scholarly journals Landslide susceptibility zonation in the Tartagal River basin, Sierras Subandinas, Salta, Argentina

2021 ◽  
Vol 48 (1) ◽  
pp. 75
Author(s):  
Claudia Paola Cardozo ◽  
Guillermo Toyos ◽  
Valérie Baumann
Keyword(s):  
River Basin ◽  
Accuracy Assessment ◽  
Mitigation Strategies ◽  
Susceptibility Map ◽  
Stereo Pair ◽  
High Susceptibility ◽  
Conditioning Factors ◽  
Inventory Map ◽  
Using Data ◽  
Very High

On February 2009 intense rainfall triggered landslides in the Tartagal River basin that evolved into a debris flow that caused severe flooding in the town of Tartagal, Salta, Argentina. Based on these events, this paper presents a first attempt to map the landslides susceptibility in the Tartagal River basin. First, we elaborated an inventory map by using a 10 m pixel SPOT image acquired just after the disaster. Second, we evaluated a set of conditioning factors, which included lithology, slope and curvature; we derived the topographical variables from a 12.5 m pixel digital elevation model (DEM) based on a stereo-pair of satellite images ALOS-PRISM. Finally, we used these conditioning factors and the 2009 landslides inventory map as input for a heuristic model to elaborate the susceptibility map. The results indicated that landslides affected an area of 8 km2 and that at least 2.2x106 m3 of material were removed. The susceptibility map identified zones of low, moderate, high and very high susceptibility that occupied 18, 22, 25 and 17 km2, respectively. Accuracy assessment using data covering landslides occurred in 2006 showed that 95% of them fell within the high and very high susceptibility areas. The results presented herein provide vital baseline information for future studies and may contribute for the development of landslide hazard mitigation strategies.

scholarly journals Susceptibilidad a la licuación de suelos y sedimentos en el valle Ullum-Zonda (31º30’ S-68º25’ O), Precordillera, Andes Centrales, Argentina

2021 ◽  
Vol 48 (2) ◽  
pp. 333
Author(s):  
Silvia Palacios ◽  
Gabriela Lara ◽  
Laura Perucca
Keyword(s):  
Industrial Sector ◽  
Distal Portion ◽  
Alluvial Fan ◽  
San Juan ◽  
Historical Records ◽  
High Susceptibility ◽  
Conditioning Factors ◽  
Sedimentary Deposits ◽  
San Juan River ◽  
Very High

. The earthquakes of 1894, 1944, 1952 and 1977 occurred in the province of San Juan in central-wesern Argentina caused numerous processes of soils and sediment liquefaction, including those in the Ullum-Zonda valley. Historical records showed cracks, sand volcanoes, craters and differential settlements, which caused significant damage to housing and the agro-industrial sector of the region. In this work, we carried out a study of the susceptibility to liquefaction of soils and sedimentary deposits in the Ullum-Zonda valley. This was conducted using a methodology in which conditioning factors such as depth of the water table, historical records of liquefaction, potential seismogenic sources, origin, age and grain size of the soils and sedimentary deposits, among others, were evaluated and weighted. An iterative process of overlapping maps weighted the influence of the different factors in the assessment of susceptibility. Once the optimal combination was achieved, a final map with the zoning of soils and sediment susceptibility to liquefaction was obtained for the Ulum-Zonda Valley. The achieved zoning was related to a susceptibility index (SI), qualitatively classified as very high, high, moderate and low. The zone of very high susceptibility to liquefaction is located in the distal portion of the alluvial fan formed by the San Juan River in the Ullum-Zonda Valley, the areas of high to moderate susceptibility in the middle sector of the fan, and those of moderate to low susceptibility correspond to the proximal-middle sector of the fan. The main villages of the Ullum-Zonda valley, Ibáñez (head of the Ullum department) to the north of the San Juan River, Basilio Nievas (head of the Zonda department), to the south of the river, Tacú residential sector (located south of the Ullum dam) and the yacht clubs (located on the northeast periphery of the dam) are located in the areas of high to very high susceptibility, where the main conditioning factors are soil and sediments granulometry and the depth of the phreatic level.

scholarly journals Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

2019 ◽  
Vol 58 ◽  
pp. 163-171 ◽  
Author(s):  
Arishma Gadtaula ◽  
Subodh Dhakal
Keyword(s):  
Landslide Susceptibility ◽  
Google Earth ◽  
Weight Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Mountainous Area ◽  
High Susceptibility ◽  
Seismic Landslides ◽  
2015 Gorkha Earthquake ◽  
Very High

The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

scholarly journals Landslide Susceptibility Assessment Using Modified Frequency Ratio Model in Kaski District, Nepal

2021 ◽  
Vol 11 (1) ◽  
pp. 167-177
Author(s):  
Niraj Baral ◽  
Akhilesh Kumar Karna ◽  
Suraj Gautam
Keyword(s):  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Satellite Image ◽  
Image Interpretation ◽  
Google Earth ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Prediction Rate ◽  
Using Data

Landslides are the most common natural hazards in Nepal especially in the mountainous terrain. The existing topographical scenario, complex geological settings followed by the heavy rainfall in monsoon has contributed to a large number of landslide events in the Kaski district. In this study, landslide susceptibility was modeled with the consideration of twelve conditioning factors to landslides like slope, aspect, elevation, Curvature, geology, land-use, soil type, precipitation, road proximity, drainage proximity, and thrust proximity. A Google-earth-based landslide inventory map of 637 landslide locations was prepared using data from Disinventar, reports, and satellite image interpretation and was randomly subdivided into a training set (70%) with 446 Points and a test set with 191 points (30%). The relationship among the landslides and the conditioning factors were statistically evaluated through the use of Modified Frequency ratio analysis. The results from the analysis gave the highest Prediction rate (PR) of 6.77 for elevation followed by PR of 66.45 for geology and PR of 6.38 for the landcover. The analysis was then validated by calculating the Area Under a Curve (AUC) and the prediction rate was found to be 68.87%. The developed landslide susceptibility map is helpful for the locals and authorities in planning and applying different intervention measures in the Kaski District.

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

2021 ◽  
Vol 16 (4) ◽  
pp. 529-538
Author(s):  
Thi Thanh Thuy Le ◽  
The Viet Tran ◽  
Viet Hung Hoang ◽  
Van Truong Bui ◽  
Thi Kien Trinh Bui ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Mountainous Regions ◽  
Geological Conditions ◽  
Very High

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.

scholarly journals GIS-Based Spatial and Multi-Criteria Assessment of Riverine Flood Potential: A Case Study of the Nitra River Basin, Slovakia

2021 ◽  
Vol 10 (9) ◽  
pp. 578
Author(s):  
Matej Vojtek ◽  
Jana Vojteková ◽  
Quoc Bao Pham
Keyword(s):  
River Basin ◽  
Flood Events ◽  
Parameter Sensitivity Analysis ◽  
Analytical Hierarchical Process ◽  
Conditioning Factors ◽  
Multi Criteria Evaluation ◽  
Very High ◽  
Different Levels ◽  
The Eu

The aim of this study was to identify the areas with different levels of riverine flood potential (RFP) in the Nitra river basin, Slovakia, using multi-criteria evaluation (MCE)-analytical hierarchical process (AHP), geographic information systems (GIS), and seven flood conditioning factors. The RFP in the Nitra river basin had not yet been assessed through MCE-AHP. Therefore, the methodology used can be useful, especially in terms of the preliminary flood risk assessment required by the EU Floods Directive. The results showed that classification techniques of natural breaks (Jenks), equal interval, quantile, and geometric interval classified 32.03%, 29.90%, 41.84%, and 53.52% of the basin, respectively, into high and very high RFP while 87.38%, 87.38%, 96.21%, and 98.73% of flood validation events, respectively, corresponded to high and very high RFP. A single-parameter sensitivity analysis of factor weights was performed in order to derive the effective weights, which were used to calculate the revised riverine flood potential (RRFP). In general, the differences between the RFP and RRFP can be interpreted as an underestimation of the share of high and very high RFP as well as the share of flood events in these classes within the RFP assessment. Therefore, the RRFP is recommended for the assessment of riverine flood potential in the Nitra river basin.

scholarly journals Soil Erosion Susceptibility Mapping of Imo River Basin Using Modified Geomorphometric Prioritisation Method

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Peter C. Nwilo ◽  
Caleb O. Ogbeta ◽  
Olagoke E. Daramola ◽  
Chukwuma J. Okolie ◽  
Michael J. Orji
Keyword(s):  
Soil Erosion ◽  
River Basin ◽  
Low Income ◽  
Prediction Accuracy ◽  
Remote Sensing Data ◽  
Shuttle Radar Topography Mission ◽  
Low Income Countries ◽  
Susceptibility Map ◽  
Operating Characteristics ◽  
Very High

Abstract Gullies and other forms of erosion have been the greatest environmental problem and catastrophe in most high- and low-income countries. The challenge posed by soil erosion has compromised agricultural productivity, environmental biodiversity and food safety for the world's population. It is important to identify vulnerable areas to soil erosion in each region to initiate remedial measures. This study demonstrates the use of watershed morphometry coupled with weighted sum analysis (WSA) to estimate the soil erosion susceptibility of the Imo River Basin sub-watersheds (SWs) in South-Eastern Nigeria using satellite remote-sensing data and geographic information system (GIS) analysis. To this end, Shuttle Radar Topography Mission (SRTM), a Digital Elevation Model (DEM) with 30 m spatial resolution was used to extract and analyse 18 morphometric parameters including basic, linear, shape and relief. The method of receiver operating characteristics (ROC) curves was used to validate the model's prediction accuracy. This morphometry-based analysis resulted in the SWs being classified into zones of low, medium, high and very high erosion susceptibility. With regard to erosion susceptibility, 41.51% of the basin (2494.68 km2) is in the very high priority zone; while 10.50%, 44.33% and 3.66% of the basin are in the high, medium and low priority zones respectively. Validation of the final erosion susceptibility map showed a prediction accuracy of 81%. The use of satellite imagery and morphometric analysis in this study was cost- and time-effective for identifying areas susceptible to soil erosion.

scholarly journals Integrated Remote Sensing and GIS Analysis for Landslide Susceptibility Assessment along the Trenggalek–Ponorogo Road, East Java Province, Indonesia

2021 ◽  
Vol 1 (1) ◽  
pp. 39-48
Author(s):  
Adniwan Shubhi Banuzaki ◽  
◽  
Adelia Kusuma Ayu
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Landslide Susceptibility ◽  
Geological Structure ◽  
Mitigation Strategies ◽  
Susceptibility Map ◽  
Gis Technology ◽  
Topographic Slope ◽  
Very High ◽  
Landslide Triggering

Landslide, the second most common hazard in Indonesia, after an earthquake, is causing enormous losses of public infrastructures with subsequent economic disruptions. Roads are the most frequent public property which is affected by landslides. Due to the geomorphological condition of Indonesia, the construction of roads often intersects the mountainous topography. The Trenggalek–Ponorogo Road is one of the roads passing through mountainous terrains that are very susceptible affected by landslides. The road has an important role as the main transportation connector of some regencies in East Java Province. Landslide mitigation strategies along the Trenggalek–Ponorogo Road are needed to prevent enormous losses. This research was aimed to conduct a remote sensing-based assessment of landslide susceptibility areas along the Trenggalek–Ponorogo Road. The landslide susceptibility areas were assessed by considering landslide triggering parameters; those were topographic slope, distance to geological structure, distance to stream, lithology, and land use/land cover. The landslide triggering parameters were presented in spatial data and processed using Geographic Information System (GIS) technology. The Analytical Hierarchy Process (AHP) method was applied to integrate the landslide triggering parameters which have the degree of effect to determine Landslide Potential Index (LPI). The resulting LPI delineated the area into four susceptibility zones: very high, high, moderate, and low, which were presented as landslide susceptibility map. The susceptibility map was then validated by landslide occurrences inventory in the study area. The very high susceptibility zones, which are strongly predicted affecting the Trenggalek–Ponorogo Road, are located in Nglinggis and Grogol Village.

scholarly journals Landslide Susceptibility Zonation Using Geospatial Technologies and Multi Criteria Evaluation Techniques in Upper Didessa Sub-basin, Southwest Ethiopia

2020 ◽  
Author(s):  
Redwan Sultan Mohammednur ◽  
Dessalegn Obsi Gemeda ◽  
Kiros Tsegay Deribew
Keyword(s):  
Landslide Susceptibility ◽  
Causative Factor ◽  
Mitigation Strategies ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Multi Criteria Evaluation ◽  
Hypsometric Integral ◽  
Expert Interview ◽  
Very High

Abstract Landslide is a serious geo-hazard that poses destruction and loses of life in different part of the world. The severity of the problem is higher in developing countries like Ethiopia. This study is aimed at assessing the spatial landslide susceptibility in the upper Didessa sub-basin using GIS and multi criteria evaluation (MCE) techniques. In order to reach this objective both primary (field survey) and secondary data (expert interview, literature, remote sensing data, digital soil map and geological map) were obtained from various source. Eleven landslide causative factor identified in this research are slope, aspect, drainage density, topographic wetness index (TWI), stream power index (SPI), topographic ruggedness index, hypsometric integral, lithology, LULC, soil texture, and distance from road. The analytical hierarchy process (AHP) method was employed to identify the weight of each indicator from the pairwise comparison matrix. The weighted linear combination was then used to generate landslide susceptibility map (LSM). Based on landslide susceptibility, the study area was classified into very high, high, moderate, low, and very low susceptibility zones. Finally, based on the eleven-landslide causative factor analysis, about 24% of the study area is moderately susceptible, while 12% and 6% were classified as high and very high susceptibility to landslides, respectively. The results of this study could help decision makers for future landslide hazardous preventions and mitigation strategies.

scholarly journals Landslide Susceptibility Modeling Using a Hybrid Bivariate Statistical and Expert Consultation Approach in Canada Hill, Sarawak, Malaysia

2021 ◽  
Vol 9 ◽  
Author(s):  
Marelyn Telun Daniel ◽  
Tham Fatt Ng ◽  
Mohd. Farid Abdul Kadir ◽  
Joy Jacqueline Pereira
Keyword(s):  
Landslide Susceptibility ◽  
Geographical Information ◽  
Susceptibility Map ◽  
Expert Consultation ◽  
High Susceptibility ◽  
Quality Of Data ◽  
Local Conditions ◽  
Planar Failure ◽  
Very High

Landslide susceptibility assessment was conducted in Canada Hill, Sarawak, Malaysia through a combined bivariate statistics and expert consultation approach using geographical information system, which captures landslide-conditioning parameters specific to the study area; to ensure its usefulness in practice. Over the past four decades, many landslide parameters and increasingly sophisticated statistical methods have been used in landslide research. However, the findings have had very limited use in practice as the actual ground conditions are not well represented. The weakness is due to poor quality of data in landslide inventories and inadequate understanding of landslide-conditioning parameters. In this study, bivariate statistical method was used in conjunction with an iterative process of expert consultation. Thirteen original landslide-conditioning parameters were narrowed down to six, with the addition of a unique parameter, planar failure potential, which was selected based on expert input. The parameter captures planar failure landslides, which has the highest impact in the study area, causing loss of lives and property destruction. The inaugural landslide susceptibility map for the study area has five classes; very low, low, moderate, high and very high susceptibility. All major planar failures and most smaller circular failures fall within the very high susceptibility class, with a success rate of 75.8%. The approach used in this study has improved the quality of the landslide inventory and delineated key conditioning parameters. The resultant map captures local conditions, which is useful for landslide management.

scholarly journals GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam

2020 ◽  
Vol 42 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Dang Quang Thanh ◽  
Duy Huu Nguyen ◽  
Indra Prakash ◽  
Abolfazl Jaafari ◽  
Viet -Tien Nguyen ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Conditioning Factors ◽  
Prone Area

Landslide susceptibility mapping of the city of Da Lat, which is located in the landslide prone area of Lam Dong province of Central Vietnam region, was carried out using GIS based frequency ratio (FR) method. There are number of methods available but FR method is simple and widely used method for landslide susceptibility mapping. In the present study, eight topographical and geo-environmental landslide-conditioning factors were used including slope, elevation, land use, weathering crust, soil, lithology, distance to geology features, and stream density in conjunction with 70 past landslide locations. The results show that 6.27% of the area is in the very low susceptibility area, 21.03% in the low susceptibility area, 27.09% in the moderate susceptibility area and 27.41% of the area is in the high susceptibility zone and 18.21% in the very high susceptibility zone. The landslide susceptibility map produced in this study helps to assist decision makers in proper land use management and planning.

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