Statistical landslide prediction map as a basis for a risk map · 2014-05-18 · Statistical...

Statistical landslide prediction map as a basis for a risk map

M. Komac Geological Survey of Slovenia, Ljubljana

Abstract

Issues connected with unwanted natural occurrences, such as landslides, floods or earthquakes, are a source of concern around the world and Slovenia is no exception. Today, we cannot envisage spatial modelling and prediction of various events without information technology. GIS is also used to analyse the landslide data and satellite images can serve as a support to the ground reconnaissance. Using the methods of univariate statistics, the influences of individual spatial factors in a form of different spatial data, on the different landslide types and on landslides generally were tested. Using multivariate statistical methods (Factor and Regression analysis), the interactions between factors and landslide distribution were tested, and the importance of individual factors on landslide occurrence was defined. Having combined all the spatial data available, several models were developed. Those that produced the best results were then used to determine and locate the potentially hazardous areas and to produce a landslide hazard map. The landslide hazard map enabled the assessment of the risk to the inhabitants and roads on the tested area. It was determined that on the tested area where roughly 135,000 people live, around 3% of the population live in the high risk areas, and 20–25% of the population live in areas that are considered to be landslide-prone. More than half of the roads lie in areas subject to ground mass movement and 5% of all roads lie in high-risk areas. Keywords: geology, landslide prediction, multivariate statistics, landslide risk map, Slovenia.

1 Introduction

As a result of the recent natural disasters in Europe, like floods in 2002, the need for a better understanding of natural phenomena has arisen. Independently of

© 2004 WIT Press, www.witpress.com, ISBN 1-85312-736-1Risk Analysis IV, C. A. Brebbia (Editor)

whether these events result from human actions or are the work of nature, their prevention or mitigation is an important factor when the preservation of the modern man’s environmental quality is at stake. Hence the need for better understanding of these phenomena, especially when their consequences can be to some measure controlled, like in the case of landslides. For this purpose, the statistical approach to analysing the influence factors and their contribution to landslide occurrence was chosen (Carrara [1], Carrara et al. [2]). For the study area, the central part of Slovenia, west of Ljubljana, was selected (Figure 1), covering approximately 35×35 km. A better understanding of the described relationships should enable a more precise and a more affordable identification of the landslide-prone areas. In order to determine the accuracy capability of the spatial prediction of landslides or landslide-prone areas, several linear prediction models were developed, based on the results of multivariate analysis.

Figure 1: The study area.

2 History

Landslides are a menace occurring worldwide, hence the numerous researches on the topic of the landslide occurrence. The research of landslides and landslide-prone areas has been focused on their prediction, risks and hazards related to them, their consequences and on the factors that govern their occurrence. Also satellite images were used to locate and monitor landslides. All of the researches have three common predispositions; (1) past and present phenomena are the key for the prediction of the same phenomena in the future, (2) landslides can be described with a combination of several factors i.e. the geology, slope, rainfall etc., and (3) climatic changes will not substantially influence the factors that govern the landslide occurrence. The last predisposition is necessary due to the lack of knowledge related to climatic changes and their influence on landslide occurrence. In the beginning numerical methods in the form of simple linear formulae and also some univariate analysis methods were used. Soon researchers included probability estimation of their predictions. GIS tools enabled more complex spatial analysis. The beginnings are as “astonishing” as the computer capability,


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nevertheless, the results are important. Methods of multivariate analysis (MA) i.e. the multiple regression, the discriminate analysis, or the logistic regression were used on the point samples (Corominas et al. [3], Othman et al. [4]) and on area samples (Carrara [1], Carrara et al. [5] and [2], DeGraff and Romesburg [6], Bernkopf et al. [7], Chung and Fabbri [8], Gorsevski et al. [9] and [10], Vestal [11]). The statistical landslide prediction approach was also used by numerous other authors, who used different methods, bivariate and multivariate analysis. These methods are described more detailed in Van Westen [12] and Resources Inventory Committee [13]. Gupta [14], McKean et al. [15], Carlson and Taylor [16], and Lillesand and Kiefer [17] have assessed the usefulness of the satellite data for the landslide prediction. Indirect relation between satellite data and landslide occurrence have been shown by Woldai [18], Martínez-Alegría et al. [19], Hafner and Komac [20], Komac and Ribičič [21], Barredo et al. [22], Chung and Shaw [23], Ricchetti [24], and Hafner [25]. When landslide occurrence or hazard prediction is “upgraded” to the level, where the analysis includes the man-used areas it becomes the risk analysis. One can find theoretical basis in Resources Inventory Committee [13]. Varnes [26] revised many pre-1984 researches on the topic and one can find a huge archive on the USGS homepage. Some of the later research was done by Ribičič and Šinigoj [27], Vukadin and Ribičič [28], Jibson et al. [29] and Okunishi [30]. Trends of the research move towards the use of multivariate analysis with the more and more accurate data. The MA methods were used in the research, presented in this paper. The results of the factor and regression analysis were used for the development of linear mathematical models that were evaluated for the error. The best models served for the landslide-prone areas prediction in form of the maps of landslide occurrence probability.

3 Data analysis and results

For this research the following data were used: landslide data (Geological Survey of Slovenia), InSAR DEM 25 meter resolution (SMA [31]), geology at scale 1:100.000 (Buser et al. [32], Buser [33], Buser [34], Grad and Ferjančič [35]), satellite images (SORS [36], Sovinformsputnik [37]), topographic map at scale 1:50.000 (SMA [38]), population density data (NOSP and SMA [39]), CORINE land-cover (Hočevar et al. [40]), and infrastructure data (SMA [41]). The satellite images, used for the vegetation data, were combined, using PCA (Principal Component Analysis) merging method. The multi-spectral part of the satellite data was obtained from the Landsat-5 TM images, and the high-resolution part was obtained from the Resurs-F2 MK-4 images. These data were later used in the MA. The research area was located on the right edge of the satellite image, hence the strange shape of the area.


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3.1 Univariate analysis

All variables included in the model development were first analysed separately to determine their influence on the landslide occurrence (point sample). For this purpose, Kolmogorov – Smirnov and χ2 test (chi square) were used. The results are presented in Table 1.

Table 1: Important factors in landslide occurrence.

The steepness and the curvature of the slopes, the natural logarithm (ln) of the distance to the geological boundaries (Dist_geol_bound), the ln of the distance to the rivers (Dist_waternet), lithology, and the type of vegetation (for the point data, the CORINE land-cover was used) proved to play an important role in landslide occurrence. “Fossil” stands for fossil landslides (68), “Dormant” for dormant landslides (412), “Creeping” for creeping (56), and “Slides” for slides (58). Confidence limits for means were set to 95%. Significant variables, where statistical significance is higher than 95%, are shown in bold.

3.2 Multivariate analysis

Prediction models were developed on the basis of the landslides sample (n = 594). Prior to the model development, based on statistical analysis, approximately ⅔ of the landslides from each type of landslides, were randomly selected. This part of the sample served as the learning set (or model developing set) and the rest were used for testing of the landslide prediction error of the models (test set). Altogether, 383 (64,48%) of landslides formed the learning set (L) and 211 (35,52%) of them consisted the test set (T).

Table 2: Landslide partitioning.

Sample\Landslide type Fossil Dormant Creeping Slides All landslides

Learning set (L) 31 149 21 10 211

Test set (T) 37 263 35 48 383

Complete sample (L+T) 68 412 56 58 594

Using multivariate statistical methods – factor analysis (FA) and multiple regression analysis (RA), the interactions between factors and landslide distribution were tested, and the importance of individual factors on the landslide

All landslides Fossil Dormant Creeping Slides Variable χ2 K-S χ2 K-S χ2 K-S χ2 K-S χ2 K-S Slope 0,0 0,01 0,0003 0,01 0,0 0,01 0,039 0,01 0,28 0,01 Elevation 0,0 0,01 0,107 0,01 0,0 0,01 0,124 0,01 0,2515 0,05 Aspect 0,001 0,2 0,088 n.s. 0,008 n.s. 0,886 n.s. 0,008 0,01 Curvature 0,0 0,01 0,18 0,01 0,0 0,01 0,38 0,05 0,011 0,01 Lithology 0,0 0,01 0,0 0,01 0,0 0,01 0,002 0,01 0,005 0,01 Dist_geol_bound 0,0 0,01 0,0164 0,01 0,0 0,01 0,002 0,05 0,327 0,05 Dist_struct 0,01 0,01 0,282 0,05 0,2569 0,05 0,869 n.s. 0,067 n.s. CORINE 0,0 0,01 0,0 0,01 0,0 0,01 0,0 0,01 0,01 0,01 Dist_waternet 0,0 0,01 0,0 0,01 0,0 0,01 0,001 0,01 0,0 0,01

n 594 68 412 56 58



occurrence was defined. For the purpose of MA, the area was subdivided into 78365 slope units, for which additional 24 statistical variables were calculated. 45 different models (9 for each landslide type and 9 for all landslides) were developed based on the results of the statistical analysis and all of them were tested for the landslide prediction error. Models were classified into 27 classes according to the standard deviation (SD) of their values. The areas with the highest values represented the locations where landslides are most likely to occur, and the areas with the smallest values the safest locations. The mean value of the model was defined as a crude boundary between landslide-prone and landslide “safe” areas (Table 3).

Table 3: Model classification.

Statist. descript. # Hazard Statist. descript. # Hazard < -1,75 SD 1-6 Very small > MV - 1 SD 15-18 Relatively high

-1,5 SD - -1,75 SD 7 Small 1 SD - 1,5 SD 19-20 Predominantly high -1 SD - -1,5 SD 8-9 Predominantly small 1,5 - 1,75 SD 21 High -1 SD - < MV 10-13 Relatively small > 1,75 SD 22-27 Very high Mean value. 14 Boundary

Table 4: Model prediction error and other relevant statistical data.

Landslide type Best models Analysis Model Type TCorrect (%) χ2 p χ2haz phaz

Fossil Mup1_03 FA F×multiple R2 96,77% 95,58 0,00% 68,42 0,00%

Dormant Mup2_02 FA F×commun 95,97% 577,40 0,00% 438,10 0,00%

Creeping Mup3_02 FA F×commun 90,48% 112,02 0,00% 93,04 0,00%

Slides Mup5_03 FA F×multiple R2 100,00% 37,81 6,31% 22,54 2,06%

All landslides Mup_02 FA F×commun 96,68% 666,78 0,00% 473,66 0,00%

Landslides, that occurred in the slope units with values smaller than mean value of the model represented the error. Additionally, χ2 test was made for all of the models to determine the difference between the model prediction and the random spatial distribution of landslides. The χ2 test was made for all hazard classes that occur in the model and, separately for only those classes, where hazard is relatively high or higher. Generally models developed with the regression analysis gave much poorer results than the models developed on the basis of the factor analysis results. Table 4 shows the synopsis of the model error assessment, where only the results of the best models for landslide types are shown. The column “Analysis” stands for the method that proved to be most successful for the given model, “Model Type” represents the input data type, “TCorrect (%)” presents the portion of the landslide test set that was classified correctly, “χ2” and “χ2

haz” stand for the chi-square results, for all classes and for hazardous classes respectively, “p” and “phaz” stand for the probability of error of the given model for all classes and for hazardous classes respectively. For fossil landslides and slides the best results gave models where significant variance portions were multiplied with the multiple R2 value. For other landslide types



and for all landslides the best results gave models where factor weights were multiplied with communality values. Figure 2 shows the distribution of landslide types according to hazardous classes of the models that gave the best prediction. The shift of the cumulative landslide curves (black line) to the right show the good prediction of models. The distributions of the areas are presented with columns and the landslide distributions are presented with the grey line.

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Figure 2: Distribution of landslides according to hazard classes for best models.

In comparison with other multivariate statistical method used, the factor analysis proved to be the more appropriate and reliable method for the landslide prediction. The results showed that the following factors play an important role in the landslide occurrence; slope inclination and terrain roughness (19% - 27%), lithological properties (10% - 23%) and land-cover type (8% - 12%). There are other factors also that co-influence the landslide occurrence, for instance: slope



variation within the slope unit, average curvature of slope unit, lithological diversity, average distance to water-net, and average distance to tectonic elements. In brackets the portions of the variance are presented, where their fluctuation depends upon the landslide type.

Table 5: Distribution of roads, minor roads and inhabitants according to risk classes.

Model Risk Roads (%) Minor roads (%) Inhabitants (%)

Mup1_03 Small 15,13% 8,83% 31,29%

Medium 79,95% 86,22% 65,87%

High 4,92% 4,95% 2,84%

Mup2_02 Small 15,09% 8,53% 31,46%

Medium 79,94% 86,32% 65,51%

High 4,97% 5,15% 3,03%

Mup3_02 Small 16,65% 9,07% 33,97%

Medium 78,14% 85,32% 62,91%

High 5,21% 5,61% 3,12%

Mup5_03 Small 19,48% 10,21% 37,28%

Medium 76,63% 85,72% 60,83%

High 3,89% 4,06% 1,89%

Mup_02 Small 15,53% 8,75% 31,91%

Medium 79,59% 86,14% 65,14%

High 4,88% 4,98% 2,95%

Average on landslide-safe areas 56,31% 44,74% 76,05%

Average on landslide-prone areas 43,69% 55,26% 23,95%

Average on high risk areas 4,77% 4,95% 2,77%

4 Risk assessment and discussion

Next obvious step was to assess the risk posed by landslides to the inhabitants, roads and minor roads in the study area. A cross tabulation method between the hazard maps and population/infrastructure maps was performed to determine the portions of population and infrastructure under threat. For the results presentation, hazard classes were simplified into three classes of risk; small, medium and high. Table 5 contains the results of risk assessment for the described objects. On average, there are 16,4% roads that lie within small risk areas, around 79% of roads lie on areas with medium risk and almost 5% of roads have been constructed on the highly risky areas. When comparing just landslide-safe and landslide-prone areas, the majority (56,3%) of roads lie on areas that are less exposed to landslides. The distribution of minor roads shifts a little towards the areas with medium risk (86%), while the portion of those that fall into the highly risky areas is almost the same as in the case of roads (5%). It is a proof that spatial planners planned well. They put less important roads on



less stable areas, where on highly risky areas the construction is difficult or impossible for all types of roads. This trend is also noticeable when comparing only landslide-safe and landslide-prone areas. Here, the majority (55,3%) lies in the landslide-prone areas. One third of population lives in small risk areas and almost two thirds on medium risk areas. The rest, almost 3% (around 4000 people), live in areas that are very risky areas if the landslide occurrence is considered. Comparing the portions of the population on landslide-safe and landslide prone areas, one can state that almost one quarter of inhabitants lives in the areas subjected to landslides.

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Figure 3: Landslide risk maps. Light grey colour stands for areas of low risk and dark grey for high risk. Populated areas are shown in white.



Figure 3 shows the landslide risk maps produced from the best five models; A – Mup1_03, B – Mup2_02, C – Mup3_02, D – Mup5_03, and E – Mup_03. Of course these results are far from satisfying according to the standard risk assessment point of view (consequences, casualties and damage estimation etc.), but are a good foundation to build on it. The probability of landslide occurrence in the future in the presented maps (fig. 3) is an absolute value and it only considers the study area. It represents higher or lower landslide occurrence probability according to other locations in the study area. One important factor that was not considered in this research is the time component, which is used for the time trend estimation and for predicting the future events based on this trend. This approach is only reliable to a certain degree, because it lacks the observations over longer periods of time (this was not possible in the presented study) to achieve good and reliable results.

5 Conclusions

We have revised the research on landslide prediction with different methods, from basic to complex statistical. The work was oriented into the evaluation of the statistical landslide prediction, which was the basis for the landslide risk map production. From the methods used, the factor analysis proved to be most useful method for the landslides-prone areas prediction. In fact, the regression analysis proved to be highly ineffective in landslide prediction. On the other hand, models developed with the FA, achieved very good prediction results (more than 90% success). These models were used to define the weights of individual factors that influence the landslide occurrence, and to produce the risk maps. Based on these maps the distribution of population and infrastructure according to different risk degrees was assessed. To improve risk assessment in the future, the values to objects, included into the assessment, should be defined. Also time component of events should be implemented into the prediction. The first demand has the confidentiality restrictions of the real-estate values, and the second the relatively short period of events observation (from geological point of view) to determine reliable trend(s) of landslide occurrence(s).

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