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Jeddah, Saudi Arabia

Youssef A.M.,Sohag University | El-Kaliouby H.M.,Sultan Qaboos University | El-Kaliouby H.M.,National Research Center of Egypt | Zabramawi Y.A.,Applied Geology Sector
Journal of Applied Geophysics

Different types of sinkholes have been recognized in the Kingdom of Saudi Arabia. The sinkholes are of various sizes and shapes and occur at different depths. Their presence may create a direct risk to the infrastructural facilities such as urban areas, roads, future development areas, and farmlands. Recently, more than eight sinkholes have collapsed to the west of the Al Khafji and An Nu'ayriyah areas (northern eastern side of the Kingdom). Geologically, the area is composed of Dammam, Hadrukh, and Dibdibba formations. The essence of this paper is to integrate remote sensing analysis throughout surface features with the geophysical studies to detect the subsurface sinkholes. Our findings show that there are possible correlations between the clear circular features (ring structures) detectable in remote sensing images and geophysical observations (using two electrical resistivity surveys), indicating unconsolidated material in the subsurface (karst phenomena). © 2012 Elsevier B.V. Source

Youssef A.M.,Sohag University | Pradhan B.,University Putra Malaysia | Al-Harthi S.G.,Applied Geology Sector
Arabian Journal of Geosciences

Samma escarpment road is located in Asir Region, Saudi Arabia. It is located NW of Abha city. This escarpment road represents a major corridor in the area which connects different cities and touristic resorts in the region. It is descended from Sudah plateau at about 2700 m above sea level (asl) toward Wadi al Aws at about 1500 m asl. The total length of the road section is about 8 km which is passing through a highly mountainous area characterized by a complex geological and structural elements. This road has been exposed to frequent slope failures from time to time due to various factors such as intense rain storms, different geological and structural elements (weak rocks, shear zones, and faults), road characteristics (different horizontal/vertical curvatures and narrow road section), and human activities (uncontrolled rock cuts). Many sliding events have been documented along this escarpment road, particularly during and following rainstorms. The purpose of this paper is to evaluate the geology and structures by applying the rock mass rating (RMR) and slope stability (structurally controlled failures) along the Samma escarpment road. The stability analysis was performed using two Dips and RockPack III programs with the help of RocLab software. The Summa escarpment rock cuts were classified into 51 stations which were investigated in detail. Results indicated that most rock stations are poor quality, and 31, 32, and 41 stations are stable, 4, 6, and 7 stations are marginally stable, and 16, 13, and 3 stations are potentially unstable due to planar, wedge, and toppling failures, respectively. Finally, different recommendations and remediation methods were suggested as mitigation measures. © 2014, Saudi Society for Geosciences. Source

Youssef A.M.,Sohag University | Pourghasemi H.R.,Shiraz University | Pourtaghi Z.S.,University of Yazd | Al-Katheeri M.M.,Applied Geology Sector

The purpose of the current study is to produce landslide susceptibility maps using different data mining models. Four modeling techniques, namely random forest (RF), boosted regression tree (BRT), classification and regression tree (CART), and general linear (GLM) are used, and their results are compared for landslides susceptibility mapping at the Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslide locations were identified and mapped from the interpretation of different data types, including high-resolution satellite images, topographic maps, historical records, and extensive field surveys. In total, 125 landslide locations were mapped using ArcGIS 10.2, and the locations were divided into two groups; training (70 %) and validating (25 %), respectively. Eleven layers of landslide-conditioning factors were prepared, including slope aspect, altitude, distance from faults, lithology, plan curvature, profile curvature, rainfall, distance from streams, distance from roads, slope angle, and land use. The relationships between the landslide-conditioning factors and the landslide inventory map were calculated using the mentioned 32 models (RF, BRT, CART, and generalized additive (GAM)). The models’ results were compared with landslide locations, which were not used during the models’ training. The receiver operating characteristics (ROC), including the area under the curve (AUC), was used to assess the accuracy of the models. The success (training data) and prediction (validation data) rate curves were calculated. The results showed that the AUC for success rates are 0.783 (78.3 %), 0.958 (95.8 %), 0.816 (81.6 %), and 0.821 (82.1 %) for RF, BRT, CART, and GLM models, respectively. The prediction rates are 0.812 (81.2 %), 0.856 (85.6 %), 0.862 (86.2 %), and 0.769 (76.9 %) for RF, BRT, CART, and GLM models, respectively. Subsequently, landslide susceptibility maps were divided into four classes, including low, moderate, high, and very high susceptibility. The results revealed that the RF, BRT, CART, and GLM models produced reasonable accuracy in landslide susceptibility mapping. The outcome maps would be useful for general planned development activities in the future, such as choosing new urban areas and infrastructural activities, as well as for environmental protection. © 2015 Springer-Verlag Berlin Heidelberg Source

Youssef A.M.,Sohag University | Al-Kathery M.,Applied Geology Sector | Pradhan B.,University Putra Malaysia
Geosciences Journal

Mountain areas in the southern western corner of the Kingdom of Saudi Arabia frequently suffer from various types of landslides due to rain storms and anthropogenic activities. To resolve the problem related to landslides, landslide susceptibility map is important as a quick and safe mitigation measure and to help making strategic planning by identifying the most vulnerable areas. This paper summarizes findings of landslide susceptibility analysis at Al-Hasher area, Jizan, KSA, using two statistical models: frequency ratio and index of entropy models with the aid of GIS tools and remote sensing data. The landslide locations (inventory map) were identified in the study area using historical records, interpretation of high-resolution satellite images that include Geo-Eye in 2.5 m and Quickbird in 0.6m resolution, topographic maps of 1:10,000 scale, and multiple field investigations. A total of 207 landslides (80% out of 257 detected landslides) were randomly selected for model training, and the remaining 50 landslides (19%) were used for the model validation. Ten landslide conditioning factors including slope angle, slope-aspect, altitude, curvature, lithology, distance to lineaments, normalized difference vegetation index (NDVI), distance to roads, precipitation, and distance to streams, were extracted from spatial database. Using these conditioning factors and landslide locations, landslide susceptibility and weights of each factor were analyzed by using frequency ratio and index of entropy models. Our findings showed that the existing landslides of high and very high susceptibility classes cover nearly 80.4% and 79.1% of the susceptibility maps produced by frequency ratio and index of entropy models respectively. For verification, receiver operating characteristic (ROC) curves were drawn and the areas under the curve (AUC) were calculated for success and prediction rates. For success rate the results revealed that for the index of entropy model (AUC = 77.9%) is slightly lower than frequency ratio model (AUC = 78.8%). For the prediction rate, it was found that the index of entropy model (AUC = 74.9%) is slightly lower than the frequency ratio model (AUC = 76.7%). The landslide susceptibility maps produced from this study could help decision makers, planners, engineers, and urban areas developers to make suitable decisions. © 2015, The Association of Korean Geoscience Societies and Springer-Verlag Berlin Heidelberg. Source

Youssef A.M.,Sohag University | Pradhan B.,University Putra Malaysia | Al-Kathery M.,Applied Geology Sector | Bathrellos G.D.,National and Kapodistrian University of Athens | Skilodimou H.D.,National and Kapodistrian University of Athens
Journal of African Earth Sciences

Rockfall is one of the major concerns along different urban areas and highways all over the world. Al-Noor Mountain is one of the areas that threaten rockfalls to the Al-Noor escarpment track road and the surrounding urban areas. Thousands of visitors and tourisms use the escarpment track road to visit Hira cave which is located at the top of Al-Noor Mountain. In addition, the surrounding urban areas of Al-Noor Mountain are continuously spreading over the recent years. The escarpment track road and the surrounding urban areas are highly vulnerable and suffers from recurrent rockfall mostly in the rainy season. The steep and highly jointed slope along the different faces of the mountain makes these zones prone to failure due to different actions such as weathering, erosion and anthropogenic effect. Therefore, an attempt has been made in this study to determine the Al-Noor cliff stability, by identifying the unstable areas, and to apply the rockfall simulations. A combination of remote sensing, field study and 2D computer simulation rockfall program were performed to assess surface characteristics of the cliff faces. Bounce height, total and translational kinetic energy, translational velocity, and number of blocks have been estimated. Different unstable zones along the Al-Noor Mountain and escarpment track road were determined using filed investigation and remote sensing based image analysis. In addition the rockfall simulation analysis indicated that rockfall in zone 1 and zone 2 of the Al-Noor Mountain may reach the urban areas, whereas rockfall in zone 3 will not reach the urban areas, and rockfalls along the Al-Noor escarpment track road will have highly impact on the tourists. Proper preventive measures are also suggested to arrest the movement of falling rocks before reaching the urban areas and the Al-Noor escarpment track road. If proper care is taken, then further uncertain rockfall hazards can be prevented. © 2014 Elsevier Ltd. Source

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