Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things

doi:10.3390/w12061578

機構典藏 > College of Engineering > Graduate Institute & Department of Water Resources and Environmental Engineering > Journal Article > Item 987654321/120226

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/120226

Title:	Regional Inundation Forecasting Using Machine Learning Techniques with the Internet of Things
Authors:	Yang, Shun-Nien;Chang, Li-Chiu
Keywords:	machine learning model;Internet of Things (IoT);regional flood inundation depth;recurrent nonlinear autoregressive with exogenous inputs (RNARX)
Date:	2020-05-31
Issue Date:	2021-03-17 12:11:37 (UTC+8)
Abstract:	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
Relation:	Water 12(6), 1578
DOI:	10.3390/w12061578
Appears in Collections:	[Graduate Institute & Department of Water Resources and Environmental Engineering] Journal Article

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