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Статті в журналах з теми "SUSCEPTIBILTY ZONATION MAPPING"
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Bornaetxea, Txomin, Mauro Rossi, Ivan Marchesini, and Massimiliano Alvioli. "Effective surveyed area and its role in statistical landslide susceptibility assessments." Natural Hazards and Earth System Sciences 18, no. 9 (September 14, 2018): 2455–69. http://dx.doi.org/10.5194/nhess-18-2455-2018.
Повний текст джерелаАнотація:
Abstract. Geomorphological field mapping is a conventional method used to prepare landslide inventories. The approach is typically hampered by the accessibility and visibility, during field campaigns for landslide mapping, of the different portions of the study area. Statistical significance of landslide susceptibility maps can be significantly reduced if the classification algorithm is trained in unsurveyed regions of the study area, for which landslide absence is typically assumed, while ignorance about landslide presence should actually be acknowledged. We compare different landslide susceptibility zonations obtained by training the classification model either in the entire study area or in the only portion of the area that was actually surveyed, which we name effective surveyed area. The latter was delineated by an automatic procedure specifically devised for the purpose, which uses information gathered during surveys, along with landslide locations. The method was tested in Gipuzkoa Province (Basque Country), north of the Iberian Peninsula, where digital thematic maps were available and a landslide survey was performed. We prepared the landslide susceptibility maps and the associated uncertainty within a logistic regression model, using both slope units and regular grid cells as the reference mapping unit. Results indicate that the use of effective surveyed area for landslide susceptibility zonation is a valid approach that minimises the limitations stemming from unsurveyed regions at landslide mapping time. Use of slope units as mapping units, instead of grid cells, mitigates the uncertainties introduced by training the automatic classifier within the entire study area. Our method pertains to data preparation and, as such, the relevance of our conclusions is not limited to the logistic regression but are valid for virtually all the existing multivariate landslide susceptibility models.
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Bhandary, Netra Prakash, Ranjan Kumar Dahal, Manita Timilsina, and Ryuichi Yatabe. "Rainfall event-based landslide susceptibility zonation mapping." Natural Hazards 69, no. 1 (May 18, 2013): 365–88. http://dx.doi.org/10.1007/s11069-013-0715-x.
Повний текст джерела
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Khaidem, Sukhajit, and Kanwarpreet Singh. "Landslide Susceptibility Mapping along Manipur-Assam NH-37." IOP Conference Series: Earth and Environmental Science 889, no. 1 (November 1, 2021): 012002. http://dx.doi.org/10.1088/1755-1315/889/1/012002.
Повний текст джерелаАнотація:
Abstract Landslides are a natural hazard in steep places that occur regularly and cause significant damage. To avoid and minimise hazards, comprehensive landslide remediation and control, landslide assessment, and hazard zonation are required. Various methods are established based on different assessment methodologies, which are essentially split into qualitative and quantitative approaches. GIS-based landslide susceptibility mapping was carried out along the National Highway 37, which connects Assam and Manipur and is a vital lifeline for the state, to identify and demarcate possible failure zones. A field visit was used to create a landslide inventory map along the road network. Google Earth and LANDSAT satellite imagery To perform landslide susceptibility zonation, thematic layers of several landslide causative elements were constructed in the study region. The study region has been divided into five endangered zones i.e. (“very low, low, moderate, high, and extremely high”). The landslide susceptibility zonation map was validated using the AUC and landslide density methods. The final map will be helpful to a variety of stakeholders, including town planners, engineers, geotechnical engineers, and geologists, for development and construction in the study region.
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Chawla, Amit, Sowmiya Chawla, Srinivas Pasupuleti, A. C. S. Rao, Kripamoy Sarkar, and Rajesh Dwivedi. "Landslide Susceptibility Mapping in Darjeeling Himalayas, India." Advances in Civil Engineering 2018 (September 16, 2018): 1–17. http://dx.doi.org/10.1155/2018/6416492.
Повний текст джерелаАнотація:
Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes.
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Kavitha, G., S. Anbazhagan, and S. Mani. "Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India." Journal of Geology, Geography and Geoecology 30, no. 4 (December 25, 2021): 683–91. http://dx.doi.org/10.15421/112163.
Повний текст джерелаАнотація:
Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.
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Shrestha, Him Lal, and Mahesh Poudel. "Landslide Susceptibility Zonation Mapping in Post- Earthquake Scenario in Gorkha District." Forestry: Journal of Institute of Forestry, Nepal 15 (July 31, 2018): 45–56. http://dx.doi.org/10.3126/forestry.v15i0.24920.
Повний текст джерелаАнотація:
Landslide hazard zonation map is prepared to assist planners to implement mitigation measures so that further damage and loss can be minimized. In this study, post 25 April 2015 earthquake remote sensing data were used to prepare landslide inventory. Landsat images after the earthquake were downloaded from the National Aeronautics and Space Administration (NASA) website and processed using ArcGIS, ERDAS imagine and Analytical Hierarchy Process (AHP) as an extension in ArcGIS. The study was carried out in Gorkha district as this was the epicenter of the main earthquake of 25 April 2015 and consequently was highly affected by earthquake triggered landslide. The digital imagery was processed to analyze land use/land cover type. Geological features were analyzed using the criteria like color, tone, topography, stream drainage, etc. Primary topographic features like slope, aspect, elevation, etc. were generated from Digital Elevation Model (DEM). Seismological data (magnitude and epicenter) were obtained from Department of Seismology. For Landslide Susceptibility Zonation (LSZ) different thematic maps like Land Use and Land Cover (LULC) map, slope map, aspect map, lithological map, buffer map (distance from road and river/water source), soil map, and seismological map were assigned relative weights on the ordinal scale to obtain Landslide Susceptibility Index (LSI). Threshold values were selected according to breaks in LSI frequency and a LSZ map was prepared which shows very low, low, moderate, high, very high hazard zones in Gorkha district.
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Das, Jayshree, Susanta Mahato, Pawan Kumar Joshi, and Yuei-An Liou. "Forest Fire Susceptibility Zonation in Eastern India Using Statistical and Weighted Modelling Approaches." Remote Sensing 15, no. 5 (February 27, 2023): 1340. http://dx.doi.org/10.3390/rs15051340.
Повний текст джерелаАнотація:
Recurring forest fires disturb ecological balance, impact socio-economic harmony, and raise global concern. This study implements multiple statistical and weighted modelling approaches to identify forest fire susceptibility zones in Eastern India. Six models, namely, Frequency Ratio (FR), Certainty Factor (CF), Natural Risk Factor (NRF), Bivariate statistical (Wi and Wf), Analytical Hierarchy Process (AHP), and Logistic Regression (LR) were used in the study. Forest fire inventory (2001 to 2018) mapping was done using forest fire points captured by the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Fire responsible components, namely, topography (which has four variables), climate (5), biophysics (8) and disturbance (4) were used as inputs to the modelling approaches. Multicollinearity analysis was carried out to examine the association and remove the highly-correlated variables before performing the modeling. Validation of model prediction levels was done using Area Under the Receiver Operating Characteristic Curve (ROC curve-AUC) value. The results reveal that the areas with west and southwest orientations, and moderate slope demarcate higher susceptibility to forest fire. High precipitation areas with lower temperature but ample solar radiation increase their susceptibility to forest fire. Mixed deciduous forest type with ample solar radiation, higher NDVI, lower NDWI and lower TWI values exhibits higher susceptibility. Model validation shows that LR (with AUC = 0.809) outperforms other models used in the study. To minimize the risk of fire and frame with proper management plans for the study area, susceptibility mapping using satellite imageries, GIS technique, and modelling approaches is highly recommended.
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Basnet, P., M. K. Balla, and B. M. Pradhan. "Landslide hazard zonation, mapping and investigation of triggering factors in Phewa lake watershed, Nepal." Banko Janakari 22, no. 2 (December 1, 2013): 43–52. http://dx.doi.org/10.3126/banko.v22i2.9198.
Повний текст джерелаАнотація:
The landslide triggering factors were investigated followed by the thematic maps and landslide distribution map prepared and classified using the GPS and GIS Softwares like CartaLinx, ArcView and ERDAS IMAGINE in Sarangkot and Kaskikot Village Development Committees, Kaski district. In analytical hierarchy process, the factors for zonation were compared by Couple Comparison Method and their weights were determined using Arithmetic Mean Method and earned weight values of each factor. The landslide hazard zonation model was employed to prepare landslide hazard zonation map of the study area, and then classified into five relative hazard classes using the equal interval classification method. Finally, the landslide hazard zonation map was crossed with the landslide distribution map and the model applicability was confirmed by determining the per hazard class percent of area covered by the landslide. In the land hazard zonation map, 0.44% of the study area was in very low hazard, 2.11% in low hazard, 54.92% in moderate hazard, 21.34% in high hazard and 21.19% in very high hazard area. The major portion of the study area was on the moderate zone whereas the least portion was on the very low hazard zone. In the study area, most of the high and very high hazard class areas were found occupying the areas closer to the linear triggering factors like presence of linement and fault, presence of motorable road and presence of rivers and streams. The landslide density of the study area was found to be 0.44 per km2 indicating the higher hazard susceptibility of the area.DOI: http://dx.doi.org/10.3126/banko.v22i2.9198Banko Janakari: A Journal of Forestry Information for NepalVol. 22, No. 2, 2012 November Page: 43-52 Uploaded date: 12/1/2013
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Sekarlangit, Nadia, Teuku Faisal Fathani, and Wahyu Wilopo. "Landslide Susceptibility Mapping of Menoreh Mountain Using Logistic Regression." Journal of Applied Geology 7, no. 1 (June 28, 2022): 51. http://dx.doi.org/10.22146/jag.72067.
Повний текст джерелаАнотація:
Menoreh mountain is one of the priority areas developed for tourism and to support sustainable development, it must pay attention to disaster aspects, one of which is landslides. The map published by Center for Volcanology and Geological Hazard Mitigation of Indonesia (PVMBG) has a regional scale, so it is necessary to have a more detailed landslide susceptibility map in the Menoreh Mountains. Identification and evaluation of the landslide conditioning factor were done using logistic regression so that the zonation of the probability of landslide susceptibility can be made. The data was used from field observation conducted at 372 locations including 129 locations where landslides occurred and from a local disaster management agency (BPBD) of 200 landslide locations. Significant landslide conditioning factors include slope, lithology, distance to lineaments, distance to river, and distance to road. The research area is divided into three susceptibility zones classified into low landslide susceptibility zone (0-0.33) covering 39.82%, moderate landslide susceptibility zone (0.34-0.66) covering 25.86%, and high landslide susceptibility zone (0.67-1.00) covering 34.31% of the whole area. Analysis using the logistic regression method has a model prediction accuracy rate of 90.5%, which means that it can predict landslide occurrence in the Menoreh Mountains accurately.
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Rossi, Mauro, Txomin Bornaetxea, and Paola Reichenbach. "LAND-SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation." Geoscientific Model Development 15, no. 14 (July 21, 2022): 5651–66. http://dx.doi.org/10.5194/gmd-15-5651-2022.
Повний текст джерелаАнотація:
Abstract. In the past 50 years, a large variety of statistically based models and methods for landslide susceptibility mapping and zonation have been proposed in the literature. The methods, which are applicable to a large range of spatial scales, use a large variety of input thematic data, different model combinations, and several approaches to evaluate the models' performance. Despite the numerous applications available in the literature, a standard approach for susceptibility modeling and zonation is still missing. The literature search revealed that several software program and tools are available to evaluate regional slope stability using physically based analysis, but only a few use statistically based approaches. Among them, LAND-SE (LANDslide Susceptibility Evaluation) provides the possibility to perform and combine different statistical susceptibility models and to evaluate their performances and associated uncertainties. This paper describes the structure and the functionalities of LAND-SUITE, a suite of tools for statistically based landslide susceptibility modeling which integrates LAND-SE. LAND-SUITE completes and extends LAND-SE, adding functionalities to (i) facilitate input data preparation, (ii) perform preliminary and exploratory analysis of the available data, and (iii) test different combinations of variables and select the optimal thematic/explanatory set. LAND-SUITE provides a tool to assist the user during the data preparatory phase and to perform diversified statistically based landslide susceptibility applications.
Дисертації з теми "SUSCEPTIBILTY ZONATION MAPPING"
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Schlögel, Romy. "Quantitative landslide hazard assessment with remote sensing observations and statistical modelling." Thesis, Strasbourg, 2015. http://www.theses.fr/2015STRAH009/document.
Повний текст джерелаАнотація:
La création d’inventaires de glissements de terrain sert de base à l’évaluation quantitative de l’aléa et à la gestion du risque. Les cartes d’inventaires de mouvements gravitaires sont produites en utilisant des méthodes conventionnelles (campagnes de mesures de terrain, interprétation visuelle de photographies aériennes) et par des techniques de télédétection plus innovantes. Une des techniques les plus prometteuses pour la détection et la cartographie des glissements de terrain fait appel à la mesure de la déformation du sol par interférométrie radar satellitaire (InSAR). Cette thèse est consacrée à la constitution d’un inventaire multi-dates à partir de données multi-sources (incluant les données InSAR) en vue d’évaluer de façon quantitative l’aléa glissement de terrain. Les méthodes associent l’analyse de produits d’Observation de la Terre et des modélisations statistiques pour la caractérisation de l’aléa dans la vallée de l’Ubaye, une région rurale et montagneuse des Alpes du Sud. Elles ont été développées à l’échelle du versant (1:5.000-1:2.000) et à l’échelle régionale (1:25.000- 1:10.000). Pour la création des inventaires, cette étude propose une interprétation combinée de séries temporelles d’images SAR, de photographies aériennes, de cartes géomorphologiques, de rapports historiques et de campagnes de terrain. A l’échelle locale, une méthodologie d'interprétation guidée par la géomorphologie et utilisant l’InSAR a été proposée pour identifier les champs de déplacement des glissements de terrain et mesurer leur évolution. A l’échelle régionale, la distribution spatio-temporelle des glissements de terrain a été caractérisée et l’aléa a été calculé à partir des probabilités d’occurrence spatiale et temporelle pour une intensité donnée des phénomènes. L’occurrence spatiale est estimée grâce à un modèle multivarié (régression logistique). L’occurrence temporelle des mouvements gravitaires est évaluée grâce à un modèle de probabilité de Poisson permettant de calculer la probabilité de dépassement (incluant ou non un seuil de surface) pour plusieurs périodes de retour. Plusieurs unités d'analyse spatiale ont été utilisées pour la modélisation ; les résultats démontrent clairement leur influence sur les résultats. L’analyse de l’aléa a été réalisée sur quelques cas spécifiques. Des relations entre les (ré)activations de glissements de terrain et les facteurs déclenchants sont proposéesThe analysis of landslide inventories is the basis for quantitative hazard assessment. Landslide inventory maps are prepared using conventional methods (field surveys, visual interpretation of aerial photographs) and new remote sensing techniques. One of the most promising techniques for landslide detection and mapping is related to the measurement of the ground deformation by satellite radar interferometry (InSAR).This doctoral thesis is dedicated to the preparation of a multi-date inventory, from multi-source data, including InSAR, for a quantitative assessment of landslide hazard. The methods associate the analysis of Earth Observation products and statistical modelling for the characterization of landslide hazard in a rural and mountainous region of the South French Alps. They have been developed at the slope (1:5000-1:2000) and the regional (1:25.000-1:10.000) scales. For the creation of a multi-date inventory, this study developed a combined interpretation of time series of SAR images, aerial photographs, geomorphological maps, historical reports and field surveys. At the slope-scale, a geomorphologically-guided methodology using InSAR was proposed to identify landslide displacement patterns and measure their kinematic evolution. At regional scale, spatio-temporal distribution of landslides is characterised and hazard is assessed by computing spatial and temporal probabilities of occurrence for a given intensity of the phenomena. The spatial occurrence is evaluated using a multivariate model (logistic regression). The temporal occurrence of landslide is estimated with a Poisson probability model to compute exceedance probabilities for several return periods. Different mapping units were used in the modelling, and their influence on the results is discussed. Analysis of landslide hazard is then proposed for some particular hotspots. Relationships between landslide (re)activations and triggering factors are envisaged
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KUMAR, ANMOL. "LANDSLIDE SUSCEPTIBILTY ZONATION MAPPING USING GIS FOR IDUKKI REGION." Thesis, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19708.
Повний текст джерелаАнотація:
When weathering causes a rock to crumble and decay, the shabby material, wet with rain water,
May float due to gravity.The phrase "land slippery" denotes to a rapid downhill slide movement of
rock rubble. They may grow on any piece of ground if the soil, moisture, and slope conditions are rig
ht. Landslides are an important part of the earth science activities on the surface of the planet and for
happening of those when he condition of the soil is good and moisture contain is maintained and
angle of slope must be maintained .Due to landslide failure of slopes, failure of earth surface and
flow of mud, flow of boulders, can happened .the main factor of the movement is due to either
earthquake which shakes the earth surface and movement of mass can happened and it could occur
due to when deep excavation could have been made for the construction of various structures like
buildings and it could happen because when the precipitation is heavy and its happening for long
duration like what happened in Idukki in 2019. Water is not only the factor for landslide or
movement of slope but weathering of rocks plays a predominant role in landslide .shear strength of
the rocks is reduced due to weathering. Many researchers have found that the main reason is weight
of building and their slope which act downwards due to gravity is one of the main reason for
movement .to prevent the movement of mass some resisting force is applied which is in the opposite
direction of friction angle and when the earthquake is for long duration the forces is automatically
reduced for different kind of landslide the movement speed will be different its depend on the weight
of the mass movement.
Частини книг з теми "SUSCEPTIBILTY ZONATION MAPPING"
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Kumar, Virender, and Kanwarpreet Singh. "Effectiveness of Remote Sensing and GIS-Based Landslide Susceptibility Zonation Mapping Using Information Value Method." In Lecture Notes in Civil Engineering, 225–34. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6717-5_22.
Повний текст джерела
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Yadav, Manish, Sanjit Kumar Pal, Prasoon Kumar Singh, and Neha Gupta. "Landslide Susceptibility Zonation Mapping Using Frequency Ratio, Information Value Model, and Logistic Regression Model: A Case Study of Kohima District in Nagaland, India." In Landslides: Detection, Prediction and Monitoring, 333–63. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23859-8_17.
Повний текст джерела
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Trucchia, Andrea, Giorgio Meschi, Paolo Fiorucci, Antonello Provenzale, Marj Tonini, and Umberto Pernice. "Spatial wildfire hazard patterns in the Eastern Mediterranean: perspectives from a harmonised approach." In Advances in Forest Fire Research 2022, 1311–17. Imprensa da Universidade de Coimbra, 2022. http://dx.doi.org/10.14195/978-989-26-2298-9_198.
Повний текст джерелаАнотація:
Wildfires are a menace which is growing in intensity and spreading in range across all planet’s ecosystems causing devastation on the environment, wildlife, human health, and infrastructure. Most of the damage caused by forest fires is related to extreme wildfire events (EWEs). To foster prevention activities, a thorough understanding of territorial features determining EWEs is crucial in Civil Protection and fire management activities. An approach which learns from past wildfire events providing susceptibility, intensity and hazard maps is presented. This mapping approach leads to the individuation of the main drivers of EWEs and in the zonation of the areas more prone to hazardous and impactful wildfire events. The case study where the mapping approach is applied encompasses thirteen countries of the Eastern Mediterranean and Southern Black Sea basins. The presented results focus on wildfire susceptibility. A Machine Learning approach is pursued, by adopting open data layers as both predisposing factors and past wildfire events. In particular, the role of vegetation continuity in determining the occurrence of EWEs is assessed.
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Singh, Nongmaithem Bragy, and Ramesh Veerappan. "GIS-based landslide susceptibility zonation mapping using fuzzy gamma operator model in part of Trans-Asian Highway (Mao-Kangpokpi), Manipur, India." In Disaster Resilience and Sustainability, 171–96. Elsevier, 2021. http://dx.doi.org/10.1016/b978-0-323-85195-4.00027-5.
Повний текст джерелаТези доповідей конференцій з теми "SUSCEPTIBILTY ZONATION MAPPING"
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Su, Fenghuan, and Peng Cui. "GIS-Based Susceptibility Mapping and Zonation of Debris Flows Caused by Wenchuan Earthquake." In 2009 International Conference on Information Engineering and Computer Science. IEEE, 2009. http://dx.doi.org/10.1109/iciecs.2009.5364077.
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Singh, Ankit, Tarun Singh, and K. S. Rao. "Comparative Study of Machine Learning Vs. BIS Approach for Landslide Hazard Zonation in Kashmir Himalayas, India." In 57th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2023. http://dx.doi.org/10.56952/arma-2023-0917.
Повний текст джерелаАнотація:
ABSTRACT Presently slope stability analysis and landslide hazard monitoring are the most challenging tasks in mountainous regions like the Himalayas. These events which were earlier considered as a process of attaining equilibrium in the topographic surface of the earth by nature, with the increase in population and onset of industrial revolution in the past few decades, changed the scenario of this natural phenomenon and transformed it into a disaster. The main reason for this is the utilization of inaccessible terrains for engineering mega projects and urbanization. Today, landslides are considered as the worst natural disasters and have become the objects of mass destruction and thereby new approaches for landslide study are being constantly developed with time to understand this natural phenomenon as much as possible. Witnessing the severity of this disaster in India and globally; this study is aimed to study landslide hazard in the domain of landslide susceptibility mapping and hazard zonation. This approach makes use of different instability causing parameters prevalent in the area to demarcate the region into different probable hazard zones. The resulting maps thus prepared are called Landslide Hazard Zonation (LHZ). For the study, a sub-part of Karewa Basin is selected as an area of study in Anantnag district, Jammu and Kashmir, India. Two different methods, the established Bureau of Indian Standards (BIS) method and the new machine learning based classification method using decision tree classification algorithm, are selected for the study of landslide hazard in the area. The resulting maps from both methods are analyzed and validated using the landslide inventory data. The results from both the methods are also used to perform a comparative analysis to examine which method yields better results. It is concluded that machine learning based methods, provided accurate training dataset can yield better results than the traditional methods which require a makeover to incorporate more data, made available with the advancement of technology.