Advanced remote sensing techniques in flash flood delineation in Tabuk City, Saudi Arabia

2020 ◽  
Vol 103 (3) ◽  
pp. 3401-3413
Author(s):  
Mohamed Elhag ◽  
Shemsu G. Abdurahman
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Flash Flood ◽  
Remote Sensing Techniques

scholarly journals Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques

2019 ◽  
Vol 12 (1) ◽  
pp. 106 ◽  
Author(s):  
Romulus Costache ◽  
Quoc Bao Pham ◽  
Ehsan Sharifi ◽  
Nguyen Thi Thuy Linh ◽  
S.I. Abba ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information Gain ◽  
Flash Flood ◽  
Slope Angle ◽  
Training Data ◽  
Operating Characteristics ◽  
Predictive Capability ◽  
Data Set ◽  
Flood Susceptibility ◽  
Remote Sensing Techniques

Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.

scholarly journals Flash-Flood Potential Assessment by Integrating the Remote Sensing Data and GIS with Reference to Adam Area, Western Saudi Arabia

2020 ◽  
Vol 9 (11) ◽  
pp. 78-84
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Statistical Data ◽  
Flash Flood ◽  
Remote Sensing Data ◽  
Curve Number ◽  
Development Planning ◽  
Return Periods ◽  
Development Plans ◽  
Gis Environment

Heavy rainstorms are common occurrences in the Western mountainous region of Saudi Arabia that results in hazardous floods damaging the infrastructure and development plans. Severe rainstorms and heavy showers cause instant flash floods that result in major damage of properties and loss of human lives. Therefore, it becomes crucial during the development planning that floods are accurately analyzed. For the calculation and spatial mapping of flood features, an integrated remote sensing and GIS methodology has been formed. This new methodology makes use of various landscape, metrological, geological, and land use datasets in a GIS environment by employing the technique of Curve Number (CN) of flood modeling for unrestricted dry catchments. The prediction of rainfall depths for 50 and 100-years are 73.6 and 82.3 mm respectively. 4.3679 and 8.0605 million cubic meters are the flood volumes for 50- and 100-year return periods. Moreover, the flood’s statistical data like the depth and volume of runoff is added in GIS layers’ attribute tables so that all results are collected in the same environment. The application of advanced methodology aids in providing exact estimations and digital results. Moreover, it is economical and can be re-operated in different circumstances as well.

An integrated technique for delineating groundwater contaminated zones using geophysical and remote sensing techniques: a case study of Al-Quway’iyah, central Saudi Arabia

2014 ◽  
Vol 51 (8) ◽  
pp. 797-808 ◽  
Author(s):  
Sayed S.R. Moustafa ◽  
Nassir Alarifi ◽  
Muhammad Naeem ◽  
Muhammad Kamran Jafri
Keyword(s):  
Remote Sensing ◽  
Saudi Arabia ◽  
Waste Disposal ◽  
Health Hazard ◽  
Seismic Refraction ◽  
Disposal Site ◽  
Waste Disposal Site ◽  
Remote Sensing Techniques ◽  
Integrated Technique ◽  
Elevation Model

Geophysical and remote sensing techniques were carried out to raise the groundwater quality and delineate expected contaminated zones near an open-waste disposal site in Al-Quway’iyah, central Saudi Arabia. An extracted digital elevation model (DEM) from very high resolution (VHR) satellite images was used to define the surface lineaments and prevailing flow path directions present in the study area. Remote sensing results indicated that groundwater in the Al-Quway’iyah metropolitan area flows through a complex network of interconnected fractures, which are controlled by the regional geological and structural settings of the area. Seismic refraction profiling was applied to delineate the depth to the groundwater table and bedrocks, and to locate those faults that may provide pathways to contaminants associated with the open-waste disposal site in the survey area. The results showed that possible subsurface groundwater contamination zones are mainly associated with weaker–fractured zones underlying the surface lineaments. This survey suggests that adequate integration of remote sensing and seismic refraction data can be applied to map spatial distribution of contaminants efficiently. It can facilitate future studies to be conducted for environment and human health hazard appraisal.

Monitoring Urban and Land Use Changes in Al-Kharj Saudi Arabia using Remote Sensing Techniques

2015 ◽  
pp. 515-523 ◽  
Author(s):  
Osama S. Algahtani ◽  
Ahmed S. Salama ◽  
Abdullah M. Iliyasu ◽  
Belal A. Selim ◽  
K. Kheder
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Saudi Arabia ◽  
Land Use Changes ◽  
Remote Sensing Techniques
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