1. Introduction
Landslides are considered as one of the most destructive geohazards [
1] as they cause substantial economic, human, and environmental losses in worldwide. Nearly 9% of global natural disasters refer to landslides [
2]. The publication of landslide papers has experienced a remarkable increase from the 1990s to the present [
3]. Landslide susceptibility assessment can be tricky because it is very difficult to evaluate both the spatial and temporal distribution of past events for large areas mainly due to limitations and gaps of both historical records and geographic information [
4,
5]. Thus, a considerable amount of recent research has focused on landslide susceptibility assessment [
6,
7].
Landslide susceptibility (LS) is the propensity of soil or rock to produce various types of landslides [
6,
8]. LS is usually expressed through cartographic means. Such maps are useful for developing mitigation plans and selecting the most suitable locations for construction. A LS map presents the areas with the potential of landsliding in the future by combining some of the critical factors, which contributed to the occurrence of past landslides [
9].
Elevation, slope, aspect, lithology, land cover, precipitation and seismicity were selected as these factors in our study. Among all parameters for LS zonation, elevation, slope and aspect have been recognized as the most important conditioning factors [
10,
11,
12]. The elevation dataset is useful to classify the local relief and locate points of maximum and minimum heights within terrains. Generally, it is well justified through the literature [
13,
14], that slope gradients have a large impact on landsliding in Peloponnese. The aspect parameter is related to differential weathering, exposure to sunlight and drying winds, and soil moisture. Lithology also plays a key role in landslide activity since different lithologic units have different landslide susceptibility values [
15]. Moreover, slope stability is strongly influenced by land cover. Finally, during the last decades, both seismicity [
16,
17,
18] and precipitation factor [
19,
20,
21] have been used as conditioning factors in many LS zonation studies. Considering the geotectonic context in the study area, as well as the climate factor, seismicity and precipitation were included in the study.
Geographic Information Systems (GIS) is an efficient technology to integrate and analyze a large amount of geographical data. During the last decades, many GIS-based LS assessment methods have been developed. General overviews of landslide susceptibility analyses are presented in [
22,
23,
24,
25]. LS modeling is divided into qualitative and quantitative methods. The most important difference between these methods is their degree of objectivity.
The qualitative methods depend on the knowledge and previous experience of the experts, and include the geomorphologic analysis [
26] and the use of index or parameter maps [
27,
28]. The quantitative methods depend on numerical expressions of the relationships between conditioning factors and landslide occurrence. They include geotechnical engineering approaches [
29,
30], statistical analysis [
31,
32,
33], as well as new interesting approaches of LS assessment such as artificial neural network (ANN) and neuro-fuzzy logic methods [
34,
35].
Some qualitative approaches however incorporate the idea of ranking and weighting the parameters involved, and may turn to be semi-quantitative in nature [
34,
35,
36,
37]. The use of quantitative methods should not be seen as an easier option than the quantitative methods. Qualitative methods are of value where the available resources or data dictate that more formalized quantitative assessment would be inappropriate or impractical [
38]. Since, the current working scale is a regional, (1:500,000), it was considered by the authors to investigate the application of the expert based fuzzy weighting. It is also worth emphasizing the quality of a landslide risk assessment, is related to the extend the hazards are recognized, understood, and explained, which is not necessarily, related to the extent to which they are quantified [
39]. Such an approach, known as trapezoidal fuzzy number weighting (TFNW), was applied in this study. Wang
et al. [
40] applied a model to produce a LS zonation map using GIS in the Guizhou area (176,167 km
2). The weight of each factor (and subclass relatively) caused landslide was achieved by the TFNW approach. Also, Wang
et al. [
37] presented a weighting method, integrating objective weight (based on entropy) with subjective weight (based on TFNW) to assess the LS under GIS environment. The distinction of this method is that a landslide inventory is not compulsory, because the weightings are assigned based on the field knowledge of an experienced geomorphologist [
41].
The main aim of this paper was to produce a regional landslide susceptibility map at regional scale using a semi-quantitative analysis approach. The performance of this model was evaluated in Peloponnese peninsula, Greece. Furthermore, validation analysis was implemented to estimate the prediction ability of the applied model.
2. Study Area
The proposed method was evaluated in Peloponnese, which constitutes the largest peninsula of Greece and one of its nine geographical departments. It is located in the southern part of Greece (
Figure 1), and is connected with the mainland through the Isthmus of Corinth. The total area of Peloponnese is 21,439 km
2, and its population stands at 1,086,935 inhabitants (Hellenic Statistical Agency, 2001). Agricultural, forest and semi-natural areas cover the main part of Peloponnese, whereas urban is the dominant land cover in the coastal zone of the peninsula. The climate is typical Mediterranean with a hot and relatively dry summer between June and August, and a wet season during autumn, winter and spring [
42].
Figure 1.
The location of the study area (Peloponnese peninsula) and the landslide validation dataset.
Figure 1.
The location of the study area (Peloponnese peninsula) and the landslide validation dataset.
Peloponnese has a complex geomorphology, with mountainous inland, many coastal cliffs in the south, and basins, coastal beaches, lakes and inland basins in west and southeast coasts. The slopes vary from gentle to very steep, while the drainage network is well developed, and is highly controlled by fault tectonics. The study area belongs to an active zone with tectonism expressed through faults, thrust zones and folds. The main lithological formations in the study area are (a) carbonate rocks (44%): limestones, dolomites and marbles; and (b) Neogene sediments (22%): usually marls, sandstones and mudstones. In these formations the majority of landslides have occurred.
Peloponnese is a region highly damaged by the occurrence of severe natural disasters such as earthquakes, floods, landslides, and forest fires. Heavy rainfall and earthquakes have triggered several landslide events, mainly in northern and western areas of the peninsula [
13,
43,
44]. Many serious events are related to major fault tectonics and to unstable zones located in steep slopes. In addition, the human interventions for the construction of roads have played a key role in landslide activation.
Accordingly, the study area forms a complex physiographic region where all conditioning factors of landslides present a high spatial variability. The high spatial variability of the factors is related to the complexity of the local conditions. These conditions reflect the regional characters of landslide manifestation process. Moreover, this variability permitted the creation of distinguishable classes in order to implement experts’ assessment of LS for each class.
4. Results
The overall results of the EFW analysis are presented in
Table 2 and
Table 3. According to the experts, the most important conditioning factors are precipitation, slope and lithology with weight values 0.18, 0.18 and 0.17, respectively. Importance evaluation for each subclass is more or less linear for factors with continuous values (slope, precipitation, seismicity). With regards to these factors, the experts estimated that high values are related to high landslide susceptibility. As far as lithology, land cover and aspect (factors with nominal values) are concerned, “
flysch”, “
agricultural areas” and “
west facing” subclasses have the highest importance values (
i.e., weight values of 0.22, 0.41 and 0.27, respectively). On the contrary, “
limestones”, “
forest and semi-natural land” and “
Flat facing” subclasses were found to have the lowest importance values relating to LS mapping (
i.e., weight values of 0.06, 0.22 and 0.11 respectively).
The output LS map (
Figure 2) from the EFW model shows that 25% (5239 km
2) and 7% (1370 km
2) of the study area were classified as “
High” and “
Very High” susceptibility zones, respectively. The same map also shows that the northern, central and south-southwestern parts of the study area are susceptible at “
High” and “
Very High” scale. Finally, the overlay of the final LS map with the landslide validation dataset indicated that 16%, 38% and 38% (total: 92%) of the landslide events fall within “
Very High”, “
High” and “
Moderate” landslide susceptibility zones (in total 71% of the study area), respectively. It is notable that according to the used model only 7% and 1% of the landslide events fall in “
Low” and “
Very Low” susceptibility zones, respectively.
To estimate the sensitivity of the weighting for the aforementioned method sensitivity analysis was implemented by changing the weight of the three most important factors (slope, MAP and lithology) and examine the effect of this change on the output LS map [
61].
Thus, a series of evaluation runs were conducted. In these tests the weight of the most important factor was altered (±5%, 10% and 20%). At the same time, the weights of the other criteria were adjusted proportionally to satisfy the rule which requires all weights to sum to one. Accordingly, for each simulation a series of evaluation LS maps was generated and compared with the LS output map of the base run. Finally, a summary table (
Table 4,
Table 5 and
Table 6) was created to quantify the changes in the evaluation maps in comparison with the base map.
Table 4.
Changes in evaluation map (%) (Slope factor sensitivity analysis).
Table 4.
Changes in evaluation map (%) (Slope factor sensitivity analysis).
Weight Change % | Change in Classification |
---|
M to H | H to M | M to L | L to M | H to L/L to H |
---|
−20 | 8.78% | 6.55% | 6.03% | 6.08% | 0.14% |
−10 | 7.57% | 6.72% | 5.92% | 6.21% | 0.13% |
−5 | 7.48% | 6.52% | 5.98% | 6.17% | 0.13% |
+5 | 7.43% | 6.38% | 6.11% | 5.97% | 0.14% |
+10 | 7.40% | 6.48% | 6.07% | 6.05% | 0.14% |
+20 | 8.78% | 6.55% | 6.03% | 6.08% | 0.14% |
Table 5.
Changes in evaluation map (%) (mean annual precipitation (MAP) factor sensitivity analysis).
Table 5.
Changes in evaluation map (%) (mean annual precipitation (MAP) factor sensitivity analysis).
Weight Change% | Change in Classification |
---|
M to H | H to M | M to L | L to M | H to L/L to H |
---|
−20 | 7.47% | 6.71% | 6.19% | 6.33% | 0.14% |
−10 | 7.30% | 6.51% | 5.99% | 6.21% | 0.13% |
−5 | 7.47% | 6.39% | 6.00% | 6.21% | 0.13% |
+5 | 7.30% | 6.48% | 5.92% | 5.90% | 0.13% |
+10 | 7.61% | 6.38% | 5.84% | 5.98% | 0.13% |
+20 | 7.74% | 6.22% | 5.99% | 5.92% | 0.15% |
Table 6.
Changes in evaluation map (%) (Lithology factor sensitivity analysis).
Table 6.
Changes in evaluation map (%) (Lithology factor sensitivity analysis).
Weight Change % | Change in Classes of Landslide Susceptibility |
---|
M to H | H to M | M to L | L to M | H to L/L to H |
---|
−20 | 7.59% | 6.32% | 5.97% | 5.96% | 0.13% |
−10 | 7.50% | 6.30% | 5.84% | 5.94% | 0.13% |
−5 | 7.21% | 6.45% | 5.88% | 5.96% | 0.13% |
+5 | 7.48% | 6.42% | 6.17% | 5.83% | 0.14% |
+10 | 7.48% | 6.52% | 6.20% | 6.08% | 0.14% |
+20 | 7.44% | 6.76% | 6.19% | 6.13% | 0.13% |
The results of the sensitivity analysis show that there is very limited area (less than 0.2%) with very significant change (more than one susceptibility class) from each original rank on the base run. Moreover, slope is the most sensitive criterion which causes susceptibility class modification equal to 27.6% when its weight changes for 20%. The greatest change (8.78%) in this run was from the class Moderate to the class High. The greatest variation throughout sensitivity analysis occurred in the classes Moderate, High and Low. Classes Very High and Very Low are relatively stable.
A standard validation analysis was additionally performed, using the validation dataset in order to estimate the overall performance of the LS model in the study area. For the validation of the output from our analysis, the receiver operating characteristics (ROC) curve was drawn, and the area under curve (AUC) value was calculated for the proposed model. In practice, the AUC performs very well and is often used when a general measure of predictiveness is desired [
62]. ROC analysis is considered as a powerful method for the validation of landslide susceptibility models [
19,
63]. The AUC value ranges from 0.5 to 1.0. The ideal model yields an AUC value close to 1.0 (perfect fit), whereas a value close to 0.5 indicates an inaccurate model (random fit).
Figure 3 shows the ROC curve of EFW model for the validation dataset. The AUC value of 0.70 indicates a reasonable prediction ability of the model.
Figure 3.
Receiver operating characteristics (ROC) curve for the EFW model.
Figure 3.
Receiver operating characteristics (ROC) curve for the EFW model.
5. Discussion and Conclusions
This study applied an expert-based (EFW) method to prepare a landslide susceptibility map at regional scale (Peloponnese peninsula, Greece). To achieve this objective, seven conditioning factors (elevation, slope, aspect, seismicity, precipitation, lithology and land cover) were taken into consideration.
For the creation of the EFW susceptibility map all the factors were combined, after expert-based weighting. For this model the landslide inventory map is not needed. This kind of analysis is purely subjective. To some extent, opinions may change for every individual expert and thus may be subjected to cognitive limitations with uncertainty and subjectivity. However, methods depend on expert opinions are often useful for regional assessments [
22]. Most of the quantitative/semi-quantitative landslide susceptibility research follows similar strategy with the proposed one by inviting a limited number of experts (See among others [
24,
64]). The main issue is not to invite as many scientists as possible but to invite experts with detailed knowledge of the landslides in the area under investigation and to ensure an overall consensus of their evaluation about the importance of the factors involved.
With the implementation of ROC analysis we can assess the prediction accuracy of a model. In this study, the empiric ROC area for the EFW model was estimated to be 0.70 for the validation dataset (
Figure 3). Then, there is 70.0% agreement between prepared LS map and landslide locations of the validation dataset, which is a reasonable result, taking into consideration the scale of analysis. Recently, LS analyses in the international literature have used ROC analysis, not only to validate the landslide susceptibility mapping models, but also to compare their prediction capabilities. Many researchers—among others [
65,
66]—have implemented expert-based approaches for LS mapping at regional scale with fair to good results (AUC values: 0.65–0.81).
The implementation of the expert-based model in the study area revealed that there are different zones within Peloponnese, which seem to configure various landslide susceptibility clusters (
Figure 2). The high susceptibility values are mainly located in the northern, central and south-southwestern Peloponnese. According to the final LS map, the “
Very High” susceptibility zone covers a significant part of the study area (7% of the total area). Most of the landslide events of validation dataset occur in areas with elevation lower than 880 m, slope angle from 7° to 15°, north or west facing, high levels of annual precipitation (991–1170 mm) and high seismic acceleration (2.91–3.10 m/s
2).
The most important factors for the LS zonation in the study area are precipitation, slope and lithology. It seems that the incorporation of dynamic factors (precipitation, seismicity) in this regional analysis was more or less beneficial to the assessment of landslide susceptibility.
The main idea behind this research strategy is to investigate how this “subjective” method is effective in this scale. The findings of our analysis are more or less acceptable. Thus, in a future worl, we intent to combine this method with statistical modeling (based on landslide inventories) in a “hybrid approach”.
Some basic characteristics, limitations and assumptions of the method have to be pointed out. A limitation of the proposed method is that the LS assessment is dependent on the subjective judgment of the experts and can be sensitive to slight differences in the weights associated with factors [
64,
67,
68]. To deal with this problem the sensitivity analysis was performed. Secondly, as the analysis based on medium-scale datasets, the results are unsuitable for detailed site-oriented specific analysis. At large scales, more exhaustive datasets and detailed geotechnical information are required. The subclass division of the conditioning factors is considered as the most subjective aspect of slope instability zonation methods. However, using some consistency (e.g., the adoption of similar classification approach) in different study areas may help to reduce the effects of subjectivity. Additionally, the proposed approach does not insist on consensus but rather formalizes a synthetic outcome from experts’ judgments. Furthermore, it should be mentioned that, according to our analysis, the output LS map presents only the predicted spatial distribution of landsliding. It does not present the temporal probability of landsliding. Therefore, the result from this paper should be used in the first stage of preliminary susceptibility mapping. Despite these limitations, the used method can produce trustworthy landslide susceptibility maps at regional scale. This is very useful information for local authorities and decision makers in order to target their mitigation strategies.