The main purpose of the present study is to use three state-of-the-art data mining techniques, namely, logistic model tree (LMT), random forest (RF), and classification and regression tree (CART) models, to map landslide susceptibility. Long County was selected as the study area. First, a landslide inventory map was constructed using history reports, interpretation of aerial photographs, and extensive field surveys. A total of 171 landslide locations were identified in the study area. Twelve landslide-related parameters were considered for landslide susceptibility mapping, including slope angle, slope aspect, plan curvature, profile curvature, altitude, NDVI, land use, distance to faults, distance to roads, distance to rivers, lithology, and rainfall. The 171 landslides were randomly separated into two groups with a 70/30 ratio for training and validation purposes, and different ratios of non-landslides to landslides grid cells were used to obtain the highest classification accuracy. The linear support vector machine algorithm (LSVM) was used to evaluate the predictive capability of the 12 landslide conditioning factors. Second, LMT, RF, and CART models were constructed using training data. Finally, the applied models were validated and compared using receiver operating characteristics (ROC), and predictive accuracy (ACC) methods. Overall, all three models exhibit reasonably good performances; the RF model exhibits the highest predictive capability compared with the LMT and CART models. The RF model, with a success rate of 0.837 and a prediction rate of 0.781, is a promising technique for landslide susceptibility mapping. Therefore, these three models are useful tools for spatial prediction of landslide susceptibility.

A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility

There are two types of debris flows, known as Lahar and Jökulhlaup. The word Lahar is Indonesian in origin and has to do with flows that are related to volcanic activity. A variety of factors may trigger a lahar, including melting of glacial ice due to volcanic activity, intense rainfall on loose pyroclastic material, or the outbursting of a lake that was previously dammed by pyroclastic or glacial material.

What is debris flow

Suitability estimation for urban development using multi-hazard assessment map.

Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling

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.

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Erratum to: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

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.

Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models

Assessment of rockfall hazard at Al-Noor Mountain, Makkah city (Saudi Arabia) using spatio-temporal remote sensing data and field investigation

Jizan mountainous areas in Kingdom of Saudi Arabia are suffering from a variety of slope failures. Most of these failures happen due to heavy rainfalls from time to time. These landslides include rock topples, rockslides, debris flow, and some combination of these which affected many roads, highways, and buildings. The Al-Raith Road is one of these roads connecting Red Sea coast cities with Asir and Al-Hasher areas. The length of this road reaches about 45 km and it has been exposed to landslides during each heavy rain storm. One of these events happened in 24 August 2013, which caused huge debris flows that cut and damaged the road. The current research aims to evaluate the debris flow assessment along this highway using remote sensing data and field studies. According to the detailed analysis of geological and geomorphological maps, as well as field investigation, it is evident that the debris flow materials are mainly related to the different types of landslides. These landslides included rock topples ...

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Debris flow impact assessment along the Al-Raith Road, Kingdom of Saudi Arabia, using remote sensing data and field investigations

Escarpment highways, roads and mountainous areas in Saudi Arabia are facing landslide hazards that are frequently occurring from time to time causing considerable damage to these areas. Shear escarpment highway is located in the north of the Abha city. It is the most important escarpment highway in the area, where all the light and heavy trucks and vehicle used it as the only corridor that connects the coastal areas in the western part of the Saudi Arabia with the Asir and Najran Regions. More than 10 000 heavy trucks and vehicles use this highway every day. In the upper portion of Tayyah valley of Shear escarpment highway, there are several landslide and erosion potential zones that affect the bridges between tunnel 7 and 8 along the Shear escarpment Highway. In this study, different types of landslides and erosion problems were considered to access their impacts on the upper Tayyah valley's bridge along Shear escarpment highway using remote sensing data and field investigation. These landslides and erosion problems have a negative impact on this section of the highway. Results indicate that the areas above the highway and bridge level between bridge 7 and 8 have different landslides including planar, circular, rockfall failures and debris flows. In addition, running water through the gullies cause different erosional (scour) features between and surrounding the bridge piles and culverts. A detailed landslides and erosion features map was created based on intensive field investigation (geological, geomorphological, and structural analysis), and interpretation of Landsat image 15 m and high resolution satellite image (QuickBird 0.61 m), shuttle radar topography mission (SRTM 90 m), geological and topographic maps. The landslides and erosion problems could exhibit serious problems that affect the stability of the bridge. Different mitigation and remediation strategies have been suggested to these critical sites to minimize and/or avoid these problems in the future.

Assessment of impact of mass movements on the upper Tayyah valley's bridge along Shear escarpment highway, Asir region (Saudi Arabia) using remote sensing data and field investigation

Mohamed Al-Kathery

Papers

Landslide susceptibility mapping at Al-Hasher area, Jizan (Saudi Arabia) using GIS-based frequency ratio and index of entropy models

Assessment of rockfall hazard at Al-Noor Mountain, Makkah city (Saudi Arabia) using spatio-temporal remote sensing data and field investigation

Debris flow impact assessment along the Al-Raith Road, Kingdom of Saudi Arabia, using remote sensing data and field investigations

Assessment of impact of mass movements on the upper Tayyah valley's bridge along Shear escarpment highway, Asir region (Saudi Arabia) using remote sensing data and field investigation