation levels, so that the model’s accuracy and applicability in multi-step-ahead flood inundation forecasts are promoted
關鍵字machine learning model;Internet of Things (IoT);regional flood inundation depth;recurrent nonlinear autoregressive with exogenous inputs (RNARX)
語言英文(美國)
ISSN2073-4441
期刊性質國外
收錄於SCI;EI;
產學合作國內;
通訊作者Li-Chiu Chang
審稿制度
國別瑞士
公開徵稿
出版型式,電子版
相關連結
SDGs
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