Landslide Susceptibility, Hazard, and Risk Assessment Twin Hosea W. K. Advisor: Prof. C.T. Lee Date: 2018/05/24 1
OUTLINE INTRODUCTION LANDSLIDE HAZARD ASSESSTMENT LOGISTIC REGRESSION IN LSA STUDY CASE FUTURE WORKS 2
INTRODUCTION Landslide is one of the natural disasters causing significant damages to lives and properties. Dilley, 2005 3
INTRODUCTION AFFECTING FACTORS Geology Geotechnics LANDSLIDES Topography Suzen and Kaya, 2011 Environment 4
INTRODUCTION LANDSLIDE AFFECTING FACTORS Suzen and Kaya, 2011 5
INTRODUCTION CAUSING FACTORS Geometric Changes Loading or Unloading LANDSLIDES Water Regime Changes Brunsden, 1979 in Budimir et al., 2015 Shock and Vibration 6
INTRODUCTION TRIGGERING EVENTS Earthquake Flooding LANDSLIDES Strom/Typhoon Marano et al., 2010 7
LANDSLIDE HAZARD ASSESSMENT Past and current landslide occurrence. Landslide Inventory Map Produced by: Comprehensive paper datasheets. Detailed geomorphological maps. Processing and analysis of remotely sensed digital imagery and DEM. Analysis in different time periods Gringnon et al., 2004, Hervas and Bobrowski, 2009 Multitemporal landslide inventories 8
LANDSLIDE HAZARD ASSESSMENT Landslide Inventory Map The main purpose: to portray the location (spatial distribution). to show the effects of major landslide triggering events. to determine the frequency-area statistics of landslide areas. To provide reliable information and validation to produce landslide susceptibility and hazard models. Galli et al., 2008, Hervas and Bobrowski, 2009 WebGIS user interface of the IFFI Landslide Inventory of Italy. (http://www.progettoiffi.isprambiente.it/cartanetiffi) 9
LANDSLIDE HAZARD ASSESSMENT Landslide Event Based Inventory Map Distribution of landslides triggered by a Typhoon Herb in 1996 and b the Chi-Chi earthquake in 1999, (Chang et al, 2007) 10
LANDSLIDE HAZARD ASSESSMENT Landslide Susceptibility Map Landslide susceptibility refers to the PROPENSITY of an area to landslide occurrence. The probability of occurrence of landslides of a particular type in a given location. Simply classifies a region into several classes with different potential of landsliding. Landslide Susceptibility Map Landslide Inventory + Conditioning Factors Hervas and Bobrowski, 2009 Pradhan and Abdulwahid, 2017 11
LANDSLIDE HAZARD ASSESSMENT Landslide Susceptibility Map Produced by: a. Heuristic Approach (Qualitative) Direct and Weighting Approach. Hervas and Bobrowski, 2009 12
LANDSLIDE HAZARD ASSESSMENT Landslide Susceptibility Map Produced by: b. Quantitative Approach of Landslide Inventories Based on Landslide Density (Ratio between affected area with total area) Landslides #/Km 2 Hervas and Bobrowski, 2009 Landslide density map of Masprem, Slovenia (Auflic et al, 2016) 13
LANDSLIDE HAZARD ASSESSMENT Landslide Susceptibility Map Produced by: c. Statistical (Probabilistic) Approach Model quantitatively by correlating the causative factors and the spatial distribution of landslides (inventory map) Bivariate and Multivariate Models Hervas and Bobrowski, 2009 Landslide susceptibility map of Ringlet Area, Malaysia produced by LR (Pradhan and Abdulwahid, 2017) 14
LANDSLIDE HAZARD ASSESSMENT Landslide Susceptibility Map Produced by: d. Physically-Based Models rely on physical laws influencing slope instability and are mainly based on slope stability analysis. also called geotechnical models. The susceptibility is expressed by the safety factor. FoS maps of Site B-2 of Calabria, Italy (Formetta, 2014) Guzzetti, 2005 in Hervas and Bobrowski, 2009 15
LANDSLIDE HAZARD ASSESSMENT Landslide Hazard Map Landslide hazard refers to the probability of occurrence of landslides of a particular type and magnitude in a given location within a reference period of time. It considers the magnitude of the event and it also considers the frequency (temporal occurrence or recurrence) of landslides In the hazard analysis, two factors were considered: landslide susceptibility map and landslide triggering factors. Landslide Hazard Map Landslide Susceptibility + Hervas and Bobrowski, 2009 Triggering Factors Pradhan and Abdulwahid, 2017 16
LANDSLIDE HAZARD ASSESSMENT Landslide Risk Map Landslide risk refers to the expected damage or losses caused by landslides, They are including: casualties, damage to property and infrastructure, and interruption of services and economic activities. Landslide Hazard Map Landslide Risk Map + Van Westen et al, 2006 in Landslide Vulnerability Hervas and Bobrowski, 2009 17
LANDSLIDE HAZARD ASSESSMENT Steps in Natural Hazard Assessment Inventory Construction Susceptibility Analysis Hazard Analysis Risk Analysis Construction of time series inventory of hazard Dividing a region into successive classes representing different grades of slope instability Calculation of the probability of a hazard level in a given region and a given time period Calculation of the lost of life or property in a given region and a given time period. Prof. 18CT. Lee
LOGISTIC REGRESSION IN LSA Logistic Regression (LR) Model LR is a multivariate statistical model that employs the maximum likelihood estimation method. The goal of logistic regression in landslide susceptibility mapping is to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of independent parameters, such as lithology, slope angle, etc. Logistic regression generates the model statistics and coefficients of a formula useful to predict a logistic transformation of the probability that the dependent variable is 1 (probability of occurrence of a landslide event). Hervas and Bobrowski, 2009 Pradhan and Abdulwahid, 2017 19
LOGISTIC REGRESSION IN LSA Logistic Regression (LR) Model While, z denotes the lean combination, which can be calculated using Eq. as follows: where P denotes the landslide probability, ranging from 0 to 1 in an S-shaped curve Where b 0 is the intercept, b i (i = 0,1,2,, n) represents the LR coefficients, x i (i = 0, 1, 2,, n) represents the conditioning layers Pradhan and Abdulwahid, 2017 20
Landslide Risk Assessment Using Multi-Hazard Scenario Produced by Logistic Regression and LiDAR-Based DEM Biswajeet Pradhan and M. Abdulwahid (2017). In B. Pradhan (Ed), Laser Scanning Application in Landslide Assessment (pp. 253-275) Twin Hosea W. K. Advisor: Prof. C.T. Lee Date: 2018/05/11 1
OUTLINE INTRODUCTION STUDY AREA METHODS RESULTS and DISCUSSION CONCLUSION 22
INTRODUCTION BACKGROUND: 1. Landslide is one of the natural disasters causing significant damages to lives and properties 23
INTRODUCTION BACKGROUND: 2. Landslide management programs, such as risk map, is needed to be conducted in land use planning. 24
INTRODUCTION BACKGROUND: 3. To develop risk map, it will need high quality of imagery data and dependable model. 25
INTRODUCTION OBJECTIVES: -- To develop the landslide risk maps for the Ringlet area located in Cameron Highlands, Malaysia, using logistic regression and multi-hazard scenarios constructed by analyzing 15-year rainfall data -- 26
STUDY AREA The study area is the Ringlet area, a township in Cameron Highlands, Malaysia. The average altitude of the area is 1,200 m above mean sea level, and the total land area is 24.38 km 2. 66% of the slopes having gradients of more than 20 27
STUDY AREA Post-Triassic Mesozoic granite comprises most of the rocks. However, there are few patches of metamorphic rocks, mostly comprised of Silurian Ordovician Schist, phyllite, limestone, and sandstone. The average annual rainfall is 2660 mm. Approximately 80% of the area is forested. 28
Landslide Inventory Map Selection of Effective Causative Factors METHODS Landslide Susceptibility Map Triggering Factor Map Land-Use Land-Cover (LULC) Map Landslide Vulnerability Map Landslide Hazard Map Landslide Risk Map 29
METHODS 1. Landslide Inventory Map It is the most important step. Remote sensing methods were used to obtain historical records of the landslides over the past 11 years. Derived from 1:10,000 1: 50,000 aerial photographs and SPOT 5 panchromatic satellite images. A total 164 landslides were identified. 30
METHODS 2. Landslide Conditioning Factors 1. altitude, 2. slope, 3. aspect, 4. curvature, 5. stream power index (SPI), The conditioning factor data set included: 6. topographic wetness index (TWI), 7. Terrain roughness index (TRI), 8. sediment transport index 9. distance from a river, These factors are chosen on the basis of the literature and expert knowledge. 10. distance from roads, 11. distance from lineament, and 12.Geology 31
METHODS A. Altitude The LiDAR data were used in constructing the altitude. The absolute accuracy of the LiDAR data should meet the rootmean-square errors of 0.15 m in the vertical axis and 0.3 m in the horizontal axis. 32
METHODS B. Slope The slope directly affects landslide occurrence and is typically considered in landslide susceptibility analysis. The slope in the study area ranges from 0 to 87.18. This layer was also used in the analysis as a continuous layer, where each cell represents the calculated slope 33
METHODS C. Slope Aspect Aspect influences weathering and, by implication, the sheer force of the object. The aspect map was employed to draw the relationship between landslide occurrence and this element. Ten classes were produced for the aspect. 34
METHODS D. Curvature The effect of curvature on slope failure reflects the convergence or divergence of water during downhill movement. The curvature was derived from a DEM and subsequently categorized into three classes: concave, convex, and flat. 35
METHODS E. Stream Power Index (SPI) SPI demonstrates the power of water flow to create erosion based on the assumption that discharge is related to a particular catchment area. SPI = (A tanβ)/b Which: A = Flow accumulation (m 2 ) b = the cell width which water flows (m) β = the slope (radian) 36
METHODS F. Topographic Wetness Index (TWI) TWI is the amount of water accumulation at a site. It has been used extensively to describe the effect of topography on the location and size of saturated source areas of runoff generation. TWI= log e (A/btan β) A = Flow accumulation (m 2 ) b = the cell width which water flows (m) β = the slope (radian) 37
METHODS G. Terrain Roughness Index (TRI) TRI is the mean difference between a highest (max) and minimum (min) values of the cells in the nine rectangular neighborhoods of altitude. It reflects the topographic heterogeneity. TWI= max 2 min 2 38
METHODS H. Sediment Transport Index (STI) STI defines the procedure of the slope failure and deposition. The index combines upstream area (As) and slope (β). under the assumption that contributing area is directly related to discharge, STI = ( As 22.13 )0.6 ( sin β 0.0896 )1.3 39
METHODS I. The Distance from River For the distance from the river factor, only the undercutting of the side slopes of rivers might cause slope failure initiation. 40
METHODS J. The Distance from Road The distance from road is considered as an important factor because constructing roads in hilly areas weakens the stability of the slope structure and therefore increases the area s susceptibility to landslides. The distance from roads was calculated using the Euclidean distance in the spatial analyst tool. 41
METHODS K. Distance from Lineament Lineaments are tectonic breaks that usually decrease rock strength. They are responsible for triggering a large number of landslides in the study area. Lineaments were acquired from the topographic map and DEM of the territory. 42
METHODS L. Geology Geology influences the shear strength of rock. Geological data were generated by digitizing geological boundaries, fieldwork, and interpretation of aerial photos rasterized and resampled to a 2 m grid. In the present study, two geological types are given: acid intrusive and schist. 43
METHODS 3. Landslide Susceptibility Assessment A. Logistic Regression (LR) Model A landslide susceptibility mapping is a process of predicting future landslides from the use of previous landslide records with consideration of certain conditioning factors LR model is used to produce the landslide susceptibility map. The LR was used to determine the landslide probability in the Ringlet area. The landslide susceptibility index was calculated using the following Equations of LR multivariate statistical modeling method: 44
METHODS 3. Landslide Susceptibility Assessment A. Logistic Regression (LR) Model While, z denotes the lean combination, which can be calculated using Eq. as follows: where P denotes the landslide probability, ranging from 0 to 1 in an S-shaped curve Where b 0 is the intercept, b i (i = 0,1,2,, n) represents the LR coefficients, x i (i = 0, 1, 2,, n) represents the conditioning layers 45
METHODS 3. Landslide Susceptibility Assessment B. Validation of Landslide Susceptibility Map Landslide susceptibility models validated by ROC curves and by calculating the area under the curve (AUC) using the training and testing landslide inventory data. To create the ROC curves, the produced landslide susceptibility map was compared with the landslide inventory data. The AUC validation method defines the prediction and success rates. 46
METHODS 4. Landslide Hazard Assessment Landslide hazard refers to the temporal probability of occurrence of a landslide event with a given intensity. In the hazard analysis, two factors were considered: landslide susceptibility map and landslide rainfall triggering factors. The final hazard maps were produced in GIS after calculating the hazard using the following expression (Xu et al, 2014): H= P s x P T Average rainfall intensity for 15-year return period 47
The vulnerability value assessment for each type of LULC METHODS 5. Landslide Vulnerability Assessment The vulnerability is defined as the level of loss to a given component in the area affected by the landslide. The authors demonstrated the vulnerability levels from 0 (no misfortune) to 1 (absolute misfortune). The final hazard maps were produced in GIS after calculating the hazard using the following expression (Xu et al, 2014): 48
METHODS 6. Landslide Risk Assessment Landslide risk is the amount of the negative impact to well-being, property, or the environment. In the present study, risk analysis was conducted to calculate the expected amount of loss caused by landslides in the study area. where : R=Expected Risk H=Estimated Hazard V=Assessed Vulnerability R = H x V 49
METHODS 6. Landslide Risk Assessment In addition, the loss analysis has to be conducted for each combination of hazard maps and an element at risk map. Then, the annualized risk was calculated using the following equation: R = 1 P1 x S1 + 1 P2 1 P1 where : Pn (n: 1, 2, ) = The return period used Sn (n: 1, 2, ) = the Losses x S1 + S2 2 + 1 P3 1 P2 x S2 + S3 2 50
RESULTS and DISCUSSION 1. Landslide Susceptibility Map The landslide susceptibility index (LSI) was reclassified into five susceptibility classes according to the quantile classification method of ArcGIS 10.2 51
RESULTS and DISCUSSION 1. Landslide Susceptibility Map The AUC-based validation of this map showed that the success and prediction rates of the LR model were 86.22% and 84.87%, respectively. 52
RESULTS and DISCUSSION 2. Landslide Hazard Map Susceptibility Map + Triggering Factor (Average Rainfall in any day) Hazard Map 53
RESULTS and DISCUSSION 2. Landslide Hazard Map Susceptibility Map + Triggering Factor (Rainfall 15 year return period) Hazard Map 54
RESULTS and DISCUSSION 3. Landslide Vulnerability Map The vulnerability map was derived according to the LULC criteria because of the lack of information on landslide intensity. The LULC map of the study area was produced by a supervised classification of high-resolution SPOT image. 55
RESULTS and DISCUSSION 3. Landslide Vulnerability Map The vulnerability map was generated and divided into five classes. Most of the LULC types have a vulnerability of more than 0.5. 56
RESULTS and DISCUSSION 4. Landslide Risk Map LULC MAP X (Any Day) Hazard Maps with Respect to Vulnerability Index Risk Map (in any day) 57
RESULTS and DISCUSSION 4. Landslide Risk Map LULC MAP X Return-Period Hazard Maps with Respect to Vulnerability Index Risk Map (in a return period) 58
RESULTS and DISCUSSION 4. Annual Risk Assessment The area under risk is calculated and defined as annual risk. The annual risk is used in the cost benefit analysis, where the difference in annual risk before and after the implementation of risk reduction measures (benefit) is compared with the cost of implementation Risk curve for the study area 59
CONCLUSION 1. The results of this study showed that the average annual economic risk of landslides is 5,981,379.00 MYR in the study area. 2. The prediction accuracy of LR model (84.87%) can be considered as acceptable for landslide susceptibility assessment. 3. The validation methods of landslide risk assessment are at early stages, to improve the landslide risk modeling approaches, validation methods should be improved so that better comparison can be done with the available models 60
COMMENTS 1. Already consider the size and the type of the landslide (rotational shallow landslide). 2. The effective factors only be selected by expert judgment and literature, without any selection test. 3. The landslide distribution looked like following the road. 4. Multi-hazard scenario: (i) average rainfall, (ii) abnormal rainfall, (iii) Three return periods. 5. Risk Map does not consider the mitigation measure/effort yet. 61
FUTURE WORKS 1. Establish hazard map for regional area in Indonesia using 2017 s tropical cyclone as triggering factor. 2. The susceptibility map will be analysis using logistic regression and other methods. 62
COMMENTS 1. I have collected: Topographic map 1:25000, Geological Map 1:50000; Land use map 1:25:000. 2. Further research is needed on wider temporal and spatial scales and in other areas in order to improve the predictability of the model such that it can be used for rainfall-landslide warnings. 63
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FUTURE WORKS 1. I have collected: Topographic map 1:25000, Geological Map 1:50000; Land use map 1:25:000. 2. I also have gotten the rainfall data of the area when the cyclone occurred. 3. I am trying to get Landsat data of this location. 65
THANK YOU FOR YOUR ATTENTION 66