Landslide Hazard Susceptibility Research Articles

Co-seismic and rainfall-triggered landslide hazard susceptibility assessment for Uganda derived using fuzzy logic and geospatial modelling techniques

AbstractUganda has suffered from many damaging landslides like the 1966 Rwenzori, 1994 Kisomoro and 2010 Bududa events. Despite escalating landslide risks exacerbated by rapid deforestation, urbanization and population growth coupled with a substandard building stock, comprehensive national co-seismic and rainfall-induced landslide hazard and risk maps for Uganda do not exist. This study therefore aims to conduct landslide hazard assessment and zonation for Uganda using a geospatial-based fuzzy logic methodology. In this methodology, landslide frequency ratios obtained for the 1966 Toro and 1994 Kisomoro earthquakes are assigned to the stochastic event-based probabilistic seismic hazard map derived using OpenQuake-engine. The available co-seismic and rainfall-induced landslide inventory datasets are used to derive the distribution of landslide frequency ratios based on geology, topographic slope position index, slope aspect, slope angle, distance from streams, and proximity to major active faults. The spatial distribution of fuzzy membership functions obtained from frequency ratios are overlaid and aggregated to produce landslide susceptibility maps showing relative probabilities of landslide occurrences across Uganda. Results indicate that the highest overall landslide hazard susceptibility is expected in areas comprising highly weathered outcropping rocks of precambrian granites, dominantly metasedimentary, and granulites and gneisses geologies within 40 km from major active faults; where the bedrock peak ground acceleration ≥ 0.1 g, topographic position index ≥ 3.8, slope gradient ≥ 10°, and the distance from streams ≤ 1.25 km. These findings can inform Uganda’s directorate of disaster preparedness and management towards pioneering the development of co-seismic landslide risk mitigation measures for the country.

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm: A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm: A case study of Baihetan Reservoir Area

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture.

At present, landslide susceptibility assessment (LSA) based on landslide characteristics in different areas is an effective measure for landslide management. Nujiang Prefecture in China has steep mountain slopes, a large amount of water and loose soil, and frequent landslide disasters, which have caused a large number of casualties and economic losses. This paper aims to understand the characteristics and formation mechanism of regional landslides through the evaluation of landslide susceptibility so as to provide relevant references and suggestions for spatial planning and disaster prevention and mitigation in Nujiang Prefecture. Based on the grid cell, this study selected 10 parameters, namely elevation, slope, aspect, lithology, proximity to faults, proximity to road, proximity to rivers, normalized difference vegetation index (NDVI), land-use type, and precipitation. Support vector machine (SVM), certainty factor method (CF), and deterministic coefficient method-support vector machine (CF-SVM) were used to evaluate the landslide susceptibility in Nujiang Prefecture. According to these three models, the study area was divided into five landslide susceptibility grades, including extremely high susceptibility, high susceptibility, moderate susceptibility, low susceptibility, and very low susceptibility. Receiver operating characteristic curve (ROC) was applied to verify the accuracy of the model. The results showed that CF model (ROC = 0.865), SVM model (ROC = 0.892), CF-SVM model (ROC = 0.925), and CF-SVM model showed better performance. Therefore, CF-SVM model results were selected for analysis. The study found that the characteristics of high and extremely high landslide-prone areas in Nujiang Prefecture have the following characteristics: intense human activities, large density of buildings and arable land, rich water resources, good economic development, perfect transportation facilities, and complex topography and landform. In addition, there is a finding inconsistent with our common sense that the distribution of landslide disasters in the study area does not decrease with the increase of NDVI value. This is because the Nujiang River basin is a high mountain canyon area with low rock strength, barren soil, and underdeveloped vegetation and root system. In an area with large slope, the probability of landslide disaster will increase with the increase of NDVI. The CF-SVM coupling model adopted in this study is a good first attempt in the study of landslide hazard susceptibility in Nujiang Prefecture.

Combined SBAS-InSAR and PSO-RF Algorithm for Evaluating the Susceptibility Prediction of Landslide in Complex Mountainous Area: A Case Study of Ludian County, China.

In complex mountainous areas where earthquakes are frequent, landslide hazards pose a significant threat to human life and property due to their high degree of concealment, complex development mechanism, and abrupt nature. In view of the problems of the existing landslide hazard susceptibility evaluation model, such as poor effectiveness and inaccuracy of landslide hazard data and the need for experts to participate in the calculation of a large number of evaluation factor weight classification statistics. In this paper, a combined SBAS-InSAR (Small Baseline Subsets-Interferometric Synthetic Aperture Radar) and PSO-RF (Particle Swarm Optimization-Random Forest) algorithm was proposed to evaluate the susceptibility of landslide hazards in complex mountainous regions characterized by frequent earthquakes, deep river valleys, and large terrain height differences. First, the SBAS-InSAR technique was used to invert the surface deformation rates of the study area and identified potential landslide hazards. Second, the study area was divided into 412,585 grid cells, and the 16 selected environmental factors were analyzed comprehensively to identify the most effective evaluation factors. Last, 2722 landslide (1361 grid cells) and non-landslide (1361 grid cells) grid cells in the study area were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide and non-landslide data, the performances of the PSO-RF algorithm and three other machine learning algorithms, BP (back propagation), SVM (support vector machines), and RF (random forest) algorithms were compared. The results showed that 329 potential landslide hazards were updated using the surface deformation rates and existing landslide cataloguing data. Furthermore, the area under the curve (AUC) value and the accuracy (ACC) of the PSO-RF algorithm were 0.9567 and 0.8874, which were higher than those of the BP (0.8823 and 0.8274), SVM (0.8910 and 0.8311), and RF (0.9293 and 0.8531), respectively. In conclusion, the method put forth in this paper can be effectively updated landslide data sources and implemented a susceptibility prediction assessment of landslide disasters in intricate mountainous areas. The findings can serve as a strong reference for the prevention of landslide hazards and decision-making mitigation by government departments.

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China.

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

Modeling and Optimization of Traffic Sensor Layout Based on Landslide Risk

The layout of traffic sensor network nodes is related to the economic benefits of intelligent transportation systems. In this paper, we use the information model of landslide hazard susceptibility as the new measurement method, and adapt the landslide disaster susceptibility zoning result as the constraint condition of the maximum comprehensive value optimization model. The relevant parameters of the model are analysed, and the mathematics of each parameter is given. Through the example of sensor layout of Chongqing Yuxiang Expressway, it is determined that the number of optimal section sensors is 142 and the optimal layout spacing is about 676m. The results show that compared with the widely used graph theory model, the model can not only determine the optimal number of sensors but also give the best position of each sensor. The parameters of the model are calibrated for different situations. The results show that the model is easy to extend to different cities, different road conditions and different sensor types, and has good universality.

A Fuzzy Comprehensive Evaluation Method Based on AHP and Entropy for a Landslide Susceptibility Map

Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen’an County, Shan’xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert’s knowledge and the opinions of decision-makers.

Multihazard Mapping of Banepa and Panauti Municipalities

A combination of rough topography, steep slopes, active tectonic and seismic process and intense impact of monsoon rain has made the fragile environment of Nepal vulnerable to a variety of natural hazards. Most frequent hazards are floods, landslides, epidemics, fires, earthquake and other hydro-meteorological disasters, causing heavy loss of human lives as well as economic loss including housing and infrastructures (MDRIP, 2009). Hence, hazard assessments are the need of the hour. They help district and regional decision makers, policy makers and development agencies prepare disaster risk reduction plans. The chosen study area was Banepa and Panauti municipality. Separate hazard assessments have been performed for four hazards, namely, earthquake, flood, landslide and industrial hazards.Earthquake hazard zone maps have been made following the Probabilistic Seismic Hazard Assessment (PSHA) approach for 500 year return period to produce seismic intensity distribution maps in the form of Modified Mercalli Intensity (MMI) maps using Trifunac and Brady formula. Flood inundation maps have been made using HEC-RAS and HEC-GeoRAS extension for ArcGIS for return periods of 2, 10 and 500 of Chandeswori and Punyamata rivers. Landslide hazard susceptibility map has been made using the Stability Index Mapping (SINMAP) extension for ArcGIS that uses an infinite-slope equation accurate for debris flows. Industrial hazard maps that depict the vicinity that falls within various ranges of danger in the event of different industrial hazards like fire, Vapor Cloud Explosion (VCE) and Boiling Liquid Expanding Vapor Explosion (BLEVE) have been prepared as well. Finally, a composite multi hazard map has been prepared by combining all the four hazards.Nepalese Journal on Geoinformatics -13, 2014, Page: 25-31

GIS-based landslide hazard predicting system and its real-time test during a typhoon, Zhejiang Province, Southeast China

Yongjia County, Zhejiang Province, is located in southeast China. Statistics and previous reports have shown that 62% of study area has experienced various forms of slope instabilities, including debris flows and landslides. The geological buildup promotes the occurrence of landslides, since most part of the area is covered by clayey sandstones, volcanic tuffs and other types of pyroclastic rocks. A GIS-based system is established to assess and visualize landslide hazards. The information layers in the MAPGIS system are generated by using 11,397 regular grids with size of 5mm∗5mm. At first, Information–Matter-Element Model is adopted to compile landslide susceptibility map by using input parameters such as lithology, structural geology, slope morphology and angle, surface waters and human induced geomorphological changes. Rainfall data, quantity of rainfall and rainfall intensity are obtained to calculate the Effective Rainfall Model. Past landslide frequency with the combination of effective rainfall is used to determine rainfall threshold values and to generate a rainfall threshold map. A warning map is compiled including five-steps of warning levels by overlaying landslide hazard maps and rainfall threshold maps. Above the third level the population is noticed, while at level five immediate evacuation takes place. During “Yunna” typhoon in August 2004, a real-time warning was realized based on the landslide hazard susceptibility map and the real rainfall information. The GIS-based system proved to be satisfactory in predicting areas of landslides, marking nine critical localities during this catastrophic event.

Application of Satellite images and fuzzy set theory in Landslide hazard Mapping in Central Zab basin

Landslides are one kind of slopes processes have been take place in some area at worldwide and Iran every year, and they are associated with damaged, many people are harmed and losses their properties. The aim of this research landslide hazard assessment in central zab basin in the southwest mountainsides of West-Azerbaijan province, Iran by using information layers and effective factors in landslide hazard such us: slope, aspect, litho logy, land use, distance to drainage, distance to road, distance to fault and precipitation using fuzzy set theory, images satellite from Landsat ETM + sensor in 2009 and PAN sensor with 5.6 meters in 2010 from IRS-1D satellite, SPOT 5 satellite images in 2008, and GIS. The classification was done with histogram trend changes and available classifications. Weighting variables classes were determined between 0-1 (profusion proportion). Fuzzification each one of effective factors using IDRISI software terminated and after was assigned type and shape membership function. In this research, used J-shape membership function and gamma fuzzy operator to obtain landslide hazard susceptibility. Different maps of gamma fuzzy operator values 0.8 and 0.9 to make ready for study area. Obtained maps accuracy was evaluated with use of quality sum index (QS). Models evaluation results were showed 0.9 operator in gamma fuzzy model had best accuracy in making landslide susceptibility mapping in study area.

Fuzzy gamma based geomatic modelling for landslide hazard susceptibility in a part of Tons river valley, northwest Himalaya, India

Himalaya, being an active orogen, is continuously undergoing tectonic activities that pose threats of land stability on the mountains. A large number of mass movement occurrences have been observed in a part of the Tons river valley area. Remote sensing and geographical information system (GIS) based techniques have been used to derive and analyse various parameters and attributes pertinent to the landslide hazards. The GIS based analysis in the present work, incorporating the fuzzy membership values from the existing landslides, has been used for spatial modelling of the parameter maps including geology, slope amount, slope aspect, weathering, erosion, drainage influence, tectonic influence and landuse/land cover for landslide hazard zonation. The model has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide hazard zones. Eighty-five percent of the active landslides have been observed to occur in very high and high hazard zones.

Landslide Research at the British Geological Survey: Capture, Storage and Interpretation on a National and Site‐Specific Scale

Abstract:Landslide research at the British Geological Survey (BGS) is carried out through a number of activities, including surveying, database development and real‐time monitoring of landslides. Landslide mapping across the UK has been carried out since BGS started geological mapping in 1835. Today, BGS geologists use a combination of remote sensing and ground‐based investigations to survey landslides. The development of waterproof tablet computers (BGS·SIGMAmobile), with inbuilt GPS and GIS for field data capture provides an accurate and rapid mapping methodology for field surveys. Regional and national mapping of landslides is carried out in conjunction with site‐specific monitoring, using terrestrial LiDAR and differential GPS technologies, which BGS has successfully developed for this application. In addition to surface monitoring, BGS is currently developing geophysical ground‐imaging systems for landslide monitoring, which provide real‐time information on subsurface changes prior to failure events. BGS's mapping and monitoring activities directly feed into the BGS National Landslide Database, the most extensive source of information on landslides in Great Britain. It currently holds over 14 000 records of landslide events. By combining BGS's corporate datasets with expert knowledge, BGS has developed a landslide hazard assessment tool, GeoSure, which provides information on the relative landslide hazard susceptibility at national scale.

Landslide hazard susceptibility mapping based on terrain and climatic factors for tropical monsoon regions

Deep weathering, residual material (colluvium) and random rainfall intensity are mainly responsible for landslides in tropical monsoon regions. These parameters are often not taken into consideration in a landslide susceptibility assessment. Sustainable resources development in this region requires information on the spatial distribution of areas susceptible to landslides. This study highlights various aspects of the landslides that take place on the west coast of India and a methodology developed for landslide susceptibility mapping.