Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling

Abdelkarim, Ashraf; Gaber, Ahmed; Youssef, Ahmed M.; Pradhan, Biswajeet

doi:10.3390/s19051024

Cited by 68 publications

(42 citation statements)

References 62 publications

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“…Case studies for the assessment of natural hazards supported by remote sensing a GIS-tools have been carried out worldwide and in Saudi Arabia [8]. However, so far, most of the case studies are related to single, extreme geohazard events, such as volcanic eruptions or flash floods [1,[29][30][31]. The cases that were studied have shown the value of remote sensing and GIS as a contribution to a systematic build-up of a natural hazard database according to standardized approaches in Saudi Arabia, including all kinds of geohazards and potential cascading effects.…”

Section: Goals Of This Studymentioning

confidence: 99%

Remote Sensing and GIS Contribution to a Natural Hazard Database in Western Saudi Arabia

Theilen‐Willige

Wenzel

2019

Geosciences

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The most frequent disasters in Western Saudi Arabia are flash floods, earthquakes and volcanism, especially submarine volcanism potentially causing tsunamis in the Red Sea and submarine mass movements, dust storms and droughts. As the consequences and effects of the climate change are expected to have an increasing impact on the intensity and occurrence of geohazards as flash floods, length of drought periods, or dust storms, the systematic, continuous monitoring of these hazards and affected areas using satellite data and integration of the results into a geographic information systems (GIS) database is an important issue for hazard preparedness and risk assessment. Visual interpretation and digital image processing of optical aerial and satellite images, as well as of radar images, combined with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Shuttle Radar Topographic Mission (SRTM) and Advanced Land Observing Satellite (ALOS) PALSAR DEM data are used in this study for the mapping and inventory of areas prone to geohazards, such as flash floods or tsunami flooding. Causal or critical environmental factors influencing the disposition to be affected by hazards can be analyzed interactively in a GIS database. How remote sensing and GIS methods can contribute to the detection and continuously, standardized monitoring of geohazards in Western Saudi Arabia as part of a natural hazard geodatabase is demonstrated by several examples, such as the detection of areas prone to hydrological hazards, such as flash floods causing flooding of roads and settlements, the outlining of coastal areas of the Red Sea prone to tsunami flooding and storm surge, the mapping of traces of recent volcanic activity, and of fault/fracture zones and structural features, especially of ring structures.

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Section: Goals Of This Studymentioning

confidence: 99%

Remote Sensing and GIS Contribution to a Natural Hazard Database in Western Saudi Arabia

Theilen‐Willige

Wenzel

2019

Geosciences

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“…Geographic information system (GIS)-based multi-criteria decision analysis tools, such as analytic hierarchy process, have been used for flood hazard zonation in Tucumán Province, Argentina [17], Eldoret Municipality, Kenya [2], and Palilula municipality, Serbia [18]. A combination of hydraulic and hydrological models, e.g., HEC-RAS and the Watershed Modeling System (WMS), has been used for flood mapping in Volos City, Greece [19] and Tabuk City, Saudi Arabia [20]. The latest research is using machine learning models to predict urban flood inundation.…”

Section: Introductionmentioning

confidence: 99%

Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

Rahmati

Darabi

Haghighi

et al. 2019

Water

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Floods are the most common natural disaster globally and lead to severe damage, especially in urban environments. This study evaluated the efficiency of a self-organizing map neural network (SOMN) algorithm for urban flood hazard mapping in the case of Amol city, Iran. First, a flood inventory database was prepared using field survey data covering 118 flooded points. A 70:30 data ratio was applied for training and validation purposes. Six factors (elevation, slope percent, distance from river, distance from channel, curve number, and precipitation) were selected as predictor variables. After building the model, the odds ratio skill score (ORSS), efficiency (E), true skill statistic (TSS), and the area under the receiver operating characteristic curve (AUC-ROC) were used as evaluation metrics to scrutinize the goodness-of-fit and predictive performance of the model. The results indicated that the SOMN model performed excellently in modeling flood hazard in both the training (AUC = 0.946, E = 0.849, TSS = 0.716, ORSS = 0.954) and validation (AUC = 0.924, E = 0.857, TSS = 0.714, ORSS = 0.945) steps. The model identified around 23% of the Amol city area as being in high or very high flood risk classes that need to be carefully managed. Overall, the results demonstrate that the SOMN model can be used for flood hazard mapping in urban environments and can provide valuable insights about flood risk management.

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“…More than 60% of total economic and human losses took place during the period from 1950 to 2016 in Asia. In Saudi Arabia, floods were characterized as one of the most frequent and devastating natural disasters [9].…”

Section: Introductionmentioning

confidence: 99%

“…The geographic information system (GIS) and remote sensing (RS) applications have contributed significantly to the analysis of natural hazards [39,40]. Over the past few decades, researchers have been involved in developing different methods and models for natural hazard mapping using RS and GIS technologies [41,42], including frequency [43,44], analytical hierarchy [45], fuzzy logic [46], logistic regression [47], artificial neural networks [48][49][50], evidence weights [51], multi-standard resolution [52,53], and hydraulic modeling, which are essential for flood risk management and mitigation [9,[54][55][56][57][58][59].…”

Section: Introductionmentioning

confidence: 99%

“…Martin and others [68] used a flood risk index, based on remote sensing and GIS technology, to describe the risk of flooding in areas where data are scarce, taking into account slope, flow accumulation, drainage network density, distance from drainage channel, geology, land use/cover, rainfall intensity, and the factors causing floods in order to produce a flood risk map. The study of Karim et al [9] applied a new approach to identify urban areas exposed to the risk of the floods in the city of Tabuk, with the recommendation of utilizing a mechanism for flood prevention and city protection through the integration of hydrological modeling.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Remote Sensing and Hydrologic Modeling to Assess the Impact of Land-Use Changes on the Increase of Flood Risk: A Case Study of the Riyadh–Dammam Train Track, Saudi Arabia

Abdelkarim¹,

Gaber

Alkadi

et al. 2019

Sustainability

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The current study aimed at measuring the impact of the change in land-use morphology on the increase of flood risk through its application to the case of the Riyadh–Dammam train track in Saudi Arabia. The track was exposed to drift on 18 February 2017, over a length of 10 km, in the district of Dhahran in the capital of Dammam. Flooding caused the train to drift off its track and resulted in damage to lives, property, and infrastructure. This resulted from human interventions in the preplanning land uses and changing the morphology of the land by encroaching on the valleys, which resulted in the loss of the environmental and ecological balance in the study area. In order to achieve these goals, land-use changes in the study area were monitored by analyzing successive images from the GEO-I-1 satellite with a resolution of 60 cm for the years 2011 and 2017, before and after the train drift, using the maximum likelihood classification process provided in ERDAS IMAGINE 2016. GIS was used in the processing of 1 m digital elevation models to extract the morphological changes of the wadies between 2011 and 2017. A hydrological model (HEC–HMS) was used in calculating the (flood) hydrograph curve of the wadies basins and estimating the calculation of flood water quantities and its flow rates based on the Soil Conservation Services (SCS) Unit Hydrograph Method. Rain depth was analyzed and estimated for different return periods. The HEC–RAS hydraulic modeling program was employed in developing a 2D model to calculate the velocity, depth, and spread of the flood in order to apply the risk matrix method.

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Flood Hazard Assessment of the Urban Area of Tabuk City, Kingdom of Saudi Arabia by Integrating Spatial-Based Hydrologic and Hydrodynamic Modeling

Cited by 68 publications

References 62 publications

Remote Sensing and GIS Contribution to a Natural Hazard Database in Western Saudi Arabia

Remote Sensing and GIS Contribution to a Natural Hazard Database in Western Saudi Arabia

Urban Flood Hazard Modeling Using Self-Organizing Map Neural Network

Integrating Remote Sensing and Hydrologic Modeling to Assess the Impact of Land-Use Changes on the Increase of Flood Risk: A Case Study of the Riyadh–Dammam Train Track, Saudi Arabia

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