12 th International Symposium on Landslides June 2016, Napoli (Italy)

Transcription

1 12 th International Symposium on s June 2016, Napoli (Italy) use of thematic for landslide susceptibility assessment by means of statistical : case study of shallow landslides in fine grained soils Michele Calvello, Mariantonietta Ciurleo University of Salerno (Italy)

2 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello Introduction Multiple rainfall-triggered shallow landslides Mangawhero, New Zealand (2004) Catanzaro, Italy (2010) susceptibility (Fell et al. 2008) A quantitative or qualitative assessment of the, volume (or area), and spatial distribution of landslides which exist or potentially may occur in an area. Susceptibility zoning by statistical (data driven) methods Fell et al. (2008). Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Engineering Geology 102:

3 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello Where do we move from? Calvello et al. (2013) Procedure proposed to produce landslide distribution zoning Definition of scale-dependent terrain computational units () related to the spatial resolution of the map Territorial domains used to define, calibrate and/or validate a model for landslide terrain zoning units (TZUs) related to the desired informative resolution of the zoning TZUs Territorial domains used to produce a landslide map for zoning purposes Calvello M, Cascini L, Mastroianni S (2013) zoning over large areas from a sample by means of scale-dependent terrain units. Geomorphology 182:33-48

4 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello Where do we move from? Ciurleo et al. (2016) Methodology to zone the susceptibility to shallow landslides in fine grained soils Algorithm employed to optimize the size of the terrain zoning units (TZUs) Ciurleo M., Calvello M., Cascini L. (2016). Susceptibility zoning of shallow landslides in fine grained soils by statistical methods. Catena, 139:

5 performance indicators susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method: landslide susceptibility assessment by means of statistical Derive and choose a set of optimal independent from a set of thematic Input data From thematic to numerical to independent statistical i.e. correlation between the dependent and each independent W ij = log D ij D β i = TPR i FPR i = σ i = = log n j=1 F ij F tot N ij N tot Sensitivity i 1 Specificity i W ij W i 2 n 1 = TP i (TP i + FN i ) FP i (FP i + TN i ) W ij = W ij N ij N tot n landslide susceptibility

6 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method: landslide susceptibility assessment by means of statistical Derive and choose a set of optimal independent from a set of thematic Input data From thematic to numerical to independent statistical i.e. correlation between the dependent and each independent landslide susceptibility to relevant to optimal spatial of relevant ( of ROC curves) Mutivariate statistical i.e. correlation between the dependent and a series of relevant independent IS TCU = i W ik(i) S 10 = TPR i = 0.1 E 90 = FPR i = 0.9 = computational susceptibility index

7 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method: landslide susceptibility assessment by means of statistical Derive and choose a set of optimal independent from a set of thematic Input data From thematic to numerical to independent statistical i.e. correlation between the dependent and each independent landslide susceptibility to relevant to optimal spatial of relevant ( of ROC curves) Mutivariate statistical i.e. correlation between the dependent and a series of relevant independent landslide susceptibility

8 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The case study Test area (southern Italy) Multiple rainfall-triggered shallow landslides Two small catchments approximately 8 km 2 on the left bank of the Corace river Thickness from 3 to 15 m. Length from 10 to 100 m. Depth of the sliding surface from a few decimetres to 3 m. The biggest ones are prevalently localized in morphological hollows

9 information employed for the analysis DEM, river network and landslide Geo-structural map Weathered rock thickness map physical weathering 1.0 m Weathered rock Year landslide source areas 2. landslide propagation zones 429 phenomena classified as shallow earth slides or earth slides earth flows 1. holocene alluvial deposits 2. holocene colluvial 3. holocene landslide debris 4. Pliocene light blue-grey silty clays 5. pleistocene sandstone 6. plio-pleistocene sands and clays 7. Normal faults 8. strike-slip faults Slaking consists of the alternate process of rocks wetting and drying. It produces two types of disintegration (Ollier 1994): minor disintegration Pattern of cracks due to flaking, major disintegration due to the splitting of the sample into large pieces. Blight (1997) susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The case study

10 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study DEM and derived (5x5 m 2 ) defined employing statistic techniques Example: Slope curvature in 5 classes (dimension equal to 1, 2, 4, 8, 16 and 32 ) landslide susceptibility

11 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study DEM and derived (5x5 m 2 ) defined employing statistic techniques V1 to V6: quantile and natural breaks in 8 classes V1 V2 V3 V4 V5 landslide susceptibility V6

12 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study landslide susceptibility Values of statistical indicators used to select the relevant independent : V1, V2, V3 map V1 Elevation V2 Curvature V3 Slope V4 Aspect V5 Distance from river V6 Distance from fault Focal Quantile (Q) Natural breaks (NB) βi σi βi σi

13 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study landslide susceptibility Values of indicators used to select the best performing spatial of the three relevant V V V (# cells) _Q _NB βi_q σi_q σi_nb βi_nb β, σ β, σ β, σ

14 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study Values of indicators used to select the best performing of the relevant C1opt C1nat C2opt C2nat C3opt C3nat V1 opt nat opt nat opt nat V2 opt nat opt nat V3 opt nat S 10 50% 40% 50% 40% 60% 40% E 90 20% 30% 20% 20% 20% 20% nat = native opt = optimal landslide susceptibility

15 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study susceptibility s landslide susceptibility

16 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello The proposed method applied to the case study Computational map C3opt IS TCU = i W ik(i) landslide susceptibility

17 susceptibility assessment by means of statistical ISL 2016, Napoli (Italy). Prof. Michele Calvello Concluding remarks On the case study The results of the performed show that: i) the best criterion, of the two adopted herein, is natural breaks; ii) iii) β and σ values related to the of the ( 1) almost always assume values substantially higher than those obtained from the native ( =1); for each significant independent, the values of are not considerably influenced by the criterion nor by the value assumed by. On the proposed method The landslide susceptibility s obtained with and without processing (in the various s) showed significant differences in terms of success of the analysis, as testified by the changes in values of the two indicators S 10 and E 90. The approach can be also successfully used to get landslide susceptibility zoning (Ciurleo et al., 2016)