Contents. 1.Introduction

Transcription

1 25/2/5 The 4th International Conference of the International Association for Computer Method and Advances in Geomechanics September 24, 24.Introduction Contents Method for hazard assessment to deep-seated catastrophic landslides due to heavy rain with both artificial neural network and mathematical statistics Shinichi ITO Osaka University, Japan Kazuhiro ODA Osaka University, Japan Keigo KOIZUMI Osaka University, Japan 2.Analytical methods 3.Categorical data originated from topographical information 4.Application of proposed method 5.Conclusions Contents Background.Introduction 2.Analytical methods 3.Categorical data originated from topographical information 4.Application of proposed method 5.Conclusions Typhoon Subsurface layer Surface failure Basement rock Heavy rain Sediment disasters

2 25/2/5 Background Heavy rain Typhoon Subsurface layer Background Heavy rain Typhoon Subsurface layer Debris flow Basement rock Deep-seated catastrophic landslide Basement rock Sediment disasters Deep-seated catastrophic landslides are big and rapid Sediment disasters Deep-seated catastrophic landslides Two factors of sediment disasters Typhoon Talas On September 2 Examples of deep-seated catastrophic landslides Dangerous Factor Endogenous factor Topography, Geology Exogenous factor Rainfalls, Ground water More than mm rainfall More than 5 deep-seated catastrophic landslides (larger than ) At least 73 deaths Difficult to forecast Simizu, Gojo city, Nara Ashinose, Tenkawa village, Nara Tsubouchi, Hiyamizu, Tenkawa village, Nara Nojiri, Totsukawa village, Nara Tsujido, Ui, Gojo city, Nara Nagatono, Totsukawa village, Nara A lot of slopes with higher hazard It is necessary to identify those slopes Establish analytical method to identify slopes with higher hazard based on endogenous factors

3 25/2/5 Problem A lot of slopes Time and costs Few detailed geological information Analytical method Analytical method Treat a lot of data Easily and effectively Treat topographical information Artificial neural networks and mathematical statistics should be applied Self-Organizing Map (SOM) Purpose Hayashi s second method 目的 Cluster analysis Purpose Proposal of a method that can identify the slopes with higher hazard to deep-seated catastrophic landslides.introduction Contents 2.Analytical methods 3.Categorical data originated from topographical information 4.Application of proposed method 5.Conclusions Proposed method Classified by SOM and cluster analysis

4 25/2/5 Proposed method Self-Organizing Map (SOM) Artificial neural networks Classified by SOM and cluster analysis High-dimensional vectors Two-dimensional space Slopes Similar characteristics Same cluster Self-Organizing Map (SOM) Artificial intelligence Weak point of SOM Two-dimensional space High-dimensional vectors Two-dimensional map Slopes Similar characteristics Same cluster Subjective judgments of users control results

5 25/2/5 Strong point Objective clustering Cluster analysis Combination SOM and cluster analysis 5 clusters Weak point Number of clusters is necessary Conceptual diagram of cluster analysis Exhibit:CONSUMER BEHAVIOR RESEARCH CO. SOM and cluster analysis are combined Number of clusters is determined by SOM Combination SOM and cluster analysis Combination SOM and cluster analysis 4 clusters 2 Results of cluster analysis are plotted on SOM 3 Number of clusters is changed

6 25/2/5 Combination SOM and cluster analysis Ⅱ Ⅲ Proposed method Classified by SOM and cluster analysis Ⅰ Ⅳ Ⅱ 4 Completion of clustering Failed slopes Mathematical statistics Slopes Estimate by sample score Objective understanding Not failed slopes Estimate slopes to failed or not failed %.% 75% 75.% 5% 5.% 25% 25.% % Cumulative distribution graph.% Stable Not failed Score Sample of boundary score.24 Failed Hazard Failed slopes Not failed slopes Sample score>score of boundary : Failed Sample score<score of boundary : Not failed

7 25/2/5 Classified by SOM and cluster analysis Proposed method %.% 75% 75.% 5% 5.% 25% 25.% % Cumulative distribution graph.% Stable Priority according to hazard Not failed Difference Score Sample of boundary score.24 Failed Hazard Priority of slopes with higher hazard is judged by the difference Failed slopes Not failed slopes Proposed method Characteristics of failed Classified by SOM and cluster slopes analysis can be classified Characteristics of failed and not failed appear clearly.introduction Contents 2.Analytical methods 3.Categorical data originated from topographical information 4.Application of proposed method 5.Conclusions

8 25/2/5 Target slopes and parameters Parameters Geographic Information System (GIS) Target slopes 42 slopes Failed slopes 4 slopes (Several area in Nara Prefecture Not failed slopes slopes Tenkawa village, Nara Prefecture Undercut slopes Main scarps Nara Prefecture River Parameters items ①Valley density ③Undercut slopes ⑤Contrary topography ⑦Zero point valley ⑨Knick line(convex) ⑪Gradient of slopes ②River or valley ④Zigzag topography ⑥Multistage topography ⑧main scarps ⑩Knick line(concave) Catchment area Valley density Topographical map of Tenkawa village Digital Elevation Model (DEM) 8ｍ Valley Valley Categorical data Parameters 85ｍ Ridge Valley River Failed slopes Zigzag topography Contrary topography Knick line (convex) Knick line (concave) 7ｍ Multistage topography Zero point valley Gradient of slopes Representation of categorical data Typical landslides topographies Cross-sectional views of slopes Valley River or Undercut Zigzag Contrary Multistage zero point density valley slope topograph topograph topograph valley ｍ Slope number A A2 A3 A4 A5 A6 A7 A8 A9 A A A2 A3 A4 A5 A6

9 25/2/5.Introduction Contents 2.Analytical methods 3.Categorical data originated from topographical information 4.Application of proposed method 5.Conclusions Classified by SOM and cluster analysis Analytical results Analytical result of clustering Topographical characteristics of each clusters Failed slopes River or Undercut Zigzag Contrary Multistage Zero point Main Knick line Knick line valley slopes topography topography topography valley scarps (convex) (concave) Cluster Cluster 2 Cluster 3 Cluster Clusters :Applicable to more than 9 % of slopes :Applicable to 9 % from 5 % of slopes :Applicable to 5 % from % of slopes :Applicable to less than % of slopes Failed slopes can be classified into four clusters Each clusters have the different characteristics

10 25/2/5 Classified by SOM and cluster analysis Analytical results Analytical results of identification Group A Real Failed Not failed Failed Estimate Not failed 9 Group C Real Failed Not failed Failed 7 2 Estimate Not failed 89 Group B Real Failed Not failed Failed Estimate Not failed 9 Group D Real Failed Not failed Failed 9 Estimate Not failed Proposed method 3 slopes with higher hazard are identified Characteristics of slopes identified from group A convex concave Characteristics of slopes identified from group B convex concave Zero point valley Main scarps Knick lines River Zero point valley Knick lines Topographical characteristics of cluster River or Undercut Zigzag Contrary Multistage Zero point Main Knick line Knick line valley slopes topography topography topography valley scarps (convex) (concave) Cluster =, (Cluster ) (estimated to failed) Topographical characteristics of cluster 2 River or Undercut Zigzag Contrary Multistage Zero point Main Knick line Knick line valley slopes topography topography topography valley scarps (convex) (concave) Cluster 2 =, (Cluster 2) (estimated to failed)

11 25/2/5 Characteristics of slopes identified from group C convex Analytical results Classified by SOM and cluster analysis Zero point valley Main scarps Knick line Topographical characteristics of cluster 3 River or Undercut Zigzag Contrary Multistage Zero point Main Knick line Knick line valley slopes topography topography topography valley scarps (convex) (concave) Cluster 3 (estimated to failed) =, (Cluster 3) Analytical result of ranking Slopes with higher priority Group A Group B Group C priority slope number difference C D D D A D C C B4.482 C7.9 priority slope number difference B D D B D C C C C C4.4 D4.4 priority slope number difference B B D B D A B D A6.37 D25.94 C A4.497 C9 can be given priority D6 km B2 Image c 24 DigitalGlobe

12 25/2/5 Slopes with higher priority (B2) Slopes with higher priority (D6) Main scarps Knick lines Zero point valley B3 River Undercut slopes River Undercut slopes Image 24 DigitalGlobe Slopes with higher priority (C9) Image 24 DigitalGlobe Contents.Introduction 2.Analytical methods Main scarps The slopes, identified by the proposed method, may have the possibility of deep-seated catastrophic landslides Typical landslides topographies Image 24 DigitalGlobe 3.Categorical data originated from topographical information 4.Application of proposed method 5.Conclusions

13 25/2/5 Conclusions Acknowledgement A method, based on SOM, cluster analysis, and was proposed in order to identify slopes with higher hazard The characteristics of slopes can be represented in categorical data. This study was funded by Grant-in-Aid for Scientific Research (245254), for which I wish to express my gratitude here. The categorical data can be applied into three analytical methods. The proposed method can identify slopes with higher hazard. The proposed method can prioritize these slopes according to hazard. Thank you for your kind attention! An example of SOM Slopes with higher priority (B2) Data of animals small middle state big noctumal two-legs four-legs mane possession hair ungulate feather stripe hunting running tendency flying swimming food plant-eating dove.5 duck.5 dog tiger.5 cow Difficult to determine similarity of animals Main scarps Zero point valley River Undercut slopes Data SIO, NOAA, U.S, Navy, NGA, GEBCO Image 24 DigitalGlobe

14 25/2/5 An example of SOM Raptors Analytical Result of Priority (Group A) Group A (Cluster+Not failed slopes) Birds Carnivorous animals slope valley river or undercut zigzag contrary multistage zero point main knick line knick line gradient of prioritydifference number density valley slopes topography topography topography valley scarps (convex) (concave) slope.629 C D D D A 6.76 D C C B C Plant-eating animals Easy to determine similarity of animals Characteristics Zero point valley, Main scarps, Knick lines Higher priority Typical landslide topographies Analytical Result of Priority (Group B) Group B (Cluster2+Not failed slopes) prioritydifference slope valley river or undercut zigzag contrary multistage zero point main knick line knick line gradient of number density valley slopes topography topography topography valley scarps (convex) (concave) slope.994 B D D B D C C C C C D4.25 Characteristics River, Zero point valley, Main scarps, Knick lines Higher priority Undercut slopes Analytical Result of Priority (Group C) Group C (Cluster3+Not failed slopes) river or undercut zigzag contrary multistage zero point main knick line knick line gradient of prioritydifference slope valley number density valley slopes topography topography topography valley scarps (convex) (concave) slope.2824 B B D B D A B D A D C A Characteristics Zero point valley, Main scarps, Knick line (convex) Higher priority Contrary topography, Multistage topography