The exploration of the relation among landslide susceptibility, probability of failure and rainfall by using independent events - Lanyang catchment and neighboring area for a case study Date : 2018/10/18 Presenter : Yu-Cheng Tai Advisor : Chyi-Tyi Lee
Outline Introduction Motivation and objective Literature review Methodology Study area Future work
Introduction 1. The standard of the selection of the independent rainfall event : The event that the first rainfall threshold was reached after the interval of more than 6 months is an independent event. 2. Collect the DEM, satellite imagery, rainfall data to build the independent event based landslide susceptibility model. 3. Find the relation of the landslide susceptibility level from the event-based susceptibility map, probability of failure and rainfall by the information given from the above data.
Landslide probability(%) Motivation and objective 1. Select the independent rainfall events by setting the total rainfall and rainfall intensity thresholds which can trigger the landslides. Rainfall threshold Rainfall event Landslide probability curve Rainfall factor(mm) (Chiou, 2012) 2. Make use of multi-independent event to do landslide susceptibility analysis of each event ; in addition, the statistical regression is conducted by the results of the landslide susceptibility analysis including the landslide probability and rainfall.
Literature review Deterministic approach : Performing hazard analysis by an infinite slope model and a hydrological model with parameters precalibrated by a set of landslide data, and a return-period rainfall may be used as a final input for the hazard map. (Montgomery and Dietrich, 1994; Dietrich, 1994; Iverson, 2000; Claessens et al., 2007) Probabilistic approach : It used annual landslide rate at a slope unit derived from a multi-temporal landslide inventory and applied the Poisson model to find an annual exceedance probability of certain magnitude of landslide failure at each slope unit over a median-sized drainage basin. (Guzzetti et al., 1999; Guzzetti et al., 2005) Statistical approach : Conventional multivariate statistical analysis via discriminant analysis or a logistic regression for landslide susceptibility is a kind of statistical approach. Among the statistical methods, the event-based LSA introduced by Lee et al. (2008) is a recent progress which may be possible for further development and upgrade to LHA. (Lee et al., 2004; Lee et al., 2008; Chung, 2009; Tsai, 2012; Lee, 2014; Lee, 2015; Chien, 2015)
DEM Geology map Satellite imagery Rainfall data Methodology Causative factor process i.e. Slope gradient, Slope aspect, Roughness series, Curvature series, Topographic index... Causative factor process i.e. Lithology Select pre-images and post-images No Integrate and enhance process Digitizing landslide Select event Yes Select event over rainfall threshold No Triggering factor process Event-triggered landslide inventory Factor analysis, Important factor selection Event-independent susceptibility model Research flow Probability of failure regression Landslide probability model Validation No Yes End of work
Methodology Landslide inventory Before After Legend Study area New New Extended Extended Deeper Other Legend Study area New Extended Deeper Other Deeper Others
Methodology Selection of effective factors : 1. Causative factors : Lithology, Slope gradient, slope aspect, terrain roughness, slope roughness, Terrain curvature, Elevation, Slope height series, Topographic index, etc. 2. Triggering factors : Rainfall intensity, Total rainfall.
Methodology 3. Probability of failure : Probability of failure % = 4. Success-Rate curves : Triggered landslide area of susceptibility level Total area of susceptibility level 100% The percentage of correctly classified objects on y axis. The percentage of area classified as positive on x-axis. (Chung and Fabbri, 1999)
Methodology Selection of effective factors : a : slopehigh a Slope gradient = 100% b b : slopelength Relationship of slope gradient versus Frequency Frequency, % 3 2 1 0 D.=0.615 Landslide Non-landslide 0 30 60 90 120 150 Probability of Failure, % 2.5 2 1.5 1 0.5 Relationship of slope gradient versus probability of failure 0 0 30 60 90 120 150 Success Rate The area under success rate curve of slope gradient 1 0.8 0.6 0.4 0.2 0 AUC=0.673 0 0.2 0.4 0.6 0.8 1 Portion of Area
Methodology Susceptibility model : Logistic regression : p i ln 1 p i n k 1 x k ki Success rate curve for Susceptibility map model (Lee, 2014) Landslide set : p i Non-landslide set : p 0 i 1 p i : Probability : Constant : Coefficient x k k : Variable i : Each pixel k : Each factor Susceptibility map for the Haitang event landslides (Lee, 2014)
Methodology Probability of failure surface : y 1 = 26.815λ(1 e 3.758λ0.760 )(1 e 2.0175( x 1 100 )1.836 ) y 2 = 32.357λ(1 e 3.483λ0.768 )(1 e 1.6835( x 2 2000 )2.445 ) x 1 is the maximum rainfall intensity in millimeter x 2 is the total rainfall in millimeter y 1, y 2 is the probability of landslide failure λ is the basic susceptibility. y = ( 1 r y 1 2 + y 2 2 + r(y 1 y 2 )) 0.5 r is the correlation coefficient of between maximum rainfall intensity and the total rainfall Fig. An example of Probability of failure surface from Kaoping River basin in southern Taiwan (a) rainfall intensity, (b) total rainfall. (Lee, 2015) Fig. 100-year rainfall landslide hazard map for the Kaoping River basin in southern Taiwan. (Lee, 2015)
Study area Rainfall gauge Selected rainfall gauge stations 站號 (number) 站名 (name) C0U520 雙連埤 C0U640 羅東 C0U650 玉蘭 C0U680 冬山 C0U710 太平山 C0U730 思源 C1A630 下盆 C1U501 牛鬥 C1U511 古魯 C1U630 再連 C1U660 三星 C1U670 寒溪 C1U690 新寮 C1U700 土場 C1U720 南山
Probability of failure(%) Probability of failure(%) Study area Lanyang catchment : All average Rainfall event First linearization Second linearization Maximum hourly rainfall Original Best Linear(original) Linear(best) Maximum hourly rainfall Average total rainfall Tsai, 2016 research area The threshold of total rainfall is approximately 380mm Chiou, 2012 research area The threshold of maximum hourly rainfall is approximately 33mm
Study area Total rainfall values of rainfall gauges in every event Total(mm) Herb (1996) Xangsan e (2000) Nari (2001) Aere (2004) Haitang (2005) Sepat (2007) Sinlaku (2008) Parma (2009) Megi (2010) Saola (2012) Matmo (2014) Soudelor (2015) C0U520 雙連埤 427.0 401.5 815.0 408 241.0 323.5 558.5 511.0 345.0 517.5 214.0 153.0 C0U640 羅東 302.5 445.5 629.0 165 236.5 278.0 393.5 559.5 791.5 571.5 189.0 301.0 C0U650 玉蘭 350.0-938.0 461.5 255.5 276.5 601.0 1028.5 384.0 656.5 199.5 147.5 C0U680 冬山 412.0 373.0 416.5 115.5 319.5 480.5 381.5 614.0 938.5 604.5 235.0 351.5 C0U710 太平山 693.5 632.0 989.0 400 1154.5 656.0 1131.5 639.0-1880.5 562.5 1066.5 C0U730 思源 555.0 370.0 381.0 444 769.0 534.0 801.5 354.5 137.0 1042.0 422.5 374.5 C1A630 下盆 - 456.5-558 611.5 377.5 1084.5 491.0 278.5 746.0 192.0 531.5 C1U501 牛鬥 336.5-1212.0 488.5 262.0 401.5 586.5 1302.5 440.5 844.5 251.5 129.0 C1U511 古魯 570.5 710.0 1454. 5 253.5 750.5 918.0 716.5 1579.5 877.0 1583.0 548.5 713.0 C1U630 再連 283.0 395.5 653.0 311.5 182.5 139.0 528.5 464.5-376.5 92.0 78.0 C1U660 三星 229.0 444.5 760.0 237.5 262.0 352.5 574.5 1221.5 464.5 598.0 244.5 256.5 C1U670 寒溪 347.5 524.5 805.0 189.5 300.5 528.0 361.5 1312.5 704.5 726.5 351.0 359.5 C1U690 新寮 449.5 532.0 1.5 115.5 320.5 537.5 421.5 1149.0 892.0 703.0 400.5 299.0 C1U700 土場 430.5 426.0 1428.0 460 571.5 388.0 690.5 692.0 - - - - C1U720 南山 568.5 332.5 134 381 431.0 354.0 647.0 359.5 171.5 853.0 335.0 319.0
Study area Max 1hr rainfall values of rainfall gauges in every event Max 1hr(mm) Herb (1996) Xangsane (2000) Nari (2001) Aere (2004) Haitang (2005) Sepat (2007) Sinlaku (2008) Parma (2009) Megi (2010) Saola (2012) Matmo (2014) Soudelor (2015) C0U520 雙連埤 40 33 53.5 34 31 46.5 36 55 25.5 51.5 25.5 25.5 C0U640 羅東 23 48.5 113.5 15.5 21.5 43.5 39 74.5 69.5 59.5 28 37.5 C0U650 玉蘭 29.5-87.5 28.5 26 22.5 40 55.5 26.5 46.0 20.5 19 C0U680 冬山 36.5 48.5 65 24.5 60.5 57 42.5 115 80.5 56.5 42 38.5 C0U710 太平山 45.5 59 140.5 35 91 42.5 55.5 43-93 67.5 98.5 C0U730 思源 40.5 39 28 23.5 51 37 30.5 23.5 16 52.5 56 29.5 C1A630 下盆 - 45 45 44.5 50.5 51 47 69.5 24.5 65 18 52 C1U501 牛鬥 25.5-90 27 31.5 51 37 88 36.5 51 27.5 21.5 C1U511 古魯 59 57.5 134.5 22 52.5 49.5 47 109 110 101 54 70 C1U630 再連 29 47 66 27.5 23 15 52.5 77-46 18 16.5 C1U660 三星 15 45 94 20.5 31 27.5 57.5 99 41.5 54 34.5 38 C1U670 寒溪 22.5 45 104.5 24.5 39.5 44.5 31 89.5 61.5 54.5 32.5 40.5 C1U690 新寮 37 56 0.5 22 48 38.5 60.5 65.5 76 62.5 33.5 65 C1U700 土場 32.5 53.5 134.5 27.5 54.5 29.5 35.5 74 - - - - C1U720 南山 68 36 44.5 21 32.5 28.5 31.5 22 16 46 31.5 21.5
Study area Satellite imagery properties Independent Event Event time Satellite imagery time Satellite Name Xangsane typhoon_before Xangsane typhoon_after 2000/10/31~ 2000/11/01 2000/06/08 2000/08/10 2000/12/10 2001/01/18 SPOT1 SPOT2 SPOT1 2001/07/01 SPOT1 Nari typhoon_before 2001/03/15 SPOT2 2001/09/08~ 2001/09/19 Nari typhoon_after 2002/01/05 SPOT2 Aere typhoon_before Aere_typhoon_after 2004/08/23~ 2004/08/26 2004/06/14 SPOT5 2004/10/12 2005/01/24 SPOT5 Parma typhoon_before Parma typhoon_after 2009/10/03~ 2009/10/06 2009/05/08 2009/05/29 2009/12/13 2010/01/13 SPOT5 SPOT5
Future work 1. Collect the enough satellite imagery data and try to digitize each event-base landslide inventory. 2. Use the landslide inventory and some factors to do the landslide susceptibility analysis and build the model. 3. Validate the landslide susceptibility model with each event.
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