1. Introduction
Shallow landslides are one of the most common and frequent geo-disasters that occur in mountainous regions [
1]. In most areas of Korea, where approximately 63% of the territory consists of mountainous regions, soil layers are generally less than 2–3 m in thickness with underlying bedrock [
2]. In addition, the annual rainfall in the central region of Korea is approximately 1200–1500 mm, and more than half of the annual precipitation is concentrated during the months from July to September due to the influence of the Monsoon season. Due to these topographical and climate conditions, Korean mountains are regarded as regions that are susceptible to shallow landslides [
3,
4]. According to the statistics of the Korea Forest Service from 1976 to 2018, an average of 34 casualties and 395 ha of landslides occur annually. Considering such figures, there is a growing national interest in the development of proactive technologies for the prevention and mitigation of landslide hazards.
Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for disaster prevention and mitigation, as it provides information regarding landslide-prone areas. However, reliable spatial prediction of landslides remains a challenging task due to its complexity as it is affected by various internal factors (e.g., hydro-geotechnical properties, lithology, forestry, geological structure, topographic conditions) and external factors (e.g., rainfall, the melting of snow, earthquakes, volcanic eruptions) [
5,
6]. To resolve these problems, many studies have been conducted over several decades with the goal of developing high-performance-based landslide susceptibility models through various approaches, which can be divided into two categories: physically based methods and data-driven methods.
Physically based methods of landslide prediction [
7,
8] are generally expressed as safety factors of slope stability, which refers to the ratio of soil shear strength to the shear stress of potential sliding surfaces in the slope. Such methods do not require a historical inventory of landslides when developing susceptibility maps but require detailed geotechnical properties and geometric conditions. As such, physically based models are more practical for site-specific areas with homogeneous conditions [
9], as it is expensive and time-consuming to build up a database for applications in large-scale areas [
10,
11].
Data-driven methods of landslide prediction [
10,
11,
12] estimate potential landslides by analyzing and interpreting the relationship between historical landslide data and various predisposing factors through the means of statistical or machine learning techniques without physical processes. Therefore, historical landslide data and various factors related to landslide occurrence should be collected as the first step for the landslide susceptibility mapping [
13]. Recent advances in data mining and soft computing have made it possible to easily link with Geographic Information System (GIS) platforms, enabling landslide susceptibility assessment over wide areas [
14].
According to the literature review, artificial neural network (ANN) models have been reported as a suitable machine learning method for predicting non-linear and complex phenomena [
15,
16]. Such models have been widely applied for landslide susceptibility modeling [
17,
18,
19,
20]. In a study of applying an ANN-based susceptibility model, Vasu et al. [
21] improved the predictive ability of the ANN by integrating a hybrid feature selection and an extreme learning machine. Tien Bui et al. [
22] compared two training algorithms (Levenberg–Marquardt and Bayesian regularization network) and found that the latter algorithm was more robust and efficient. Lee et al. [
23] showed that an ANN model performed better with the weights of each factor being determined compared to without determining the weighting. Ermini et al. [
24] compared two architectures of ANN models (Multi-Layer Perceptron and Probabilistic Neural Network) and obtained slightly better results with the former architecture. Despite these efforts, there is still a multitude of considerations that should be accounted for when developing an optimal ANN model capable of high levels of performance [
25,
26], such as factor selection, the number of neurons and layers, and activation functions.
In this study, landslide susceptibility maps of Mount Umyeon were produced using ANN models with consideration of various model architectures. The main objective of this study is to determine the optimal structure of the ANN model considering the factor selection method and various activation functions for high-performance-based landslide susceptibility mapping. In the factor selection stage, information gain ratio and multicollinearity analysis were applied for the evaluation of predictive power and mutual exclusivity of the landslide predisposing factors. Once evaluated, the optimal architecture of the ANN model was selected with consideration of the number of neurons and various activation functions by evaluating model performance using receiver operating characteristics (ROCs), Kappa index, and various statistical evaluation measures. Finally, a non-parametric test (Friedman test) was conducted to compare the developed susceptibility models to confirm significant differences.
5. Discussion
Landslide susceptibility mapping is an essential task in the determination of landslide-prone areas and is well recognized as an important step in the prevention and mitigation of landslide hazards. Many researchers have utilized ANN models to develop landslide susceptibility models [
15,
16,
17,
18,
19,
20,
21,
22]. Despite such attempts, there is still a multitude of considerations involved in determining the optimal structure of a high performance-based ANN model, such as landslide predisposing factor selection, the number of neurons in the hidden layer, and the activation function.
The first important step in developing a landslide susceptibility map involves building a reliable database of input–output pairs as it can control the performance of the susceptibility model. In this study, a landslide inventory was constructed in the form of a feature point with 5 m resolution at the center of the source area. The inventory is able to represent the overall morphological, hydrological, geological, and land cover characteristics of the study area as the landslides that occurred in the study area were shallow and translational or slightly rotational types. For other types of landslides, such as deep failures, it would be more suitable to construct an inventory in the form of feature polygons.
A total of 20 landslide predisposing factors (elevation, slope, aspect, curvature, TRI, SRR, SEI, soil density, forest type, forest density, and distance from road) were established through an abundant literature review of existing landslide susceptibility studies. The established predisposing factors were normalized to a comparable range of 0.01–0.99 for further data analysis and ANN modeling. This process can guarantee stable convergence of weight and biases in ANN modeling [
56]. Future studies are recommended to use geotechnical databases such as internal friction angle, cohesion, and permeability coefficient, although such databases may require significant amounts of money and time to build. As such factors directly influence slope stability, reliable research results may be obtained.
Factor selection for assessing landslide susceptibility is an essential task that influences the quality of ANN models, as not all factors affect landslide occurrence. In this study, information gain ratio (IGR), Pearson correlation, VIF, and tolerance analyses were subsequently performed to check the predictive power of each predisposing factor and conduct multicollinearity diagnosis. Although there is no universal agreement regarding factor selection methods, a high-performance ANN model was successfully developed through the method applied in this study.
In the IGR analysis phase, slope showed the highest value of average merit among the predisposing factors, which is judged to be due to its significant contribution to the factor of safety. In contrast, six factors (forest density, soil texture, forest type, soil density, weathering, distance from stream, and distance from road) were determined to possess no predictive ability and were excluded from this study. Nonetheless, the six excluded factors should be further studied in other regions, as these factors may possess predictive power if additional databases are accumulated from different regions. In the multicollinearity diagnosis phase, slope and elevation showed high correlation with TRI as well as STI and SPI. Although high correlation does not necessarily indicate multicollinearity, the calculation formulas of TRI, STI, and SPI indicate high correlation between each variable. Thus, data regarding TRI and SPI were eliminated at low-predictive ability orders. Finally, VIF and tolerance analyses determined that there was no multicollinearity between the 11 selected factors.
The 11 selected predisposing factors were randomly split into a 70:30 ratio for training and validation. Although there are no specific guidelines for dividing datasets, this process may prevent the overfitting or underfitting problem and enable reliable model verifications compared to models that do not divide datasets. The importance of dividing datasets for training and validation was also mentioned and discussed by Chung and Fabbri [
57], Tien Bui et al. [
22], and several other researchers.
In this study, six models, each with a different non-linear activation function in the hidden layer, were evaluated and validated using the Kappa index, AUC, and five statistical measures. As a result, the best performing MLP model was the model that used the hyperbolic tangent sigmoid (Tan-sig) function with five neurons in the hidden layer (
Figure 7,
Table 4 and
Table 5). The models developed with the six activation functions were identified as comparable models by the non-parametric Friedman test, which showed the models as having significant differences with each other (
Table 6). Finally, the Tan-sig model showed that 89.4% of all historical landslides ranged from the Very High to Moderate classes and produced a landslide density result of 4.38 for the Very High susceptible class, which is the highest value among the six models (
Table 7 and
Table 8). In other words, a Tan-sig function in the hidden layer best represents the complex and non-linear relationship between the predisposing factors and landslide occurrence in the study area.
The susceptibility model developed in this study is based on a single-event inventory with one extreme rainfall pattern. Slope failures are caused by the weakening of soil unsaturated shear strength as the soil becomes saturated due to rainfall infiltration. The destabilizing force exerted on the soil layer is related to the layer thickness and geotechnical properties as these factors affect normal stress and shear strength, respectively. Rainfall patterns and soil permeability dictate the rate of water infiltration into the soil; hence, both affect the saturation of the soil layer at a particular time and location. For example, if a soil has a shallow depth and a large permeability coefficient, it will be more sensitive to rainfall patterns with intensive rainfall over a short period. In contrast, if the soil layer is deep and the permeability is relatively small, rainfall patterns with low rainfall intensity over long periods of time will have greater effects on landslide occurrence. Therefore, in order to enhance the performance of the susceptibility model, a future follow-up study should be conducted using an updated multi-temporal landslide inventory generated with consideration of other rainfall conditions.
6. Conclusions
This study demonstrates the systematic procedure of determining the optimal structure of an ANN-based landslide susceptibility model for identifying landslide-prone areas in Mount Umyoen, Korea. The main objective of this study was to design the optimal structure of the proposed MLP model, taking into account the factor selection method and various non-linear activation functions. The seven main procedures to achieve this purpose were as follows: (1) collecting historical landslide data, (2) constructing landslide predisposing factors, (3) preparing training and validation datasets, (4) applying a factor selection to select suitable database subsets, (5) developing landslide susceptibility models, (6) validating and comparing landslide susceptibility models, and (7) selecting the best performing model.
The best model was the MLP model consisting of an 11 5 1 structure with the hyperbolic tangent sigmoid function in the hidden layer and the logistic sigmoid function in the output layer. The validation process confirmed that the best model (11 5 for the tan-sig function 1 for the log-sig function) had a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, positive predictive value of 79.17%, negative predictive value of 81.82%, and an AUC value of 0.879. In addition, the Kappa index was 0.609, indicating substantial agreement between the observed and predicted values. As a final conclusion, the results of this study may be useful for preemptive response in landslide-risk areas.