Flood Inundation Mapping of the Hitachi Region in the Kuji River Basin, Japan, During the October 11–13, 2019 Extreme Rain Event

The frequency of severe flood events has been increasing recently in Japan. One of the latest events occurred in October 2019 and caused extensive damage in several river basins, especially in the central and northern regions of the country. In this study, we selected the Hitachi region (Hitachi-Omiya and Hitachi-Ota) within the Kuji River Basin which underwent considerable flooding due to the failure of embankments at two locations in the region. Maximum-possible flood inundation maps were generated using survey-based data and hydrological modeling for the Hitachi region. These maps incorporated the flood scenarios (embankment failures). All the generated products were compared with the reference flood mapping, i.e., Sentinel-1 data and Geospatial Information Authority of Japan (GSI) data for that region. It was observed that generated flood inundation mapping product based on the survey-data yielded results similar to those obtained with GSI data for the Hitachi region. Although each flood mapping product has advantages and disadvantages, they can be a good reference for the proper management and mitigation of flood disaster in the future. The rapid development of flood inundation mapping products that consider varying flood scenarios is an important part of flood mitigation strategies.

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Event Based Flood Inundation Mapping Under the Impact of Climate Change: A Case Study in Lower Kelani River Basin, Sri Lanka

Hydrology Current Research ◽

10.4172/2157-7587.1000228 ◽

2015 ◽

Vol 07 (01) ◽

Author(s):

Gouri De Silva ◽

Weerakoonb SB

Keyword(s):

Climate Change ◽

Sri Lanka ◽

River Basin ◽

Flood Inundation ◽

Inundation Mapping ◽

Flood Inundation Mapping ◽

Event Based ◽

Kelani River ◽

The Impact

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Machine-learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

Modeling Earth Systems and Environment ◽

10.1007/s40808-021-01175-8 ◽

2021 ◽

Author(s):

Habtamu Tamiru ◽

Meseret Wagari

Keyword(s):

Machine Learning ◽

River Basin ◽

Water Body ◽

Hydrological Model ◽

Daily Rainfall ◽

Landsat 8 ◽

Flood Inundation ◽

Inundation Mapping ◽

Flood Inundation Mapping ◽

Daily Discharge

AbstractThis study presents an integrated machine-learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. ANN and HEC-RAS models were integrated as a predictive hydrological and hydraulic model to generate runoff and the extent of flood, respectively. Daily rainfall and temperature data of 7-years (1999–2005), daily discharge (1999–2005) and 30 m × 30 m gridded Topographical Wetness Index (TWI) were used to train a predictive ANN hydrological model in RStudio. The predictive performance of the developed ANN hydrological model was evaluated in RStudio using Nash–Sutcliffe Efficiency (NSE) values of 0.86 and 0.88 during the training period (1999–2005) and testing period (2006–2008), respectively, with the corresponding observed daily discharge. The validated ANN predictive hydrological model was linked with HEC-RAS to generate the flood extent along the river course. The HEC-RAS model result was calibrated and validated using the water body delineated using Normal Difference Water Index (NDWI) from LANDSAT 8 imagery based on historical flood events of 2005 and 2008. It was found that about 96% of an agreement was made between the flood-prone areas generated in HEC-RAS and the water body delineated using NDWI. Therefore, it is logical to conclude that the integration of a machine-learning approach with the HEC-RAS model has improved the spatiotemporal uncertainties in traditional flood forecasting methods. This integrated model is powerful tool for flood inundation mapping to warn residents of this basin.

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Machine learning and HEC-RAS integrated models for flood inundation mapping in Baro River Basin, Ethiopia

10.22541/au.161294499.94815440/v1 ◽

2021 ◽

Author(s):

Habtamu Tamiru

Keyword(s):

Machine Learning ◽

River Basin ◽

Daily Rainfall ◽

Simulation Models ◽

Spatial And Temporal Variability ◽

Flood Inundation ◽

Rainfall Runoff ◽

Ann Model ◽

Inundation Mapping ◽

Flood Inundation Mapping

This paper presents the integrated machine learning and HEC-RAS models for flood inundation mapping in Baro River Basin, Ethiopia. A predictive rainfall-runoff and spatially distributed river simulation models were developed using Artificial Neural Networks (ANNs) and HEC-RAS respectively. Daily rainfall and temperature data of 7-yrs and Topographical Wetness Index (TWI) with a spatial resolution of 50 x 50m were used to train the ANN in R studio. The integration of the spatial and temporal variability in this paper improved the accuracy of the predictive models integrated with ANN and HEC-RAS. The predictive ANN model was tested with the observed daily discharge of the same temporal resolution and the rainfall-runoff result obtained from the tested ANN model was used as input for the HEC-RAS. The flood event of 2005 was used to verify the accuracy of flood generated in the HEC-RAS model by implementing the Normal Difference Water Index (NDWI). The comparison was made between the flood inundation map generated by HEC-RAS and flood events of different periods based on coverage percentage areas and a good agreement was reached with 96 % overlapped areas. The performance of ANN and HEC-RAS models were evaluated with 0.86 and 0.88 values at the training and testing period respectively. Finally, it was concluded that the integration of a machine learning approach with the HEC-RAS model in developing a flood inundation mapping is an appropriate tool to warn residents in this river basin.

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