Study on landslide susceptibility mapping based on rock–soil characteristic factors

This study introduces four rock–soil characteristics factors, that is, Lithology, Rock Structure, Rock Infiltration, and Rock Weathering, which based on the properties of rock formations, to predict Landslide Susceptibility Mapping (LSM) in Three Gorges Reservoir Area from Zigui to Badong. Logistic regression, artificial neural network, support vector machine is used in LSM modeling. The study consists of three main steps. In the first step, these four factors are combined with the 11 basic factors to form different factor combinations. The second step randomly selects training (70% of the total) and validation (30%) datasets out of grid cells corresponding to landslide and non-landslide locations in the study area. The final step constructs the LSM models to obtain different landslide susceptibility index maps and landslide susceptibility zoning maps. The specific category precision, receiver operating characteristic curve, and 5 other statistical evaluation methods are used for quantitative evaluations. The evaluation results show that, in most cases, the result based on Rock Structure are better than the result obtained by traditional method based on Lithology, have the best performance. To further study the influence of rock–soil characteristic factors on the LSM, these four factors are divided into “Intrinsic attribute factors” and “External participation factors” in accordance with the participation of external factors, to generate the LSMs. The evaluation results show that the result based on Intrinsic attribute factors are better than the result based on External participation factors, indicating the significance of Intrinsic attribute factors in LSM. The method proposed in this study can effectively improve the scientificity, accuracy, and validity of LSM.

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.

Assessment of torrential flood and landslide susceptibility of terrain: Case study - Mlava River Basin (Serbia)

Torrential floods and landslides are frequent natural disasters in Serbia, but also in the Mlava River Basin. Due to the large number of settlements, the main goal of this research is to determine the locations that are most susceptible to torrential floods and landslides in the Mlava River Basin. Using geographic information systems (GIS), the first step is the analysis the susceptibility of the terrain to torrential floods using the Flash Flood Potential Index (FFPI) method. According to the obtained data, it was determined that 31.53% of the Mlava River Basin is susceptible, and 10.46% is very susceptible to torrential floods. The second step is the analysis of the susceptibility of the terrain to landslides, for which the statistical Probability method (PM) and the Landslide Susceptibility Index (LSI) were used. According to the results of the LSI index and PM method, 8.09% and 14.04% of the basin area is in the category of high and very high susceptibility to landslides. This paper represents a significant step towards a better understanding of unfavorable natural conditions in the Mlava River Basin, and the obtained results are applicable to numerous human activities in the research area (environmental protection, sustainable management of agricultural plots, protection of water and forest resources and ecosystems, etc.).

Land Degradation in Nepal

Nepal is a mountainous country sandwiched between China and India that extends along the Hind Kush Himalayan range. The entire country sits on a geological formation that has witnessed massive transformation in the past several decades. Land degradation is active in Nepal. This study reviews the causes of land degradation in Nepal based on publicly available reports, books, journal articles, and government policy and regulations. The review also uses publicly available global datasets to contextualize local conditions. The review shows that topography; land use and cover change driven by population growth and urbanization; traditional agricultural practice in steep slope; soil erodibility due to unscientific ways of farming; use of chemical fertilizers and, pest and disease control techniques; unsustainable land management by the government; unscientific infrastructure development has been the proximate causes of land degradation in the majority of the cases. While underlying causes include population and poverty; out migration; deforestation; land tenure and property rights, non-farm employment; and technological change. The situation when combined with the Landslide Susceptibility Index and Land Cover data shows that the country needs to make concerted effort to stop and minimize the damage of land degradation in the country.

Communality from equal weight for GIS-based landslide susceptibility mapping

Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.

Landslide Mapping and Susceptibility Assessment Using Geospatial Analysis and Earth Observation Data

The western part of Crete Island has undergone serious landslide events in the past. The intense rainfalls that took place in the September 2018 to February 2019 period provoked extensive landslide events at the northern part of Chania prefecture, along the motorway A90. Geospatial analysis methods and earth observation data were utilized to investigate the impact of the various physical and anthropogenic factors on landslides and to evaluate landslide susceptibility. The landslide inventory map was created based on literature, aerial photo analysis, satellite images, and field surveys. A very high-resolution Digital Elevation Model (DEM) and land cover map was produced from a dense point cloud and Earth Observation data (Landsat 8), accordingly. Sentinel-2 data were used for the detection of the recent landslide events and offered suitable information for two of them. Eight triggering factors were selected and manipulated in a GIS-based environment. A semi-quantitative method of Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) was applied to evaluate the landslide susceptibility index (LSI) both for Chania prefecture and the motorway A90 in Chania. The validation of the two LSI maps provided accurate results and, in addition, several susceptible points with high landslide hazards along the motorway A90 were detected.

The performance of using an autoencoder for prediction and susceptibility assessment of landslides: A case study on landslides triggered by the 2018 Hokkaido Eastern Iburi earthquake in Japan

Background Thousands of landslides were triggered by the Hokkaido Eastern Iburi earthquake on 6 September 2018 in Iburi regions of Hokkaido, Northern Japan. Most of the landslides (5627 points) occurred intensively between the epicenter and the station that recorded the highest peak ground acceleration. Hundreds of aftershocks followed the major shocks. Moreover, in Iburi region, there is a high possibility of earthquakes occurring in the future. Effective prediction and susceptibility assessment methods are required for sustainable management and disaster mitigation in the study area. The aim of this study is to evaluate the performance of an autoencoder framework based on deep neural network for prediction and susceptibility assessment of regional landslides triggered by earthquakes. Results By applying 12 sampling sizes and 12 landslide-influencing factors, 12 landslide susceptibility maps were produced using an autoencoder framework. The results of the model were evaluated using qualitative and quantitative assessment methods. The ratios of the sampling sizes on the non-landslide points randomly generated from the combination zone including plain and mountain (PM) and a mountainous only zone (M) affected different prediction abilities of the model’s performance. Conclusions The 12 susceptibility maps, including the landslide susceptibility index, indicated the various spatial distributions of the landslide susceptibility values in both PM and the M. The highly accurate models explicitly distinguished the potential areas of landslide from stable areas without expanding the spatial extent of the potential landslide areas. The autoencoder is proved to be an effective and efficient method for extracting spatial patterns through unsupervised learning for the prediction and susceptibility assessment of landslide areas.

Landslide susceptibility assessment using frequency ratio model in Bogor, West Java, Indonesia

Landslides are common natural disasters in Bogor, Indonesia, triggered by a combination of factors including slope aspect, soil type and bedrock lithology, land cover and land use, and hydrologic conditions. In the Bogor area, slopes with volcanic lithologies are more susceptible to failure. GIS mapping and analysis using a Frequency Ratio Model was implemented in this study to assess the contribution of conditioning factors to landslides, and to produce a landslide susceptibility map of the study area. A landslide inventory map was prepared from a database of historic landslides events. In addition, thematic maps (soil, rainfall, land cover, and geology map) and Digital Elevation Model (DEM) were prepared to examine landslide conditioning factors. A total of 173 landslides points were mapped in the area and randomly subdivided into a training set (70%) with 116 points and test set with 57 points (30%). The relationship between landslides and conditioning factors was statistically evaluated with FR analysis. The result shows that lithology, soil, and land cover are the most important factors generating landslides. FR values were used to produce the Landslide Susceptibility Index (LSI) and the study area was divided into five zones of relative landslide susceptibility. The result of landslide susceptibility from the mid-region area of Bogor to the southern part was categorized as moderate to high landslide susceptibility zones. The results of the analysis have been validated by calculating the Area Under a Curve (AUC), which shows an accuracy of success rate of 90.10% and an accuracy of prediction rate curve of 87.30%, which indicates a high-quality susceptibility map obtained from the FR model.