This presentation summarizes advanced landslide assessment methods applied at the Halenkovice experimental site in the Czech Republic. A variety of data collection and modeling methods were used, including field surveys, remote sensing, statistical analyses, and machine learning techniques like support vector machines. The results show improved accuracy over earlier regression models, though further refinements are still needed, like increasing the number of folds in model optimization and exploring multi-class scenarios. Overall, the study demonstrates the promise of comprehensive, multi-method landslide assessments for informing land use planning and hazard management.
GIS-3D Analysis of Susceptibility Landslide Disaster in Upstream Area of Jene...AM Publications
The assessment of landslide hazard and risk has become a topic of major interest for both geoscientists and engineering professionals as well as for local communities and administrations in many parts of the world. Recently, Geographic Information Systems (GIS), with their excellent spatial data processing capacity, have attracted great attention in natural disaster assessment. In this paper, an assessment of landslide hazard at Upper Area of Jeneberang Watershed has been studied using GIS technology. By simulating the potential landslide according the minimum safety factor value using GIS, it can be expected that great contribution as a basic decision making for many prevention works before future landslide occurs at upstream area of Jeneberang River Watershead, South Sulawesi, Indonesia
Landslide Risk Reduction Plan for Pashupati Monument Zone (Kathmandu Valley ...Akrur Mahat
The risk of natural hazards on cultural heritage is a crucial issue that demands a multi-disciplinary approach to address it appropriately and efficiently. The significant loss of heritage due to recent Gorkha earthquake 2015 has highlighted the lack of risk assessment of cultural properties and implementation of comprehensive risk reduction plan. The monitoring and evaluation of the state of conservation of individual cultural heritage property are the fundamental and essential task in the overall assessment of vulnerability.Conservation plan of action for the monuments and environment should be formulated and prioritized by heritage value of the property. Also, the safeguarding cultural properties from natural hazards also requires a comprehensive strategy that includes risk assessment and the participation of all stakeholders. This study tries to assess the vulnerability of cultural heritage property and find out the level of landslide risk which will help to prepare landslide risk reduction plan for the effective management of the every cultural property within the Pashupati Monument Zone.
A combination of extensive field survey, local and expert knowledge has been used to extract information of landslide and monument.A landslide hazard susceptibility map of Pashupati Monument Zone has been prepared using frequency ratio model in GIS software.Parameters considered are slope aspect, slope angle, elevation, drainage distance, geology and land use. The vulnerability of 290 monuments have evaluated through a combination of multiple criteria as the state of conservation and a heritage value, a combination of both served as an input factor for the physical vulnerability of the cultural properties of the entire zone. Landslide risk has been calculated combining the landslide hazard susceptibility and vulnerability of monuments within the cultural heritage site.
Final results show that Pashupati monument zone has 15% high, 31% medium landslide hazard area.Similarly, out of 290 monuments 5% (15 nos) lies in high and 38% and 57 % are in medium and low landslide risk.Findings depict that the cultural properties assessed in this area are mostly affected in the Slesmantak forest area (master plan B1 zone) where high hazard landslide area has founded.Finally, some recommendations are proposed related to conservation of environment and monuments in the Pashupati Monument Zone.
Key Words:
Cultural heritage, Heritage value, State of conservation, Landslide hazard mapping, Risk Assessment, Landslide Risk Reduction plan.
GIS-3D Analysis of Susceptibility Landslide Disaster in Upstream Area of Jene...AM Publications
The assessment of landslide hazard and risk has become a topic of major interest for both geoscientists and engineering professionals as well as for local communities and administrations in many parts of the world. Recently, Geographic Information Systems (GIS), with their excellent spatial data processing capacity, have attracted great attention in natural disaster assessment. In this paper, an assessment of landslide hazard at Upper Area of Jeneberang Watershed has been studied using GIS technology. By simulating the potential landslide according the minimum safety factor value using GIS, it can be expected that great contribution as a basic decision making for many prevention works before future landslide occurs at upstream area of Jeneberang River Watershead, South Sulawesi, Indonesia
Landslide Risk Reduction Plan for Pashupati Monument Zone (Kathmandu Valley ...Akrur Mahat
The risk of natural hazards on cultural heritage is a crucial issue that demands a multi-disciplinary approach to address it appropriately and efficiently. The significant loss of heritage due to recent Gorkha earthquake 2015 has highlighted the lack of risk assessment of cultural properties and implementation of comprehensive risk reduction plan. The monitoring and evaluation of the state of conservation of individual cultural heritage property are the fundamental and essential task in the overall assessment of vulnerability.Conservation plan of action for the monuments and environment should be formulated and prioritized by heritage value of the property. Also, the safeguarding cultural properties from natural hazards also requires a comprehensive strategy that includes risk assessment and the participation of all stakeholders. This study tries to assess the vulnerability of cultural heritage property and find out the level of landslide risk which will help to prepare landslide risk reduction plan for the effective management of the every cultural property within the Pashupati Monument Zone.
A combination of extensive field survey, local and expert knowledge has been used to extract information of landslide and monument.A landslide hazard susceptibility map of Pashupati Monument Zone has been prepared using frequency ratio model in GIS software.Parameters considered are slope aspect, slope angle, elevation, drainage distance, geology and land use. The vulnerability of 290 monuments have evaluated through a combination of multiple criteria as the state of conservation and a heritage value, a combination of both served as an input factor for the physical vulnerability of the cultural properties of the entire zone. Landslide risk has been calculated combining the landslide hazard susceptibility and vulnerability of monuments within the cultural heritage site.
Final results show that Pashupati monument zone has 15% high, 31% medium landslide hazard area.Similarly, out of 290 monuments 5% (15 nos) lies in high and 38% and 57 % are in medium and low landslide risk.Findings depict that the cultural properties assessed in this area are mostly affected in the Slesmantak forest area (master plan B1 zone) where high hazard landslide area has founded.Finally, some recommendations are proposed related to conservation of environment and monuments in the Pashupati Monument Zone.
Key Words:
Cultural heritage, Heritage value, State of conservation, Landslide hazard mapping, Risk Assessment, Landslide Risk Reduction plan.
A low cost landslide method for mitigating landslide risk, in an urban community. Initiative operationalized through a Framework Agreement between the National Disaster Office of Jamaica the General Counsel of Martinique.
Surface-related multiple elimination through orthogonal encoding in the laten...Oleg Ovcharenko
We explore the feasibility of surface-related multiple elimination by two-step separation where primaries and multiples are separated in the latent space of a convolutional autoencoder. First, we train a convolutional autoencoder to produce orthogonal embeddings of primaries and multiples. Second, we train another network to classify the latent space embedding of target data into respective wave types and decode predictions back to the data domain. Moreover, we propose an end-to-end workflow for the generation of realistic synthetic seismic data sufficient for knowledge transfer from training on synthetic to inference on field data. We evaluate the two-step separation approach in synthetic setup and highlight the strengths and weaknesses of using masks in encoder latent space for surface-related multiple elimination.
A low cost landslide method for mitigating landslide risk, in an urban community. Initiative operationalized through a Framework Agreement between the National Disaster Office of Jamaica the General Counsel of Martinique.
Surface-related multiple elimination through orthogonal encoding in the laten...Oleg Ovcharenko
We explore the feasibility of surface-related multiple elimination by two-step separation where primaries and multiples are separated in the latent space of a convolutional autoencoder. First, we train a convolutional autoencoder to produce orthogonal embeddings of primaries and multiples. Second, we train another network to classify the latent space embedding of target data into respective wave types and decode predictions back to the data domain. Moreover, we propose an end-to-end workflow for the generation of realistic synthetic seismic data sufficient for knowledge transfer from training on synthetic to inference on field data. We evaluate the two-step separation approach in synthetic setup and highlight the strengths and weaknesses of using masks in encoder latent space for surface-related multiple elimination.
In topological inference, the goal is to extract information about a shape, given only a sample of points from it. There are many approaches to this problem, but the one we focus on is persistent homology. We get a view of the data at different scales by imagining the points are balls and consider different radii. The shape information we want comes in the form of a persistence diagram, which describes the components, cycles, bubbles, etc in the space that persist over a range of different scales.
To actually compute a persistence diagram in the geometric setting, previous work required complexes of size n^O(d). We reduce this complexity to O(n) (hiding some large constants depending on d) by using ideas from mesh generation.
This talk will not assume any knowledge of topology. This is joint work with Gary Miller, Benoit Hudson, and Steve Oudot.
Vávra, A: Phenological Observation Treatment in the Landscape Mapping of the ...
Marjanović, M: Advanced Landslide Assessment of the Halenkovice Experimental Site
1. Advanced Landslide Assessment of the
Halenkovice Experimental Site
Miloš Marjanović
This presentation is co-financed by the
European Social Fund and the state
budget of the Czech Republic
2. Introduction
Motifs:
raising awareness
need for diverse case studies at different
scales, using different methods
applicability (decision making for land use planning and civil protection)
Objectives:
reliability and coherency of inputs (specially landslide inventory)
performing advanced modeling (many different methods)
evaluating models in the best fashion
providing maps/models as final outputs to be used in
practical/scientific manner
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
3. Introduction
Landslides – mass movements of the ground
Landslide susceptibility – spatial probability
of landslide occurrence (relation to hazard, risk…)
Setting definition:
Classification after Varnes 1978 (defining the mechanism and typology)
Scale/resolution (mid-scale, after Fell et all 2008)
Raster format data structure, pixel resolution 10 m
Definition of geometry (size, depth, area, frequency of landslides)
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
4. Introduction
Problems & perspectives in landslide assessment
lack of data, lack of possibility to relate events with triggers, non-
linearity of the problem…
piling investigations, promising capacities for monitoring (ground
sensors and Remote Sensing) in the future
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
5. Methodology
Methods for data pre-processing and selection: q n (ϕ oi , j − ϕ ei , j )2
Χ =∑ ∑
Chi-square
2
i=1 j =1 ϕ ei , j
Entropy
k ni n
E ( Sin ) = −∑
N
log 2 i
N
i =1
Landslide modeling methods ADVANCED!
Deterministic, Heuristic, Statistical, Fuzzy, Machine Learning
Methods for data evaluation
ROC plot
Kappa-index
n n n
κ =( ∑
i =1
xii − ∑
i =1
yii ) /(1 − ∑y )
i =1
ii
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
6. Methodology
Machine learning - Support Vector Machines
(SVM)
Classification task
Optimization (only two parameters)
Training over sampling splits
Testing the rest of the dataset with trained classifier
Kernels
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
7. Methodology
support vectors
landslide
e.g. aspect
e.g. aspect
stable
e.g. slope e.g. slope
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
8. Methodology
Experiment design
SAGA
SAGA
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
9. Methodology
Experiment design
Testing
Cross-Validation
Training
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
10. Case Study Dataset
Study Area
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
11. Case Study Dataset
Landslide Inventory
CGS survey (1:10 000)
http://mapy.geology.cz/svahove_nestability/
Field investigation
Independent field survey
Continuation from previous studies at the department
(Křivka, Marek, Bíl)
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
12. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
13. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
14. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
15. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
16. Case Study Dataset
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
17. Case Study Dataset
Thematic attributes # attribute source
Morphometric attributes 1 DEM Topo-maps
2 Slope DEM
Hydrological attributes 3 Slope length DEM
Environmental attributes 4 Aspect DEM
Geological attributes 5 Plan/profile curvature DEM
6 Convergence index DEM
7 Drainage elevations DEM
8 Elevation above drainage DEM
9 Drainage buffer DEM
10 LS factor DEM
11 TWI DEM
12 Catchment area DEM
13 Land cover units Orthophoto
nominal
14 Lithological units Geo-maps
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
18. Case Study Dataset
Attribute layers
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
19. Case Study Results
Model accuracy
=== Summary ===
Correctly Classified Instances 304080 = 88.16 %
Incorrectly Classified Instances 40814 = 11.83 %
Kappa statistic 0.1025
Mean absolute error 0.1183
Root mean squared error 0.344
Relative absolute error 75.3045 %
Root relative squared error 136.5789 %
Coverage of cases (0.95 level) 88.1662 %
Mean rel. region size (0.95 level) 50 %
Total Number of Instances 344894
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC Area Class
0.932 0.823 0.941 0.932 0.936 0.103 0.555 0.94 0
0.177 0.068 0.156 0.177 0.166 0.103 0.555 0.082 1
Avg.0.882 0.773 0.889 0.882 0.885 0.103 0.555 0.883
=== Confusion Matrix ===
a b <-- classified as: a=non-landslide
300020 21980 | a=0 b=landslide
18834 4060 | b=1
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
20. Case Study Results
Comparison with an earlier, non-predictive model based
on multivariate regression
First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
21. Conclusions
Overall:
model seems promising, but there is room for improvements
the study is in its beginning and it might be interesting to extend
it methodologically and to compare the results
Drawbacks
bad communication between GIS and Machine Learning platform
time consumption
For further notice:
it is necessary to increase the number of folds in optimization
it would be interesting to challenge the algorithm with multi-
class (multinomial) scenario
post-procesing might be good refinement for the overall
accuracy First InDOG Doctoral Conference, 29th October - 1st November 2012, Olomouc
22. Advanced Landslide Assessment of the
Thank You For Your Attention!
Halenkovice Experimental Site
Miloš Marjanović
milos.marjanovic01@upol.cz
This presentation is co-financed by the
European Social Fund and the state
budget of the Czech Republic