Landslide Susceptibility in Majalengka Regency, West Java Province

Majalengka is one of districts in Indonesia which is susceptible to landslides. Landslides in Majalengka caused enormous losses such as damage to infrastructure, loss of property, and even human fatalities. Seeing of the impact, mitigation efforts are needed to reduce risks and losses by making landslide susceptibility maps. This study aims to map areas landslide susceptibility and as a reference for the government and related agencies to reduce losses. The method used overlay using Spatial Multi-Criteria Evaluation (SMCE), using weighting values from the Minister Public Works Regulation NO.22/PRT/M/2007, Puslittanak Bogor (2014) and Directorate Volcanology and Disaster Mitigation (DVMBG) (2004). Then comparison of these sources is carried out to determine weighting value with the highest accuracy. The variables are slope, rainfall, soil type, lithology, and land use. The results of this study indicate that landslide susceptibility areas are divided into non-susceptible, low, moderate, and high areas. Where areas Majalengka Regency is dominated by moderate susceptibility level. For the accuracy value of the landslide susceptibility map produced by the weighted value source from the Minister of Public Works Regulation NO.22/PRT/M/2007 has the highest accuracy value of 76%. For weighting from the Bogor Puslittanak is 73%, while weighting source from DVMBG is 68%.

Analysis of the landslide susceptibility map using frequency ratio method in sub-sub-Watershed Mamasa

Landslides that often occur in the Subs watershed of Mamasa increase the sedimentation rate so that the Bakaru hydropower plant becomes less than optimal. The contributing factors to lanslide susceptibility are land closure, lithology, curve, slope direction aspect, slope, precipitation, fault distance, and river distance. The research aims to determine the most influential erosion causative factor in Mamasa Sub-watershed by building a landslide susceptibility map using the frequency ratio method. The most significant factor is land closure, with a value of 2.03, indicating a high probability of lanslide events. The model’s success rate and prediction rate’s success rate were expressed fairly well with 0.754 and 0.752. Based on the insanity map, the Region is very high and high at 23.74% and 12.52%; insanity is moderate, low, and very low consecutively at 27.44 %, 23.77, and 12.33%.

Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits.

Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

Landslide variation with morphometric factors using the GIS techniques: The case of Shaqlawa Forest

This study deals with the application of geographical information system (GIS) datasets and methods to assess the landslide susceptibility in Wadi Hujran. The area has a rocky terrain and belongs to the Shaqlawa district of the Kurdistan Region of Iraq. The region is placed towards the Northeast side of Erbil city. The region covers an area of 18.56 Km2 (1856.1 ha) and consists of rough broken and stones. The watershed area is surrounded by North latitudes 36° 21' 53.514" to 36° 17' 49.7796" and East longitudes 44° 17' 5.658" to 44° 20' 9.06". Three factors, namely the morphometric, geological, and environmental, were used to prepare the landslide susceptibility index. The study made use of AHP method and prepared a landslide susceptibility map. Data related to geology, topography, hydrology, rainfall, and land use were used to prepare the map. Physical and statistical methods were used to validate the map. A heuristic approach was incorporated to produce the final susceptibility map. ArcGIS software was used to generate the landslide zones. A total of five landslide zones were generated, which varied from very low landslide zones (80.5) to very high landslide zone (84.5). The zones also included low landslide zone (1262.2), moderate landslide zone (1505.9), and high landslide zone (566.8), and the ratio of consistency in the present study was 0.06 AHP less than 1, and all the five zones in the study were compiled landslide zonation estimated.

Analysis of Landslide Susceptibility Using Deep Neural Network

Landslides in Korea are caused by various factors, such as topographic characteristics, geology, and climate change, and they cause significant damage to property and human life. It is necessary to analyze landslide susceptibility to identify the location of landslide occurrence precisely and respond to the risk of landslides. In this study, the probability of landslide occurrence was calculated through a landslide sensitivity analysis using a deep neural network based on eight conditioning factors and 26 landslide data. In addition, verification was performed using the ROC method. The landslide susceptibility obtained using a deep neural network showed a success rate of 70% and a prediction rate of 81.7%, indicating that the prediction rate was 11.7% higher than the success rate. In addition, a landslide susceptibility map for estimating the probability of landslide occurrence was plotted using the geometric spacing method. The chi-square test results indicated that the landslide susceptibility map obtained in this study was statistically significant. The location of landslides can be identified more accurately using the proposed method.

Landslide Susceptibility Mapping Based on the Combination of Bivariate Statistics and Modified Analytic Hierarchy Process Methods: A Case Study of Tinh Tuc Town, Nguyen Binh District, Cao Bang Province, Vietnam

Tinh Tuc town, Cao Bang province, Vietnam is prone to landslides due to the complexity of its climatic, geological, and geomorphological conditions. In this study, in order to produce a landslide susceptibility map, the modified analytical hierarchy process and landslide susceptibility analysis methods were used together with the layers, including: landslide inventory, slope, weathering crust, water storage, geology, land use, and distance from the road. In the study area, 98% of landslides occurred in highly or completely weathered units. Geology, land use, and water storage data layers were found to be important factors that are closely related with the occurrence of landslides. Although the weight of the “distance from the road” factor has a low value, the weight of layer “<100 m” has a high value. Therefore, the landslide susceptibility index very high is concentrated along the roads. For the validation of the predicted result, the landslide susceptibility map was compared with the landslide inventory map containing 47 landslides. The outcome shows that about 90% of these landslides fall into very high susceptibility zones.

Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam

Landslides are considered one of the most serious problems in the mountainous regions of the northern part of Vietnam due to the special topographic and geological conditions associated with the occurrence of tropical storms, steep slopes on hillsides, and human activities. This study initially identified areas susceptible to landslides in Ta Van Commune, Sapa District, Lao Cai Region using Analytical Hierarchy Analysis. Ten triggering and conditioning parameters were analyzed: elevation, slope, aspect, lithology, valley depth, relief amplitude, distance to roads, distance to faults, land use, and precipitation. The consistency index (CI) was 0.0995, indicating that no inconsistency in the decision-making process was detected during computation. The consistency ratio (CR) was computed for all factors and their classes were less than 0.1. The landslide susceptibility index (LSI) was computed and reclassified into five categories: very low, low, moderate, high, and very high. Approximately 9.9% of the whole area would be prone to landslide occurrence when the LSI value indicated at very high and high landslide susceptibility. The area under curve (AUC) of 0.75 illustrated that the used model provided good results for landslide susceptibility mapping in the study area. The results revealed that the predicted susceptibility levels were in good agreement with past landslides. The output also illustrated a gradual decrease in the density of landslide from the very high to the very low susceptible regions, which showed a considerable separation in the density values. Among the five classes, the highest landslide density of 0.01274 belonged to the very high susceptibility zone, followed by 0.00272 for the high susceptibility zone. The landslide susceptibility map presented in this paper would help local authorities adequately plan their landslide management process, especially in the very high and high susceptible zones.