Landslide Risk Assessment In Wanzhou District And A Key Section,Three Gorges Reservoir

Many regions of China are suffering from various geological disasters.Landslide is the main type of geological disasters in the Three Gorges reservoir area.It directly threatens people's lives and economic properties,and may induce secondary disasters,such as surges caused by slopes flowing into the river.Meanwhile,surges could affect channel safety,even to closure if necessary,which belongs to indirect losses caused by landslides.The colluvial landslide in Wanzhou District is one of typical landslide types in the reservoir area,which has the characteristics of slow deformation and large volume.Once these large-scale landslides occur,the consequences are unthinkable.Landslide susceptibility mapping and risk assessment in Wanzhou District are significant for disaster prevention and mitigation.At the same time,the quantitative landslide risk assessment in multiple cases can provide a scientific bases for government to manage disasters.This thesis is a twofold study of landslide risk assessment.The first fold takes the entire Wanzhou District as the research area.Firstly,predicted the spatial distribution of landslide susceptibility,based on geomorphology and geological environment factors that affect the formation and development of landslide.Then,six comparison maps were created from four susceptibility maps to analysis the different spatial features of susceptibility maps.Explored the reasons of different outcomes of susceptibility maps,from the intrinsic principle of models,landslide development characteristics,and geomorphological characteristics of the study area.Finally,the landslide risk assessment was performed,in which rainfall was used as the landslide-inducing factor.The second fold is selected a key section of Wanzhou District as the study area.A deterministic model based on accurate soil thickness parameter was used to quantitative assess the landslide risk under several combination cases.The landslide risk assessments at two basin scales have different requirements for the accuracy of databases and evaluation.Therefore,in this thesis,different evaluation methods are adopted for Wanzhou District and the key section to achieve a rough to accurate risk assessment.The main research contents and conclusions are as follows.(1)Landslide susceptibility assessment based on statistical and machine learning method in Wanzhou District.According rich literatures,the hydrogeological database,detailed landslide database and field geological surveys,14 landslide causal factors related to landslide development were selected.Four combinations of causal factors were identified after the correlation analysis among 14 factors.Landslides susceptibility maps were performed based on three statistical models(frequency ratio,certainty factor and index of entropy)and machine learning model(random forest)under four combinations of factors.The research shows that the best performance of the four models appears in different factor combinations.Then,the accuracy analysis of landslide susceptibility maps was carried out.According to the area value under the success rate curve,the random forest model is most suitable for the landslide susceptibility evaluation in Wanzhou District,followed by the entropy index model and the certainty coefficient model,and the worst is the frequency ratio model.Finally,the spatial distribution characteristics of high-prone areas were summarized through the contribution of each index factor to the landslide event in the statistical model.Landslides in Wanzhou District mainly are Yangtze River reservoir landslides,and developed along both sides of the Yangtze River.At the same time,the level of the reservoir fluctuates between 145 meters and 175 meters,affecting range mostly below 350 meters altitude,which may be the reason for elevation category 1(less than 350 meters)has the highest contribution.The middle Jurassic Shaximiao Group,consisting of alternating layers of sandstone and mudstone,is the most widely distributed geological unit.Many landslides were induced by water level fluctuation and rainfall.Such as the well-known Caojiezi landslide and Taibaihe landslide with a volume of more than ten million cubic meters,and they all developed in sub-horizontally dipping sandstone and mudstone interbedded strata.Thus,Jurassic Shaximiao Formation with slopes of 6-14 ° and 14-21 °,sandstone and mudstone interlayers,and areas where the land use type is water area,presented a prone to landslides in susceptibility map.Statistics of the distance from landslide to the river show that the areas close to the river have a higher susceptibility than areas feather away.However,in the statistics of road distances,it is not clear which category has a prone to landslide.It means that landslides in Wanzhou District are mostly affected by "water" rather than by cutting slopes.(2)A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches.Landslide susceptibility assessment is vital for landslide risk management and urban planning,and the scientific community is continuously proposing new approaches to map landslide susceptibility,especially by hybridizing state-of-the-art models and by proposing new ones.A common practice in landslide susceptibility studies is to compare(two or more)different models in terms of AUC(area under ROC curve)to assess which one has the best predictive performance.The objective of this chapter is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered.To this aim,we used four susceptibility maps of the Wanzhou County(China)obtained with four different classification methods(namely,random forest,index of entropy,frequency ratio,and certainty factor).A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis,to reveal systematic spatial patterns in the differences among susceptibility maps;then,those patterns were put in relation with all the explanatory variables used in the susceptibility assessments.The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified.The results shed a new light on the susceptibility models,identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site,such as nearly flat low altitude areas near themain rivers,and some lithological units(3)Landslide risk assessment in Wanzhou District based on the time of rainfallinduced landslide events.The landslide disaster database of Wanzhou District includes time,place,scale,rainfall et al.The landslide inventory shows that most of the landslides in Wanzhou District are related to rainfall events directly or indirectly.Therefore,the recurrence period and the probability of rainfall maximum are taken as the time factors for inducing landslides.The Gumbel curve and L-moment method were used to carry out a probabilistic analysis of the annual rainfall in Wanzhou District in the past fifty years,and the maximum amount of rainfall in the next year in different periods was obtained.Based on the landslide susceptibility map built by random forest model,the spatial probability of landslide occurrence within each level is counted,and the hazard value is obtained by combining time and spatial probability.Investigate and identify the landslide victims,carry out their value estimation and vulnerability analysis,and complete the landslide disaster risk assessment.The results show that landslide risk in the main urban area of Wanzhou District is the largest,followed by the reservoir section of Tangjiao VillageDazhou Town.The main urban area has been the main target of landslide disaster prevention for government departments,and there have been a large number of effective disaster reduction measures.Due to the large scope of the whole region,the impact of disaster mitigation measures on risk reduction is not considered in this risk assessment.The actual risk in urban areas should be less than the calculated risk in this section.Therefore,the Tangjiao Village-Dazhou Township reservoir bank section was selected as the key reservoir section to access more detailed and quantitative landslide risk.(4)Generating distributed soil thickness maps by means of a geomorphologicalempirical approach and a random forest algorithm in the key section of Wanzhou County,Three Gorges reservoir.Soil thickness is an important input parameter for many environmental models.Nevertheless,in large scale applications,it is often difficult to obtain a reliable distributed soil thickness map and existing methods have been applied only to test sites with shallow soil depth.In this paper,we cope with this limitation showing the results of an application in a section of Wanzhou County(Three Gorges Area,China),where soil thickness ranges from zero to about forty meters.Two different approaches were used to derive distributed soil thickness maps: a modified version of the Geomorphologically Indexed Soil Thickness(GIST)model,purposely customized to better take into account the peculiar setting of the test site,and a regression performed with a machine learning algorithm,the Random Forest(RF),combined with the geomorphological parameters of GIST.The proposed models are implemented in a geographic information system environment on a pixel-by-pixel basis.Finally,validation quantifies errors of the two models and a comparison with geophysical data is carried out.The results showed that the GIST model is not able to fully grasp the high spatial variability of soil thickness of the study area: mean absolute error was is 10.68 m with 7.94 m standard deviation,and the frequency distribution of residuals showed a proneness to underestimation.In contrast,RF returned a better performance(mean absolute error is 3.52 m with 2.92 m standard deviation),and the derived map could be considered to be used in further analyses to feed models that require a distributed soil thickness map as a spatially distributed input parameter.(5)Landslide risk assessment in the key section of Wanzhou District.A three-dimensional deterministic model was selected to evaluate landslide susceptibility in the key section,which requires several parameters,such as soil thickness map,groundwater distribution,and geotechnical parameters.The calculated condition consists of the decline in reservoir water level and rainfall.The scale of the potential landslide body was regarded as the intensity index after the disaster.Landslide hazard assessment was performed,in which rainfall was used as the landslide-inducing factor.After that,a special consideration is given to the impact of the treated projects.Landslides that have been implemented with effective anti-slide treatment and landslides that have only implemented slope protection or drainage works are subject to different degradation treatments based on the existing danger level.Residents,buildings,roads,and land userelated information in the study area are investigated to assess landslide population casualty risk and landslide economic loss risk in the key section.With the decline of reservoir water level and the drop rate increases,landslide population casualty risk is increasing.During the fall of reservoir water level,real-time disaster monitoring and early warning work should be carried out to deal with hidden dangers of landslides.Provide notifications to households,and train them on how to prevent and mitigate landslide consequences At the water level of 159 meters,the landslide economic risk is the smallest,and the risk level is low.When the reservoir water level drops,the risk gradually increases,and the faster the decline,the more risk increases.For example,at the Tangjiao landslide,at the water level of 159 m,the landslide is a medium risk,except low risk in the trailing edge portion.At 145 m water level in case 1,the trailing edge portion became medium risk,and the leading edge is strongly deformed.At the 145 m water level in Case 2,the Tangjiao landslide presents high risk state.The Sifangbei landslide exhibited low risk at the water level of 159 m reservoir and 145 m in case 1,but the leading edge showed medium risk at 145 m case 2.The risk assessment maps are displayed quantitatively in slope units.The obtained landslide risk zoning maps with slope units are more in line with the actual situation,which means a higher application value.These maps could provide a scientific bases for landslide risk management.