Using remote sensing data for predicting potential areas to flash flood hazards and water resources

Hussein, S. A.; Abdelkareem, Mohamed; Hussein, Raafat; Askalany, Mohamed

doi:10.1016/j.rsase.2019.100254

Cited by 16 publications

(11 citation statements)

References 23 publications

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“…Development of land and water conservation measures necessitates morphometric analysis and prioritisation of sub‐watersheds within a basin (Aher, Adinarayana, & Gorantiwar, 2014). Morphometric analysis has been extensively used for the purpose of prioritisation and assessment of watersheds susceptibility to natural hazards such as flash floods and erosion (Abuzied, Yuan, Ibrahim, Kaiser, & Saleem, 2016; Alam, Ahmed, & Sammonds, 2020; Ameri, Pourghasemi, & Cerda, 2018; Asfaw & Workineh, 2019; Charizopoulos, Mourtzios, Psilovikos, Psilovikos, & Karamotsou, 2019; Hussein, Abdelkareem, Hussein, & Askalany, 2019; Kannan, Venkateswaran, Vijay Prabhum, & Sankar, 2018; Shivhare et al, 2018; Taha et al, 2017). These studies have used the classic works of Horton (1945; 1932), Smith (1950), Strahler (1952), Miller (1953), and Schumm (1956) as a guidance.…”

Section: Introductionmentioning

confidence: 99%

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Obeidat

Awawdeh

Al‐Hantouli

2021

J Flood Risk Management

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Morphometric analysis and sub‐watersheds prioritisation were carried out for the Wadi Easal Basin, Jordan, which is characterised by a high topographic diversity. The total ranking method was applied to prioritise the sub‐watersheds in terms of susceptibility to flash flood. Results of morphometric analysis revealed that the study area is a fifth order drainage system with a dendritic drainage pattern and elongated shape. Prioritisation results showed that about 71% (15 out of 21 sub‐watersheds) of sub‐watersheds have high‐very high susceptibility to flooding, which forms about 64% of the total area of the basin. The main underlying morphometric parameters behind this are the high drainage density, stream frequency, high basin relief, basin slope, ruggedness number, and circulatory ratio, and the low value of basin shape. Overall, the basin has a rugged topography with steep slopes and high relief. Since the basin is ungauged, and no information about its past hydrological behaviour is present, the results of this study can be used as guidance for competent authorities to initialize flood mitigation or artificial groundwater recharge measures.

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Section: Introductionmentioning

confidence: 99%

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Obeidat

Awawdeh

Al‐Hantouli

2021

J Flood Risk Management

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“…Such areas also display the elevated terrains of rough topography, and such catchments were received plausible amount of precipitation. Combining areas that contain steep slopes, receive high rainfall, and have a high density of streams, presents the hazardous areas which witness flash floods [22,[62][63][64]. The presence of mountainous regions and steep slopes can cause damages to life and, therefore, economic activities.…”

Section: Discussionmentioning

confidence: 99%

Multispectral and RADAR images integration for geologic, geomorphic, and structural investigation in southwestern Arabian Shield, Al Qunfudhah area, Saudi Arabia

Abdelkareem

Bamousa

Hamimi

et al. 2020

Journal of Taibah University for Science

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In this article, Western Saudi Arabia underwent remote sensing investigation via Operational Land Imager (OLI), Advanced Land Observing Satellite/ Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR), Sentinal-1 and Shuttle Radar Topography Mission (SRTM). Imagetransformation and enhancement techniques such as Band ratios, principal component analysis (PCA), and Minimum Noise Fraction (MNF) were utilized. Applying Sentinel-1 and SRTM DEMs enhanced the perspective views of the terrain. The main results are highlighting of geologic rock units, geomorphic and structural features, and extraction and visualization of dominant lineaments. N-S foliations that are associated with folds N-S tight, isoclinal, and asymmetric (F1), NW-SE trending Najd system strike-slip faults and NNE-SSW, NE-SW NW-SE, and E-W Cenozoic faults are the major identified structural lineaments, which were analyzed and synthesized via rose diagrams. Visible Near Infra-Red/Short Wave Infra-Red (VNIR/SWIR) radar data are very important data set for water resources applications.

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“…The physical model is mainly modeled by simulating the internal physical mechanism of the runoff process. They are suitable for basins with a large number of physical parameters, but they usually need massive amounts of data to calibrate and validate the models (Huo et al 2020;Hussein et al 2019;Partington et al 2012). Physical models usually require large amounts of hydrological and meteorological data, complex mathematical knowledge and accurate knowledge of the physical processes of runoff formation (Xie et al 2019).…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

Tian

Zhang

et al. 2021

Preprint

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Accurate prediction of floods is the first step in formulating flood control strategies and reducing flood disasters. This research proposes a deep learning model based on Gate Recurrent Unit (GRU), Random Forest Algorithm (RF), Whale Optimization Algorithm (WOA) and Optimal Variational Mode Decomposition (OVMD) for flood prediction. First, the random historical time series is decomposed using OVMD. Secondly, combined with the RF feature importance measurement, select features with high importance to obtain the optimal input set. Third, use the GRU model to predict all sub-models, and use the WOA algorithm to optimize the hyperparameters in the GRU model. This study also proposes a hybrid strategy to improve the traditional WOA algorithm and enhance the optimization ability of the WOA algorithm. Finally, the prediction results of all sub-modes were aggregated to generate the final prediction result. The model was validated using data from three hydrological stations in the upper, middle and lower reaches of the Minjiang river basin in China. Through the results of the experiment, it can be seen that the proposed prediction model can effectively predict the flood time series, and has better accuracy than other models.

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Using remote sensing data for predicting potential areas to flash flood hazards and water resources

Cited by 16 publications

References 23 publications

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Morphometric analysis and prioritisation of watersheds for flood risk management in Wadi Easal Basin (WEB), Jordan, using geospatial technologies

Multispectral and RADAR images integration for geologic, geomorphic, and structural investigation in southwestern Arabian Shield, Al Qunfudhah area, Saudi Arabia

An Integrated Framework of GRU Based on Improved Whale Optimization Algorithm for Flood Prediction

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