Landslide-prone Areas Research Articles

Determination and Evaluation of Landslide-Prone Regions of Isparta (Turkey): An Urban Planning View

Landslides represent a significant hazard affecting human life and property and threaten the sustainability of human settlements. They are among the most critical threats after earthquakes in Turkey. In 2020, 107 landslide events occurred in Turkey. Implementing proper policies, strategies, and tools for landslide risk mitigation remains challenging for urban planning institutions. In the plan preparation phase, urban planners and plan-making authorities, agencies, or institutions may overlook landslide risks due to a lack of data or related studies. Therefore, this article aims to develop a novel spatial analysis for identifying landslide-prone areas at the provincial level from an urban planning perspective. The analysis is compared to the approved upper-scale plan, and the results are used to build a more robust understanding of landslide risks for sustainable urban development. Isparta Province is selected as the study area, as it has active landslide areas. The methods used include a literature survey including internet sources, newspapers, plans, articles, and other research projects and a case study utilizing a GIS spatial analysis. The spatial analysis using GIS is based on three landslide inventories currently available in Turkey. This spatial analysis is developed to determine landslide-prone regions by considering thematic layers, triggering factors, and vulnerability inputs. As a result of this analysis, five landslide-prone areas in Isparta Province are determined. When these regions are compared to the upper-scale plan that covers the province, it is found that land use and planning decisions have neglected landslide risks, and urban areas are at high landslide risk. Several specific principles and strategies, such as a spatial inventory database and an integrated planning approach including landslide-prone areas, are stated with a reliable spatial analysis to assess landslide-prone areas on a regional scale, which can be applied later in any city and region of Turkey.

Pemetaan Daerah Rawan Bencana Tanah Longsor di Wilayah Kecamatan Silo Kabupaten Jember

Silo District, Jember Regency is an area with a mountainous topography dominated by relatively steep slopes. Many landslides occurred in the Silo District area. Based on the results of observations, in 2022 to July there have been 8 landslides in the Silo District area. Landslide disasters can be minimized by mapping landslide-prone areas. The mapping method used is a weighted overlay method for each parameter. The landslide parameters used in this study are: parameters of soil depth, rainfall, slope, soil type and land use. The final results of data processing for each parameter in this study will produce a map of landslide-prone areas in Silo District, Jember Regency. . Based on the research results, the landslide vulnerability classes are grouped into four classifications, namely low, medium, high and very high. The results showed that the Silo District was an area with high vulnerability (54%), medium vulnerability (37%), very high vulnerability (9%) and low vulnerability (1%). Mulyorejo Village is the village with the widest very high vulnerability classification in Silo District.

Numerical stability analysis and geotechnical investigation of landslide prone area (the case of Gechi district, Western Ethiopia)

The primary route linking Tobba, Gechi and Beddele has experienced landslides at three locations, causing significant disruption to traffic. The juniper trees and electric poles have suffered damage, and the extension lines are at risk. This investigation aims to identify the geotechnical conditions, soil type, and their role in landslide initiation, slope stability analysis, and measures to reduce the risk of landslides. The study employed fieldwork, laboratory analysis, and software analysis. The fieldwork involved measuring the slope geometry and landslide magnitude, as well as geophysical resistivity to determine the soil type and thickness of the weak zone. Soil samples were taken and tested for grain size analysis, Atterberg limit, water content, unit weight of soil, specific gravity, and shear strength parameters, following ASTM procedures. Plaxis v 8.6 software was used to supplement the slope stability analysis. The results indicate that the soil in the study area is classified as MH- elastic silt and elastic silt with sand (S1BH3 and S2BH1) behaving as sand. The fine-grained soil constitutes ≥50% of the soil, which significantly influences the soil properties. The numerical value of the factor of safety (FOS) from slope stability analysis decreases as the phreatic level increases. Electrical resistivity profiling indicated that there is a change in soil matrix and grainsize distribution. The dry unit weight varies from 6.28KN/m3 to 43.23KN/m3 and the slope angle varies from 14.09° to 14.62°. The cohesion of the soil varies from 40.71KN/m2 to 67.02KN/m2 and the angle of internal friction varies from 15.12° to 26.62°. The landslides were triggered by heavy rainfall, and the primary factors affecting slope stability are soil type and characteristics, surface and groundwater, and slope steepness. To maintain the slope's stability, it is recommended to modify the slope geometry, construct sheet piles and retaining walls, provide adequate drainage structures, and afforest the area with long-rooted grass and trees.

Ultrahigh-resolution 9C seismic survey in a landslide prone area in southwest of Sweden

SUMMARY We studied the benefits of a nine-component (9C) seismic survey over a landslide-prone area in southwest of Sweden to retrieve ultrahigh-resolution shear wave reflection images of the subsurface as well as crucial information on physical properties of the sediments. A complete, 1 m shot and receiver spacing, multicomponent 2-D seismic profile was acquired using three-component microelectromechanical-system-based landstreamer receivers, and a 5-kg sledgehammer strike in vertical and horizontal orientations as three-component seismic source. Given the rich number of shear wave reflections observed on all the 9C data, the processing work focused on their retrievals. It revealed three distinct reflections, two of which associated with coarse-grained materials and one with an extremely undulating bedrock surface. Given the extremely slow shear wave velocities on the order of 60–100 m s−1, we obtained ultrahigh-resolution shear wave sections avoiding temporal and spatial aliasing. Imaging results suggest vertical-source and horizontal-radial receiver (V–HR), and horizontal-transverse source–receiver orientations (HT–HT) provided the most optimum images of the subsurface. A non-hyperbolic algorithm was applied to the normal-moveout corrections justified by the traveltime differences of the bedrock reflection in different shear wave sections. The improved images by incorporating the anisotropy term suggest that the data set reveals moderate shear wave anisotropy along some portions of the profile. The Vp/Vs ratio obtained by using bedrock reflection in P- and S-wave sections suggests values ranging 10–16, which implies high water content. Areas with lower Vp/Vs coincides with greater anisotropic parameters and this can indicate disturbed clays or presence of sensitive clays.

Mapping of Landslide Prone Areas in Huamual Sub-District, Seram Bangian Barat Regency, Indonesia

This research aims to map landslide-prone areas in Huamual Sub-district, West Seram Regency, Indonesia. Through the collection and analysis of geospatial data, including characteristics of slope, land elevation, geology, rainfall, land cover and distance from active faults, this study successfully identified areas with high potential landslide risk. The results showed that the area in low landslide class has an area of 5,076.67 ha, the area in medium class has an area of 20,979.79 ha and the area in high landslide prone class has an area of 7,430.88 ha. The results of this study provide an important contribution in landslide risk mitigation planning, through identification of zones that need special attention, safer spatial planning, and more effective early warning system. This research provides a strong scientific basis for the government and other stakeholders to take appropriate preventive measures, so as to improve public safety and protect important assets from potential landslide hazards in Huamual Sub-district area.

Landslide susceptibility mapping of Al Taif urban area, Saudi Arabia, using remote sensing data and microtremor measurements: integrated approach

Many people are killed by landslides due to earthquakes or severe rain, and structures and facilities built on or near slopes sustain significant damage. Such landslides on naturally occurring slopes can be large enough to utterly destroy towns or communities. Based on remote sensing and microtremor data, the area around Al Taif has been evaluated for its susceptibility to landslides. Digital elevation model (DEM), slope angle, and slope aspect thematic layers were used to depict remote sensing data. The landslide susceptibility was extracted from remote sensing thematic data. The elevations of the Al Taif area, which range from 832 to 2,594 m amsl, were identified based on the DEM. Al Taif’s slope angles range from 0° to 67.3° degrees. Nearly flat (0° to 4.75°), moderate (4.75° to 11.1°), steep (11.2° to 29.1°), and very steep slope (≤29.1°) are the different classifications for the slope. Additionally, measurements of the microtremor have been taken at 42 locations throughout the region. The horizontal-to-vertical spectral ratio (HVSR) approach was used to process and analyze microtremor data in order to determine the resonance frequency and H/V amplification factor. The findings show that, while the amplification factor varies from 1.17 to 9.28, the dominant frequency values fall between 0.3 and 12.75 Hz. To determine the frequency, amplitude, and azimuthal site response, 11 sites were eventually chosen. Furthermore, the direction of the site response in the sliding areas was parallel to the landslide directional response, indicating that the site response direction tracked the landslide direction. Practical approval of the study’s findings has been given at a number of locations by field measurements at some of the Taif urban area’s most recent landslide occurrence areas. These findings show that the integration between remote sensing and microtremor measurements is a useful tool for pinpointing landslide-prone areas, which helps to lessen the danger to people’s lives and property. This susceptibility zonation applied to the Al Taif area has produced a good match between the distribution of the reported landslides and the zones of high susceptibility. To define the general trend and geographic distribution of potentially unstable slopes and landslide potential zones, this study’s findings must be used as a guide.

EVALUATING THE INFLUENCE OF SPATIAL RESOLUTION ON LANDSLIDE DETECTION: A CASE STUDY IN THE CARLYON BEACH PENINSULA, WASHINGTON

Abstract. Landslides are geological events in which masses of rock and soil slide down the slope of a mountain or hillside. They are influenced by topography, geology, weather, and human activity, and can cause extensive damage to the environment and infrastructure, as well as delay transportation networks. Therefore, it is imperative to detect early-warning signs of landslide hazards as a means of prevention. Traditional landslide surveillance consists of field mapping, but the process is costly and time consuming. Modern landslide mapping uses Light Detection and Ranging (LiDAR) derived Digital Elevation Models (DEMs) and sophisticated algorithms to analyze surface roughness and extract spatial features and patterns of landslide and landslide-prone areas. This study follows a previous study performed that demonstrated that it is possible to detect unstable terrain using algorithmic mapping techniques. The focus of this study is to show how spatial resolution can influence the accuracy of the classification results. The DEM data was resampled from 6 to 12, 24, 48 and 96 ft spatial resolution. The surface feature extractors employed (local topographic range, local topographic variability, slope, and roughness) are fused and analyzed simultaneously by applying k-means and Gaussian Mixture Model (GMM) clustering methods. When compared with the detailed, independently compiled landslide reference map, our data shows a decrease in performance as spatial resolution decreases. These results suggest that spatial resolution does impact the performance of landslide classification.

Characteristics of Slope Failures Seen outside the Designated Landslide-prone Areas

Characteristics of Slope Failures Seen outside the Designated Landslide-prone Areas

Landslide-prone areas in Makale Selatan with the analytical hierarchy process method

Landslides are one of the most common natural disasters in Indonesia. The Tana Toraja area has not been spared landslides, especially in the Makale Selatan district. Makale Selatan is one of the supply areas for agricultural products in South Sulawesi. Therefore it is necessary to map landslide-prone areas to support landslide disaster mitigation. This study aims to map landslide-prone regions and determine the factors most influencing landslide events in Makale Selatan. This study uses an Analytical Hierarchy Process (AHP) method with a weighting factor. The parameters, namely; slope, rainfall, land use, soil texture, soil type, and rock formations. The results showed that the incidence of landslides in Makale Selatan was strongly influenced by the slope, with the highest weighting factor with a weight of 0.410 (Score of 41%). Rainfall and land use with the same weight of 0.170 (Score of 17%), then the parameters of soil type and soil texture also with the exact weight is 0.096 (score of 9.6%), and the rock formation parameters with a weight of 0.058 (score 5.8%). The level of landslide-prone areas in the study area namely; 12.46% for the not prone area, 51.72% for slightly prone, prone areas 25.72%, and very prone was 10.11% for the total study area. The not-prone village is Tiromanda, and the most prone village is Lembang Randan Batu. The slope parameter is the parameter that most influences landslides in Makale Selatan. Land use for agriculture must be mechanically conserved to reduce the slope angle and prevent landslides.

Spatial Analysis of Landslide Prone Areas in Tidore Island Indonesia

The topographic condition of Tidore Island, where 50% of the area is on a steep slope, makes Tidore Island prone to landslides during the rainy season. This research aims to spatially identify landslide prone areas in Tidore Island using slope morphology method. SMORPH method is conducted by identifying and classifying potential landslide areas based on slope shape and slope angle matrix. The result of this study shows that high landslide potential area is 2,569.42 ha, medium potential area is 3,296.99 ha, low potential area is 4,778.46 ha, and very low potential area is 1,126.55 ha. Among the sub-districts in Tidore Island, North Tidore has the largest presentation area of 892.48 ha. It can be concluded that areas with steep slopes and convex or concave slope shapes have higher landslide potential. The findings of this research are expected to help local government in future spatial planning efforts for landslide mitigation.

Acquisition of high-resolution topographic information in forest environments using integrated UAV-LiDAR system: System development and field demonstration

High-resolution topographic information of landslide-prone areas plays an important role in accurate prediction and characterization of potential landslides and mitigation of landslides-associated hazards. This study presents an advanced geomorphological surveying system that integrates the light detection and ranging (LiDAR) with an unmanned aerial vehicle (UAV), a multi-rotor aerial vehicle in specific, for prediction, monitoring and forensic analysis of landslides, and for maintenance of debris-flow barriers. The test-flight over a vegetated area demonstrates that the integrated UAV-LiDAR system can provide high-resolution, three-dimensional (3D) LiDAR point clouds below canopy and vegetation in forest environments, overcoming the limitation of aerial photogrammetry and terrestrial LiDAR platforms. An algorithm is suggested to delineate the topographic information from the acquired 3D LiDAR point clouds, and the accuracy and performance of the developed UAV-LiDAR system are examined through field demonstration. Finally, two field demonstrations are presented: the forensic analysis of the recent Gokseong landslide event, and the sediment deposition monitoring for debris-flow barrier maintenance in South Korea. The developed surveying system is expected to contribute to geomorphological field surveys in vegetated, forest environments, particularly in a site that is not easily accessible.

Short-Term Rainfall Forecasting by Combining BP-NN Algorithm and GNSS Technique for Landslide-Prone Areas

Extreme rainfall is the main contributing factor to landslides. Therefore, it is of great significance to monitor and forecast short-term rainfall in landslide-prone areas. However, the spatial scale of landslide-prone areas is small, and traditional numerical forecast models have difficulty in accurately forecasting rainfall on this scale. To solve the above problem, this study proposes a short-term rainfall forecasting method for landslide-prone areas by combining the back-propagation neural network (BP-NN) algorithm and global navigation satellite system (GNSS) observations to achieve accurate short-term rainfall forecasting in landslide-prone areas. Firstly, a high-precision atmospheric weighted-average temperature (Tm) model is established using radiosonde data to obtain high-precision precipitable water vapor (PWV) estimates. Secondly, the BP-NN algorithm is introduced, and the GNSS-derived PWV, temperature and pressure from a meteorological station, and rainfall for the previous and next hour are used as input parameters to establish a BP-NN-based rainfall forecast model. As an illustrative case, experiments are conducted in a landslide-prone area in Yunnan Province using data from 15 GNSS stations and the corresponding meteorological station. Statistical results show that the established regional Tm model has high accuracy, with an average root mean square (RMS) and bias of 3 K and 0.15 K, respectively. In addition, the short-term rainfall forecast model based on the BP algorithm achieves a true detection rate of up to 93.70% and a false forecast rate of as low as 38.30%, which is significant for short-term rainfall forecasting in landslide-prone areas.

Landslide susceptibility mapping in Badakhshan province, Afghanistan: a comparative study of machine learning algorithms

This research aimed to assess landslide susceptibility in the Badakhshan province of Afghanistan, an area highly susceptible to landslides due to its complex topography and geological conditions. Three distinct machine learning (ML) models, namely the Generalized Linear Model (GLM), Maximum Entropy (ME), and Random Forest (RF), were employed to identify the key contributing factors to landslide occurrences in the study region. The dataset used in this study consisted of landslide conditioning factors and a landslide inventory map. The conditioning factors encompassed lithology, soil type, plane curvature, profile curvature, elevation, slope, aspect, precipitation, land use/land cover (LULC), distance to fault, river, road, Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Standardized Precipitation Index (SPI). The landslide inventory map contained 177 landslide locations and 65 non-landslide points obtained from Google Earth. Each machine learning (ML) model was trained and implemented independently using 70% of the training data, with the results validated against the remaining 30% of the landslide inventory dataset. Ensemble results from GLM, ME, and RF were obtained using the median approach. All three models exhibited consistent performance and identified similar landslide-prone areas. Among the various factors studied, proximity to rivers emerged as the most influential factor contributing to landslides, followed by the distance to roads and slope gradient. The study revealed that the districts of Argo and Yaftali Sufla, near Faizabad, were identified as particularly susceptible to landslides, especially in the vicinity of large valleys. Out of the total study area of 3086.4 km2, ∼2162 km2 were deemed relatively safe from landslides, while 149 km2 (representing 4.8% of the study region) were identified as highly susceptible to landslides. Area Under the Receiver Operating Characteristic Curve (AUC) and Root Mean Square Error (RMSE) statistics were used to evaluate the performance of the machine learning (ML) algorithms. The RF and ME models demonstrated the highest performance levels. This research contributes to our understanding of landslide susceptibility in Badakhshan province and can aid in implementing effective landslide risk management strategies in the region.

Landslide susceptibility mapping of Penang Island, Malaysia, using remote sensing and multi-geophysical methods

Malaysia is one of the countries in the world experiencing landslides yearly due to natural events and human activities. Penang Island is Malaysia’s second most developed state and the largest by population. It is prone to landslides with devastating environmental impacts. Hence, the need to characterize its near-surface soil-rock conditions. This study uses remotely sensed data via frequency ratio (FR) techniques to identify landslide-prone areas based on different categories of landslide causative factors. To further understand the conditions and hydrodynamics of the soil-rock profiles causing landslides, electrical resistivity tomography and seismic refraction tomography were carried out at a landslide-suspected section in the study area. Also, the satellite-derived Bouguer gravity anomaly modeling was performed to map the varied gravity anomalies associated with landslide-triggering factors in lithologic units. The multi-geophysical models offer strongly correlated results with the causative remote sensed maps and the landslide susceptibility index (LSI) map. The likelihood of landslides occurring in the area, as suggested by the area under curve modeling of LSI data, yielded a high predicted success rate of 83.47%. Hence, prospective landslides were identified at the hilly and elevated sections, while the less susceptible sections were identified on flat reliefs. Landslides may also be triggered, for instance, at steep sections with varied contractive soil bodies and shallow structures. Most importantly, leveraging the LSI map would help the necessary agencies to forestall and mitigate future landslide occurrences in the area.

Landslide Investigation through Emerging Technology of Electrical Resistivity Tomography

A landslide is an event of a disaster and has adverse effects on every country with varying degrees of impact. The lithology's complex nature, the vast slide area and the landslide's topography make the direct investigation challenging. In depth landslide investigation demands multidisciplinary knowledge, skilled human power and costly testing equipment, making direct investigation tedious and time-consuming. However, an alternative for the direct investigation is needed to cover all the aspects of landslide mechanism. Recently advanced geophysical methods have an excellent potential for collecting landslide mechanism-related information without disturbing them. One such method is effectively used to investigate the soil's subsurface properties as one of the significant threats to stability is rainfall in a landslide-prone area and its resolve is effectively called Electrical Resistivity Tomography (ERT). The present study endeavors to display cutting-edge research on the applications of Electrical Resistivity Tomography (ERT) in the field of landslide investigation. The foremost objective of the study is to present the outcomes of geophysical surveys conducted through ERT, which encompass 1D, 2D, 3D and time-lapse investigations. The study also highlights the advantages and limitations of these ERT investigations and identifies potential challenges that need to be addressed for the optimum utilization of ERT in future research.

Penerapan Sistem Informasi Geografis (SIG) Dalam Mengidentifikasi Potensi Kelongsoran

The primary objective of this study was to enhance the community's understanding and awareness of landslide risks and mitigation strategies by employing Geographic Information Systems (GIS) to identify landslide-prone areas in the Magetan District, East Java. The initiative unfolded in several stages, which included: (1) emphasizing the importance of mapping areas susceptible to landslides; (2) consolidating data through satellite image analysis and GIS; (3) developing GIS-based maps that depict landslide-prone areas; and (4) circulating these maps among the local community and governing bodies. In conclusion, this study demonstrates that a participatory approach, combined with advanced technologies such as GIS, can play a pivotal role in disaster mitigation. Early identification of landslide risks and appropriate mitigation strategies using GIS and community education can significantly enhance community preparedness and resilience to landslides. The potential applicability of this method to other regions with similar topographical and geological conditions underscores its relevance in improving community readiness and resilience against landslide disasters.

Rapid Landslide Extraction from High-Resolution Remote Sensing Images Using SHAP-OPT-XGBoost

Landslides, the second largest geological hazard after earthquakes, result in significant loss of life and property. Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, China, is a typical landslide-prone area in the Three Gorges Reservoir Area. In this study, we newly integrate Shapley Additive Explanation (SHAP) and Optuna (OPT) hyperparameter tuning into four basic machine learning algorithms: Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Additive Boosting (AdaBoost). We construct four new models (SHAP-OPT-GBDT, SHAP-OPT-XGBoost, SHAP-OPT-LightGBM, and SHAP-OPT-AdaBoost) and apply the four new models to landslide extraction for the first time. Firstly, high-resolution remote sensing images were preprocessed, landslide and non-landslide samples were constructed, and an initial feature set with 48 features was built. Secondly, SHAP was used to select features with significant contributions, and the important features were selected. Finally, Optuna, the Bayesian optimization technique, was utilized to automatically select the basic models’ best hyperparameters. The experimental results show that the accuracy (ACC) of these four SHAP-OPT models was above 92% and the training time was less than 1.3 s using mediocre computational hardware. Furthermore, SHAP-OPT-XGBoost achieved the highest accuracy (96.26%). Landslide distribution information in Fengjie County from 2013 to 2020 can be extracted by SHAP-OPT-XGBoost accurately and quickly.

Identification Of Landslide-Prone Areas In Citeureup Sub-District, Bogor Regency, West Java, Indonesia

There were 21 landslides in Citeureup Subdistrict in 2017-2021 which caused several houses to be slightly damaged to severely damaged, and road and bridge infrastructure was cut off. This study aims to identify landslide-prone areas in Citeureup Sub-District. The method used was descriptive method with a spatial approach. Data was processed using GIS 10.8 software with the analytical technique used was a spatial analysis by overlaying five parameters of landslide susceptibility. After that, field validation was carried out. The subjects in this study were 12 selected land units. The results showed that areas that are included in the category of low landslide prone are Citeureup, Leuwinutug, Puspanegara, Puspasari, Sanja, and West Karangasem and East Karangasem Villages. The distribution of areas in the landslide-prone category is in a small part of Hambalang Village, Sanja, East Karangasem Village, and Leuwinutug Village. The distribution of areas in the high landslide-prone category is in most of Hambalang, Tajur, and Tangkil Villages. The distribution of areas in the landslide-prone category is very high in most of Hambalang, Tangkil, Sukahati, and a small part of it in East Karangasem Village. The distribution of landslide-prone areas is dominated by areas in the high landslide-prone category. Meanwhile, the distribution of landslide-prone areas is at least found in the medium landslide-prone category area.

Identification of Landslide-Prone Areas Using the Horizontal to Vertical Spectral Ratio (HVSR) Method and the GIS Approach in Semakai District, Tanggamus Regency, Lampung Province

Identification of Landslide-Prone Areas Using the Horizontal to Vertical Spectral Ratio (HVSR) Method and the GIS Approach in Semakai District, Tanggamus Regency, Lampung Province

Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable.