Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential

Costache, Romulus; Pham, Quoc Bao; Corodescu-Roșca, Ema; Cimpianu, Catalin Ioan; Hong, Haoyuan; Linh, Nguyễn Thị Thùy; Fai, Chow Ming; Ahmed, Ali Najah; Vojtek, Matej; Pandhiani, Siraj Muhammed; Minea, Gabriel; Ciobotaru, Nicu; Popa, Mihnea Cristian; Diaconu, Daniel Constantin; Pham, Binh Thai

doi:10.3390/rs12091422

Cited by 61 publications

(21 citation statements)

References 113 publications

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“…First, the raster formats in both results (NDWI and HEC-RAS) were changed into vector (Polygons) using conversion tool (Scanlon et al 2005), and then the corresponding shape areas were calculated using geometry calculation algorithms in the Arc-GIS. The same geographic Coordinate system (Adindan UTM Zone 37 N) was adjusted for both polygons, and the percentage of overlapping areas is calculated as shown in ( 6) NDWI = NIR − SWI NIR + SWIR the following equation (Potential 2020;Wang et al 2017a, b):…”

Section: Hec-ras Model Evaluationmentioning

confidence: 99%

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

Tamiru

Wagari

2021

Model. Earth Syst. Environ.

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This 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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Section: Hec-ras Model Evaluationmentioning

confidence: 99%

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

Tamiru

Wagari

2021

Model. Earth Syst. Environ.

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“…However, changes in topography or soils typically occur over longer timescales while watershed hydrologic response, i.e. runoff generation, is strongly influenced by shorter timescale processes like, in particular, the land use/ land cover changes (Mohammady et al 2018;Costache et al 2020). The interaction and impact of floods and land use/land cover is reciprocal and the effect of floods on land use/land cover as well as on river geomorphology, such as changes in channel width, mobilization of channel sediments or bank erosion mainly in the meandering reaches, can be significant (Yousefi et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia

Vojtek

Vojteková

Costache

et al. 2021

Geomatics, Natural Hazards and Risk

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Identification of areas susceptible to floods is an important issue which requires an increased attention due to the changing frequency and magnitude of floods, which is mainly a result of the ongoing climate change and increasing anthropic pressure on the landscape. In this study, the aim was to identify the areas susceptible to floods using and comparing two different approaches, namely the multi-criteria decision analysis-analytical hierarchy process (MCDA-AHP) and the machine learning-boosted classification (BCT) and boosted regression (BRT) tree. The study area was represented by the Topl 'a river basin, Slovakia. Altogether, seven relevant flood conditioning factors: elevation, slope, river network density, distance from river, flow accumulation, curve numbers and lithology as well as flood inventory database consisting of 107 flood locations were used. Based on the results, almost 40% of the study area is characterized by high to very high flood susceptibility using the MCDA-AHP. In case of the BCT and BRT models, the share of high and very high flood susceptibility class on the basin area is 45% and 38%, respectively. Validation of the performed flood susceptibility models confirmed generally higher accuracy of the machine learning models. The accuracy of the MCDA-AHP model was 81.33% while the accuracy of the boosted tree models was 87.70% and 91.42%, respectively, for classification and regression. The results of this study can enhance more effective preliminary flood risk assessment according to the EU Floods Directive.

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“…The maximum likelihood (ML)classifier was used in this work. It is a supervised classification method derived from the Bayes theorem (Costache, Bao Pham, et al, 2020). Every pixel is classified to the most likely class or labelled as unclassified if the probability values are all below a threshold (Lillesand, Kiefer, & Chipman, 2015).…”

Section: Study Area and Used Datamentioning

confidence: 99%

Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt

Elkhrachy

Pham

Costache

et al. 2021

J Flood Risk Management

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Digital surface models, land use and rainfall data were used to simulate water areas using Hydrologic Engineering Centres River Analysis System (HEC‐RAS) software. Multi‐temporal synthetic aperture radar (SAR) was used for the detection of flood prone area to calibrate HEC‐RAS, due to the lack of data validation in the New Cairo City, Egypt. The thresholding water detection method was applied to two Sentinel‐1 images, one pre‐ and one post‐flash flood event from April 24 to 27, 2018. The threshold method was used to detect water areas from SAR Sentinel‐1 images. Feature statistical agreement F1 and F2 values ranged from 73.4 to 77.7% between water areas extracted based on backscattering values between 19.97 and 16.53 in decibels (dB) and reference water areas obtained using an optical image of the Sentinel‐2 satellite. The similarity between simulated HEC‐RAS two‐dimensional (2D) of water areas and reference water areas based on SAR data ranged between 74.2 and 89.7% using feature statistical agreement values F1 and F2. It provides a clear suggestion that, in the absence of field observations, SAR data can be used to calibrate the model. Two flood hazard maps created based on water velocity and depth were obtained from HEC‐RAS 2D simulation. The obtained maps indicated that 11% of the roads and 50% of the buildings in New Cairo City are exposed to high hazard areas. Furthermore, 28% of the bare land is situated in a very high vulnerability area. We recommend the use of obtained hazard map in supporting emergency response, and designing effective emergency plans.

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Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential

Cited by 61 publications

References 113 publications

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

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

Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia

Sentinel‐1 remote sensing data and Hydrologic Engineering Centres River Analysis System two‐dimensional integration for flash flood detection and modelling in New Cairo City, Egypt

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