Dissertations / Theses on the topic 'LANDSLIP SUSCEPTIBILITY MAPPING'

Thesis (MSc)--Stellenbosch University, 2013.
Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.

The purpose of this study is to evaluate and to compare the results of bivariate statistical analysis conducted with three different data sets in Geographical Information Systems (GIS) based landslide susceptibility mapping applied to the Devrek region. The data sets are created from the seed cells of crowns and flanks, only crowns, and only flanks of the landslides by using 10 different parameters of the study area. To increase the data dependency of the analysis, all parameter maps are classified into equal frequency classes based directly on the percentile divisions of each seed cells data set. The resultant maps of the landslide susceptibility analysis indicate that all data sets produce acceptable results. In each seed cell data set analysis, elevation, lithology, slope, aspect and drainage density parameters are found to be the most contributing factors in landslide occurrences. The results of the three data sets are compared by Seed Cell Area Index (SCAI). This comparison shows that the crowns data set produces the most accurate and successful landslide susceptibility map of the study area.

Thesis (M.S. in civil engineering)--Washington State University, May 2010.
Title from PDF title page (viewed on June 23, 2010). "Department of Civil and Environmental Engineering." Includes bibliographical references (p. 51-56).

Contact relations, and bedrock and overburden characteristics for approximately 8100 ha of the upper Canyon Creek basin, Skamania County, Washington, have been assessed in order to determine the causes and extent of failures and to assign slope failure susceptibilities to the area. The study area is located in the western Cascade Range on land administered by the Gifford Pinchot National Forest. Clear-cutting over the past 30 years has impacted between 50% to 80% of the study area. The total surface area occupied by failure deposits (198.6 ha) is less than 2.5% of the study area. Failures occur by one of seven processes, in decreasing order of abundance: rockfall (53.6%), rock avalanche (25.3%), slumps (15.6%), streambank failures (3.4%), soil and debris slips (1%), snow avalanches (debris falls) (1%), and translational slides (0.1%). Integrity of the bedrock is primarily influenced by jointing characteristics, in particular: dilation, orientation and continuity. Groundwater is an important constituent in the failure of fragmental igneous bedrock, but has very little impact in inducing failure in compact igneous bedrock. Areas underlain by fragmental igneous bedrock have a proportionally greater number of translational and rotational failures. With increasing compact igneous bedrock content, small volume rockfall failures become more predominant. Sixteen to twenty percent of the roadbed surfaces in the study area are experiencing some type of failure. Up to 99 percent of roadbed failures are confined to the roadfill prism. Failure due to degradation of the subgrade is rarely obseived. Arcuate and sliver-like cracks, offsets, sinkholes, concentrations of potholes, broad slumps and chute formation in the roadfill are indicators of failure. Ditches without culverts, or with poorly placed, damaged or leaking culverts, result in oversaturation and piping within the fill which may lead to failure of the road. The potential for slope failure is assigned a rating of low, moderate or high. These ratings are based on a qualitative assessment of the impact of various factors on the factor of safety, through their ability to reduce the cohesion and friction of affected rock and soil masses. Low susceptibility areas cover approximately 10 percent of the area (810 ha). Slopes are less than 3.5 degrees. Nearly 70 percent of the study area can be classified as moderately susceptable (5670 ha). Slopes in these areas range up to the natural angle of repose. The high susceptibility category covers areas with near vertical slopes, continuous rockfall, previous failures or strong indications of potential failure. These areas cover about 20 percent of the basin ( 1620 ha) and include areas of actual failure and adjacent areas which have not failed but possess similar bedrock, cultural and groundwater characteristics.

In recent years, rapid mass movements such as debris flow and debris avalanches resulted in a significant impact on Norwegian society and economy. The need for dispelling the uncertainty inherent in landslide risk assessment has encouraged the development of hazard and susceptibility maps. Different statistically-based modelling methods, in combination with geographic information systems (GIS), have been extensively used to ascertain landslide susceptibility in quantitative terms. This thesis proposes a bivariate statistical method (Weights of Evidence) for assessing the spatial proneness of debris flows within Førde and Jølster municipalities (Western Norway), where emphasis is put on the critical conditions of initiation. Since no feasible landslide database could be exploited for susceptibility mapping at medium scale, this thesis addressed the realisation of a new inventory. By coupling pre-existing data from remote sensing and field observations, circa 1100 debris flow initiation areas were outlined and differentiated in four categories with geomorphological repeatable features. Simple topography-based parameters such as slope, upslope contributing area, curvature and roughness were used to find significant statistical differences between the initiation areatypes. Moreover, they were employed together with other thematic maps as informative layers for landslide modelling. In order to test the model fitting performance, the ROC curves method is used in this thesis. The evaluation of different discretization schemes and combinations of the above-mentioned variables led to individuate models with different performances in terms of success rates. The best model is obtained by using only a combination of slope, flow accumulation and elevation (82% true positive rate), while the manual adjustment of the classification scheme did not lead to significant improvements.

This study aims to develop a Geographic Information Systems (GIS) and Remote Sensing (RS) Based systematic quantitative landslide risk assessment methodology for regional and local scales. Each component of risk, i.e., hazard assessment, vulnerability, and consequence analysis, is quantitatively assessed for both scales. The developed landslide risk assessment methodology is tested at Kumluca watershed, which covers an area of 330 km2, in Bartin province of the Western Black Sea Region, Turkey. GIS and RS techniques are used to create landslide factor maps, to obtain susceptibility maps, hazard maps, elements at risk and risk maps, and additionally to compare the obtained maps. In this study, the effect of mapping unit and mapping method upon susceptibility mapping method, and as a result the effect upon risk map, is evaluated. Susceptibility maps are obtained by using two different mapping units, namely slope unit-based and grid-based mapping units. When analyzing the effect of susceptibility mapping method, this study attempts to extend Logistic Regression (LR) and Artificial Neural Network (ANN) by implementing Geographically-Weighted Logistic Regression (GWR) and spatial regression (SR) techniques for landslide susceptibility assessment. In addition to spatial probability of occurrence of damaging events, landslide hazard calculation requires the determination of the temporal probability. Precipitation triggers the majority of landslides in the study region. The critical rainfall thresholds were estimated by using daily and antecedent rainfalls and landslide occurrence dates based on three different approaches: Time Series, Gumble Distribution and Intensity Duration Curves. Different procedures are adopted to obtain the element at risk values and vulnerability values for local and regional scale analyses. For regional scale analysis, the elements at risk were obtained from existing digital cadastral databases and vulnerabilities are obtained by adopting some generalization approaches. On the other hand, on local scale the elements at risk are obtained by high resolution remote sensing images by the developed algorithms in an automatic way. It is found that risk maps are more similar for slope unit-based mapping unit than grid&ndash
based mapping unit.

LiDAR (Light Detection and Ranging) elevation data were collected in the Panther Creek Watershed, Yamhill County, Oregon in September and December, 2007, March, 2009 and March, 2010. LiDAR derived images from the March, 2009 dataset were used to map pre-historic, historic, and active landslides. Each mapped landslide was characterized as to type of movement, head scarp height, slope, failure depth, relative age, and direction. A total of 153 landslides were mapped and 81% were field checked in the study area. The majority of the landslide deposits (127 landslides) appear to have had movement in the past 150 years. Failures occur on slopes with a mean estimated pre-failure slope of 27° ± 8°. Depth to failure surfaces for shallow-seated landslides ranged from 0.75 m to 4.3 m, with an average of 2.9 m ± 0.8 m, and depth to failure surfaces for deep-seated landslides ranged from 5 m to 75m, with an average of 18 m ± 14 m. Earth flows are the most common slope process with 110 failures, comprising nearly three quarters (71%) of all mapped deposits. Elevation changes from two of the successive LiDAR data sets (December, 2007 and March, 2009) were examined to locate active landslides that occurred between the collections of the LiDAR imagery. The LiDAR-derived DEMs were subtracted from each other resulting in a differential dataset to examine changes in ground elevation. Areas with significant elevation changes were identified as potentially active landslides. Twenty-six landslides are considered active based upon differential LiDAR and field observations. Different models are used to estimate landslide susceptibility based upon landslide failure depth. Shallow-seated landslides are defined in this study as having a failure depth equal to less than 4.6 m (15 ft). Results of the shallow-seated susceptibility map show that the high susceptibility zone covers 35% and the moderate susceptibility zone covers 49% of the study area. Due to the high number of deep-seated landslides (58 landslides), a deep-seated susceptibility map was also created. Results of the deep-seated susceptibility map show that the high susceptibility zone covers 38% of the study area and the moderate susceptibility zone covers 43%. The results of this study include a detailed landslide inventory including pre-historic, historic, and active landslides and a set of susceptibility maps identifying areas of potential future landslides.

Rainfall triggered shallow slides and debris flows constitute a significant hazard that causes substantial economic losses and fatalities worldwide. Regional-scale risk mitigation for these processes is challenging. Therefore, landslide early warning systems (LEWS) are a helpful tool to depict the time and location of possible landslide events so that the hazardous situation can be managed more effectively. The main objective of this thesis is to set up a regional-scale LEWS that works in real-time over Catalonia (NE Spain). The developed warning system combines in real-time susceptibility information and rainfall observations to issue qualitative warnings over the region. Susceptibility has been derived combining slope angle and land use and land cover information with a simple fuzzy logic approach. The LEWS input rainfall information consists of high-resolution radar quantitative precipitation estimates (QPEs). To assess if a rainfall situation has the potential to trigger landslides, the LEWS applies a set of intensity duration thresholds. Finally, a warning matrix combines susceptibility and rainfall hazard to obtain a qualitative warning map that classifies the terrain into four warning classes. The evaluation of the LEWS performance has been challenging because of the lack of a systematic inventory, including the time and location of recent landslides events. Within the context of this thesis, a citizen-science initiative has been set up to gather landslide data from reports in social networks. However, some of the reports have significant spatial and temporal uncertainties. With the aim of finding the most suitable mapping unit for real-time warning purposes, the LEWS has been set-up to work using susceptibility maps based on grid-cells of different resolutions and subbasins. 30 m grid-cells have been chosen to compute the warnings as they offer a compromise between performance, interpretability of the results and computational costs. However, from an end users’ perspective visualising 30 m resolution warnings at a regional scale might be difficult. Therefore, subbasins have been proposed as a good option to summarise the warning outputs. A fuzzy verification method has been applied to evaluate the LEWS performance. Generally, the LEWS has been able to issue warnings in the areas where landslides were reported. The results of the fuzzy verification suggest that the LEWS effective resolution is around 1 km. The initial version of the LEWS has been improved by including soil moisture information in the characterisation of the rainfall situation. The outputs of this new approach have been compared with the outputs of LEWS using intensity-duration thresholds. With the new rainfall-soil moisture hydrometeorological thresholds, fewer false alarms were issued in high susceptibility areas where landslides had been observed. Therefore, hydrometeorological thresholds may be useful to improve the LEWS performance. This study provided a significant contribution to regional-scale landslide emergency management and risk mitigation in Catalonia. In addition, the modularity of the proposed LEWS makes it easy to apply in other regions.
Els lliscaments superficials i els corrents d’arrossegalls són un fenomen perillós que causa significants perdudes econòmiques i humanes arreu del món. La seva principal causa desencadenant és la pluja. La mitigació del risc degut a aquets processos a escala regional no es senzilla. Ena quest context, els sistemes d’alerta són una eina útil per tal de predir el lloc i el moment en que es poden desencadenar possibles esllavissades en el futur, i poder fer una gestió del risc més eficient. L’objectiu principal d’aquesta tesi és el desenvolupament d’un sistema d’alerta per esllavissades a escala regional, que treballi en temps real a Catalunya. El Sistema d’alerta que s’ha desenvolupat combina informació sobre la susceptibilitat del terreny i estimacions de la pluja d’alta resolució per donar unes alertes qualitatives arreu del territori. La susceptibilitat s’ha obtingut a partir de la combinació d’informació del pendent del terreny, i els usos i les cobertes del sòl utilitzant un mètode de lògica difusa. Les dades de pluja són observacions del radar meteorològic. Per tal d’analitzar si un determinat episodi de pluja te el potencial per desencadenar esllavissades, el sistema d’alerta utilitza un joc de llindars intensitat-durada. Posteriorment, una matriu d’alertes combina la susceptibilitat i la magnitud del episodi de pluja. El resultat, és un mapa d’alertes que classifica el terreny en quatre nivells d’alerta. Amb l’objectiu de definir quina unitat del terreny és la més adient pel càlcul de les alertes en temps real, el sistema d’alerta s’ha configurat per treballar utilitzant mapes de susceptibilitat basats en píxels de diverses resolucions, i en subconques. Finalment, l’opció més convenient és utilitzar píxels de 30 m, ja que ofereixen un compromís entre el funcionament, la facilitat d’interpretació dels resultats i el cost computacional. Tot i això, la visualització de les alertes a escala regional emprant píxels de 30 m pot ser difícil. Per això s’ha proposat utilitzar subconques per oferir un sumari de les alertes. Degut a la manca d’un inventari d’esllavissades sistemàtic, que contingui informació sobre el lloc i el moment en que les esllavissades es van desencadenar, l’avaluació del funcionament del sistema d’alerta ha sigut un repte. En el context d’aquesta tesi, s’ha creat una iniciativa per tal de recol·lectar dades d’esllavissades a partir de posts en xarxes socials. Malauradament, algunes d’aquestes dades estan afectades per incerteses espacials i temporals força importants. Per a l’avaluació el funcionament del sistema d’alerta, s’ha aplicat un mètode de verificació difusa. Generalment, els sistema d’alerta ha estat capaç de generar alertes a les zones on s’havien reportat esllavissades. Els resultats de la verificació difusa suggereixen que la resolució efectiva del sistema d’alerta età al voltant d’1 km. Finalment, la versió inicial del sistema d’alerta s’ha millorat per tal poder incloure informació sobre l’estat d’humitat del terreny en la caracterització de la magnitud del episodi de pluja. Els resultats del sistema d’alerta utilitzant aquest nou enfoc s’han comparat amb els resultats que s’obtenen al córrer el sistema d’alerta utilitzant els llindars intensitat-durada. Mitjançant els nous llindars hidrometeorològics, el sistema emet menys falses alarmes als llocs on s’han desencadenat esllavissades. Per tant, utilitzar llindars hidrometeorològics podria ser útil per millorar el funcionament del sistema d’alerta dissenyat. L’estudi dut a terme en aquesta tesi suposa una important contribució que pot ajudar en la gestió de les emergències degudes a esllavissades a escala regional a Catalunya. A més a més, el fet de que el sistema sigui modular permet la seva fàcil aplicació en d’altres regions en un futur.
Enginyeria del terreny

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 proposées
The 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

Målet med denna studie är att framställa en modell för sannolikheten för skred i Cuenca del Arga i Navarra, Spanien, med hjälp av GIS och oskarp logik. Utifrån denna modell framställs en karta som visar områden där det finns sannolikhet för skred. Övergripande har metoden skett i fyra steg. Först har en skredindexkarta framställts, sedan har medlemsfunktioner skapats utifrån dessa med hjälp av skredriskparametrar. De ingående parametrarna har varit sluttningsvinkel, topografisk fuktighetsindex, avstånd till vattendrag, jordmån och markanvändning. Sedan har medlemsfunktionerna applicerats på parametrarna, som sedan slutligen kombinerats genom WLC (weighted linear combination). Detta resulterade i en sannolikhetskarta för skred med fem klasser.
The aim of this study is to construct a model in GIS (geographical information system) for landslide susceptibility mapping for Cuenca del Arga in Navarre, Spain, to identify potential areas for landslides. The model is based on fuzzy logic approach and the parameters are overlaid with WLC (weighted linear combination).

In West Bengal, India's Darjeeling Kalimpong area, landslides are a serious concern. With the use of four GIS-based techniques, including the Shannon Entropy (SE), Statistical Index Method (SIM), and Weight-of-Evidence (WoE), this study attempts to create an extensive map of landslip susceptibility. These techniques were chosen because they are good at managing huge datasets, tolerating various factors, and giving reliable estimates of landslip vulnerability. The research area was split into two parts: the first was for training the models, and the second was for model validation. A total of 13 conditioning factors were chosen and examined for their impact on the likelihood of landslides, including elevation, slope, aspect, curvature, distance from rivers, roads, and lineaments, lithology, land use/cover, stream power index, topographic wetness index, rainfall, and geology. Each strategy was put into practise, and the resulting maps of landslip susceptibility were compared and assessed. The analysis's findings demonstrated that all four models were useful for estimating the likelihood of landslides, with the SI, and WoE models performing somewhat better than the SE model. The Receiver Operating Characteristic (ROC) curve was used to evaluate the models' accuracy, and it revealed that the SI, SE, and WoE models had AUC values of 0.826, 0.77, and 0.825, respectively. The landslip inventory data was used to evaluate and validate the landslip susceptibility maps produced by the four models. The comparison revealed that for predicting landslip susceptibility, the SI and WoE models performed better than the SE model. This work provides useful data for land use planning and disaster management in the study region and shows the efficiency of GIS-based models in landslip susceptibility mapping.

碩士
國立臺灣大學
土木工程學研究所
104
The research aim is the manufacturing of potential map in different criterion of slope stability in the Tian Gul Creek Basin slate region. The research is based on possible failure types under different scale plan of slope, and establishes methodology of map production. A 1/50000 geological map means that every 500 meters of distance should consist of one state. Based on its resolution, the average area 25 hectares decides the size of a slope unit. However, as a 25-hectare slope unit area, its internal aspect and state information still show a great deal of differences, thus cannot fully explain the type of a slope failure. Moreover, the damage of slate slope is influenced by the gravitational creep and the plane of weakness on the joint. Hence, considering the making of large-scale engineering geology map along with the future project planning and design, this research chose a micro-scale slope to analyze, in hope to find the actual damage condition of the smallest slope unit for future reference. Object Oriented Analysis software eCognition that produces slope unit in object form compares past correlate researches with the result by segmentation process built by its own rules. Sky-view map inputs one-river watershed frame layer in the beginning, and it is the biggest difference of all. Since the large reduction of time that river thresholds and build watershed frame from Geographic Information System (GIS) results from Sky-View map, we can tell the distinction of the water system and ridge line from the DTM in different resolution. In order to understand the destruction of slope, GCPS on the two sides of the slopes was built in 2016 and DSM was produced by UAV completed by point coordinates token from total station. The 5m DTM and self-made DSM were compared to create the downstream of Tian Gul Creek based on Object Oriented Analysis. Then the two parts of representative were chosen to find out the different resolution. Finally, the four different scales were displayed, namely 1/50000, 1/25000, 1/5000 and 1/1000.

碩士
國立成功大學
自然災害減災及管理國際碩士學位學程
105
Landslides pose threats not only to infrastructure around the world but also to local communities. One particularly susceptible area in Taiwan is in the Zhoukou River basin, Kaoping watershed. This study aimed to produce a Classification and Regression Tree (CART) model using R software that accurately predicts landslides in this area by validating the predictions against those observed recorded landslides in this region. The landslide data were recorded in the year of 2010, a year after typhoon Morakot stroked Taiwan in 2009, triggering huge number of landslides all over the cou[ntry. This study proposed the new concept to separate landslides area into release as its source and focuses on using the topographical factors derived from Digital Elevation Model (DEM) as the independent variable in predicting landslides occurrence, including Slope, Aspect, Curvature, Topographic Wetness Index, Average Slope and Distance from the river, and an additional Geological map of the study area. An observed landslide release occurrence layer posed as the dependent variable classifier in the model. First, data sampling strategies applied show an optimal model to be created with the highest Area Under Curve (AUC) value of 0.73. Next, this model identified the most influential factors causing landslides by using information gain’ statistics in R software. Aspect, were determined as being most influential factor, where Distance from river, and Slope as second and third most influential. The concept of release area separation showed a better AUC value model compared to the model using conventional full landslide inventory. The decision tree model also showed a reliable result when compared to logistic regression and random forest using the same data sampling, with the AUC value of 0.73, 0.65, and 0.81 respectively. The results have proven that decision tree model is suitable for producing landslide susceptibility map.

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.

碩士
國立臺灣大學
土木工程學研究所
103
On average, three to four typhoons attack Taiwan each year. Although typhoon rainfall is an important source of water resources, the heavy rainfall brought by typhoons frequently result in serious disasters. Landslide is one of the most destructive slope disasters. Therefore, to establish a landslide susceptibility model, which can efficiently mitigate the disaster, is always an important task of slope disaster management. In this study, three methods are employed to construct landslide susceptibility models for the Kaoping River basin in southern Taiwan, and then the model performances of these three models are compared. The three methods include the conventional logistic regression (LR) and two novel machine learning methods, namely, Support Vector Machine (SVM) and Improved Self-organizing Linear Output Map (ISOLO). Landslide events from 2008 to 2011 are collected. The first three-year data from 2008 to 2010 are used in the training phase of the models, and the remaining data are for testing. Moreover, fourteen landslide-related factors are used in the landslide susceptibility analysis, such as slope, slope aspect, elevation, curvature, profile curvature, plan curvature, slope length, topographic wetness index, distance to river, distance to road, distance to fault, 24-hour rainfall and 48-hour rainfall. The performances of three models are checked by the accuracy and the area under the receiver operating characteristic curve (AUC). The results show that the ISOLO model outperforms over the LR and SVM models in the study area. Landslide susceptibility maps obtained from the proposed model are expected to be helpful to local administrations and decision makers in disaster planning.

Landslide dams result from the complex interaction, not yet totally understood, between river and slope dynamics. The study of past landslide dams and their consequences has acquired a significant relevance for forecasting and preventing their induced hydraulic risk on lives and property. The main aim of this thesis was the study of the landslide dam phenomenon and design a useful and easy tools to assess the damming risk with spatial planning purpose. The research started from the geomorphologic investigation of the Italian landslide dams and setting up an archive, updating previous studies on the same topic in smaller areas (Pirocchi, 1991; Ermini, 2000; Pacino, 2002), and integrating it through a careful literature review and cartographic and aerial photos interpretation. The collected data represents the wider example of systematic inventory in Italy, with almost three hundreds of cases selected from the Alps to the Southern Apennines, in Sicily. The research includes landslide dams occurred along the Cordillera Blanca mountain range, in Peru, to study the same phenomenon in a very different geographical, climatic and tectonic settings. A morphological analysis of the collected data was performed to identify morphometric parameters that best define the formation process of a blockage. This analysis confirmed the validity of schematizations already developed by previous authors and new morphometric indexes, useful for forecasting and planning purposes, were proposed. In particular, encouraging result came from the formulation of the Morphological Obstruction Index (MOI) that allowed to perform a reliable analysis of dam formation and provided a good estimator to forecast a landslide blocking a river, from a geomorphic analysis. In order to prevent part of the damages and suffer lower consequences related to landslide dam occurrence, an useful and practical tool was proposed, to predict which areas have a higher damming susceptibility and where preventive measures should be focused. Therefore a simple GIS methodology, useful as a forecasting and planning tool, was developed. This easy methodology, used on the Arno River basin, was able to assess with few data the damming predisposition, connected to existing landslides, and the probability of obstruction, by new landslides along a river network.

The Saint Lawrence Lowlands in eastern Canada contain extensive deposits of marine soils deposited in post-glacial seas during and following the retreat of the most recent continental glacier. These marine soils include silt and clay deposits known collectively as Champlain clay. When the pore fluid in these marine deposits has changed over time to a lower salinity, the clay can become very sensitive, or demonstrate substantial strength loss after reaching the peak strength with sufficient strain under undrained load conditions. Sensitive clay soils are subject to a peculiar type of very large landslide that typically involves great extents of nearly horizontal ground, usually occurring suddenly and without warning. These landslides tend to be described as “retrogressive” in the literature and practice, implying that they develop as a series of successive small failures that advance rearward until a final stable position is reached. The work of this thesis is organized into four different themes, with an overall objective of understanding the hazard and risk associated with large landslides in sensitive clay to linear infrastructure such as railways. The first theme, documented in Chapter 2, develops a number of spatial relationships between specific physiographic and geologic features and landslide occurrence or absence, as determined through air photo analysis and a review of the literature. The second theme, documented in Chapter 3, presents the construction of a digital database of large landslides in sensitive clay in eastern Canada, for the purposes of studying landslide susceptibility, hazard and risk. The third theme, documented in Chapters 4 and 5, presents and defends a novel mechanical model for development of these large landslides. This model suggests the landslides develop progressively, rather than retrogressively, and the science of fracture mechanics is employed to substantiate the model. The fourth theme, documented in Chapters 6 and 7, synthesizes the findings of the earlier themes and presents a methodology for estimating landslide susceptibility in Champlain clay. That approach is then extended to develop an understanding of the hazard. The concluding chapter extends that work to present an initial appreciation of landslide risk to railways.
Thesis (Ph.D, Geological Sciences & Geological Engineering) -- Queen's University, 2009-04-23 13:22:19.53

碩士
國立中興大學
水土保持學系所
100
In Taiwan, the landform is highly; precipitously and belong young geological that is why the geological fragile. Then belongs to the monsoon climate zone, resulting in steep terrain, fragile geology, typhoons often along with a heavy rainfall, leading to the phenomenon of often landslides, land slide, debris flow. Densely populated condition cause to Serious hillside disasters. After the 921 earthquake Taiwan hillside showing loose and fragile. There are a lot of hillside disasters which are caused by extreme storm in recent years in Taiwan. The majority of the hillside disasters in Taiwan are falling rocks, strata slip, mudflows, landslides and creep. The extreme storm and earthquakes endanger the life and property of the people who are protected by the government. The stability of hillsides depends on geomaterials and terrain condition. And the induction factors of collapse and landslide are rainfall and over capacity use of slope land and so on. This study are mainly research on Analysis of the regional mountain landslide susceptibility. The study area was located Chen Yu Lan River of Fong-Ciou region. For landslide susceptibility in Fong-Ciou region Compile the relevant literature and integration of the Modified Delphi method and Analytical Hierarchy Process as a research method. The impact factor of the landslide include potential factor, topography factor, induced factor, location factor, fourth factor and made to repair the design of the questionnaire of the Modified Delphi Method. Further to establish Characteristics of landslide susceptibility assessment criteria table Expert questionnaire seeking to take out a comprehensive assessment of landslide susceptibility index. And further use of the Analytic Hierarchy Process (AHP) to analyze calculate the landslide impact factor weight values as basis for the assessment. Combination of aerial images produced by digital elevation model (DEM) use GIS Analytic Hierarchy Process (AHP) to make the weight value the overlay Fong-Ciou region layer information. Export data in Fong-Ciou region landslide susceptibility map. Landslide susceptibility map drawn out by the Institute in accordance with the dangerous landslide susceptibility, Divided into low susceptibility, susceptibility region of high susceptibility, high susceptibility region. This area of partition as a measure of the current status of the stability and extent of the Fong-Ciou region. And to estimate the present stability of hillsides as ways for references in the future.

碩士
國立中興大學
水土保持學系所
102
As pointed out in many previous studies, climate change due global warming will result in the increases of the frequencies and intensities of storm events；Due to fragile geology, soil and torrential rain leads to severe erosion. Furthermore, increasing population and overdevelopment brings even greater damage to the land. The site of this study was selected at Chenyulan stream watershed.The study focuses on the landslides induced by the Typhoon Sinlaku occurring in 2008 and the Typhoon Morakot occurring in 2009.This study used GIS as a tool to map storm-induced landslides from SPOT images. Digital elevation model (DEM) was used to extract geomorphic landslide causative factors. SPOT image was also used to calculate an environmental factor - NDVI (normalized differential vegetation index). This study analyzes 8 factors including elevation, slope, aspect, relief, roughness, distance to roads and distance to rivers. Using the hourly maximum rainfall related to spatial information by typhoon event as trigger factor. We sample equal cell number of data randomly for landslide group and non-landslide group, then input those data to SPSS statistical software and build a logistic model for the study area. Furthermore, error matrix was classified using of classification accuracy to evaluate the effects of causative factors on the landslides at watershed. The result shows that the overall accuracy in typhoon Sinlaku and typhoon Morakot these two events are 92.2% and 90.2% respectively. Most of the actual landslide data fall in the high-moderate and high susceptibility class respectively. It indicates that the results are satisfactory. The landslide potential maps in this research can provide to supervisor of watershed to monitor the landslide.

碩士
國立中興大學
水土保持學系所
103
The Jing-Shan River is a tributary of Da-An River watershed, which is located at Li-Yu-Tan reservoir in the downstream. The facility functions as not only an electricity generator, flood preventer, farmlands irrigator as well as a tourist attraction but also the water supplier of Miao-Li and Tai-Chung district. Recently, the torrential rain that come with typhoons and extreme weathers has caused many landslides in the watershed and severely shrunk the capacity and usability of reservoir. This study used the inventories of landslide established by Central Geological Survey as the landslide data. Logistic regression, Instability index method and Support vector machine (SVM) were selected to establish the landslide susceptibility models and obtain the landslide susceptibility maps in the upstream areas of Jing-Shan River. Ten landslide causative factors were first chosen as the landslide causative factors, according to the previous studies. A selection procedure was then performed to efficiently reduce the number of factors. Further, the receiver operating characteristic curve was used to evaluate the accuracy of model results. As a result, Logistic regression and Instability index method both show that the terrain roughness is a critical factor on the susceptibility value. The instability index method is not only lead to possible underestimation around the river side but also the number of factor classification can impact the result. SVM establish the model by classifying the landslide data. The landslide susceptibility values is not prone to particular factors. Therefore, the results of model prediction is not influenced by the weights of factor. Furthermore, the landslide susceptibilities is classified into four ranks, including: low, medium, medium-high, and high. SVM and logistic regression is suggested to be superior to Instability index method due to recognize the landslides located at the medium-high susceptibility areas. The analysis of area under the curve (AUC) showed AUC of 0.825 for SVM, 0.721 for the logistic regression, and 0.718 for the instability method. This further suggests SVM is a preferred method among the others in assessment of landslide in the research areas.