Landslides result in enormous casualties and huge economic losses in mountainous regions. The recent intensification of land-use changes has raised the level of landslide susceptibility. China is a mountainous country, correspondingly, landslide disasters occur frequently. There is no doubt that this will be a serious threat to the economic and society development, especially in mountainous areas. According to the China Ministry of Land and Natural Resources, the landslide hazards resulted in almost 1000 deaths, hundreds of millions of dollars in direct economic loss, and inestimable indirect loss every year [1].There were 19,522 geological hazards from January to June in 2010 which leads to 464 people dead or missing and direct economic losses of around 18.6 billion Yuan (about 2.3 billion US dollars). Geological disasters have increased 10 times in the last few years. Massive landslide was induced by the 5·12 Wenchuan earthquake in China in 2008 [2]. The Zhouqu debris flows shocked the whole world on 8 August, 2010, which resulted in 1,144 dead and 627 missing [3]. Landslide susceptibility maps provides urban planners overview information of landslide prone areas [4]. Based on the thorough research on the formation mechanism of landslides, the spatial and temporal variation characteristics of shallow landslides can provide scientific basis for evaluating and managing hazards. In recent years, regional and medium-scale landslide susceptibility mapping have become an important topic for specialists of different disciplines, such as engineering geologists, planners, local administrations or decision makers, etc [5, 6].

Now researchers have made great progress in the landslide susceptibility assessment. Several different approaches can be found in current literature, including direct and indirect heuristic approaches, deterministic, probabilistic and statistical approaches, nonlinear system theory method, as well as coupling analysis method of endogenic and exogenic geological processes. Those methods may be summarized into statistical models and dynamical model [7]. Statistical method includes discriminant analysis [8], principal component analysis, multivariate analysis [9], and logistic regression [10] and so on. With the advanced GIS technology and the factor-overlap method, the multivariate analysis method was drawn into the landslide sensitivity assessment [11, 12]. However, there are some weaknesses in statistical models. Firstly, the result is too heavily dependent on the quality of the data collected; secondly, the statistical model cannot explain the mechanism and, thirdly, regional disparity also exists in the relationship between influence factor and landslide susceptibility, and the method cannot suit for different area. The physical mechanism model based on the limit equilibrium is widely used in geo-technical engineering [13]. The model requires physical mechanics parameters obtained by geotechnical experiment. However, the model parameters are difficult to obtain. The output of the mechanical models provides a basis for the stability analysis of a landslide. The landslide is the result of interaction of meteorological, hydrographic, geology, physiognomy and human environment. Therefore, a single-landslide pattern cannot represent a whole zone or achieve the goal of disaster prevention and mitigation.

To overcome these shortcomings, the ANN method was developed and became popular in the recent year. Many papers concerning landslide susceptibility mapping using this method have been published [14-18]. Artificial neural network method can reflect non-linear feature between the factors of landslide formation. The model’s precision of the model depends on the data samples. In fact, landslide is determined with a complicated variable nonlinear system by factors like rock deformation, nonlinearities of evolution law and internal factors. The landslide susceptibility prediction should be based on complex, uncertain, and non-linear relationships between the stability of slide rock and conditioning factors. ANNs are information-driven models and universal non-linear function approximations. The ability to learn non-linear functions from data is an important feature in classifying landslide-prone areas [19]. Moreover, a neural network does not require assumptions about the input variable distribution.

In this paper, the application of the ANN methods to landslide susceptibility mapping is studied. This paper introduced the details of the methods as well as the findings of the study. This paper is organized in four major parts. The first part describes the feature about landslide of the study area. The second part determines the statistical correlations between landslide frequency and the physical parameters contributing to the initiation of landslides. The third part describes the methods, development and training of ANNs algorithms. In the fourth part, landslide susceptibility mapping was modeled by incorporating factors in a GIS-based ANN model. The controlling factors were also identified by the model. The fifth part describes logistic regression model applied in the susceptibility mapping.

The ANN model was tested on the Meizhou city which is located at the south-east of china, with a total area 15876.05 km² and the elevation ranging from 0 to 1559 m (Fig. 1). There are widespread mountains in the study area with complex geological conditions and strong tectonic activity, which caused serious landslides disasters. Therefore, it is a serious hazard to the economic development in mountain area. Considering the hazard from landslides, it is urgent to identify area which may be prone to landslide.

The study area is typical of the geological features of surf-layer and bedrock in the south of China. The bedrock geology mainly comprises of Paleozoic and Mesozoic strata in the middle of study area (Fig. 2). Volcanic rocks compose mainly of granite in the northwest part and southeast part of study area, which occupied 40% of gross area. The climate is typical of subtropical and monsoonal, hot and humid in summer mild and dry in winter. Heavy rains and typhoons often bring huge rainfall. The average annual precipitation varies from 1067 to 1727 mm during the period of 1990–2005. The groundwater is composed of pore water, bedrock fissure water and carbonate rock karst-cranny water in the zone. The development of transient perched water table at the interface of the colluviums and residual soils, and the underlying weathered rock during or following periods of intense rainfall, result in the natural steep slopes prone to sliding.

Fig. (1). The Study Area.

Fig. (2). Input Data Layers (a) altitude; (b) slope; (c)aspect; (d)surface rolling; (e)rock types; (f)distance to faults.

Landslide locations were identified in the study areas by the interpretation of aerial photographs, field surveys and inventory reports. Altogether, 384 landslides were recorded in the landslide inventory and related spatial parameters, such as location, time of failure, and dimensions, available for each record. The scene of landslide disaster is shown in Fig. (3). Landslides type in the study area is mainly the shallow soil landslides. Landslide scale is a small shallow landslide, whereas the landslide thickness is generally 1-2 meters. The feature of the landslide district is highly regional and seasonal. Most of the landslides in the study area are located at the hilly ground area with an elevation of 100-500 meters, which is easy to deposit colluviums. As can be seen from layer structure, the hazards are mainly distributed in the territory of granite rock, which covered with weathered layer and soft metamorphosed rock. From the time of distribution, 97.35% of the landslides occurred during the flood season.

The use of the ANNs can be a valid alternative in hazard mapping, when the conditioning factors are not approximable by a normal distribution and are strongly correlated. Artificial neural network model is a non-linear model with input layer, hidden layer and output layer. The landslide influencing factors are taken as the input layer, the landslide susceptibility as the output layer. The training and learning of the model are carried out. After the model has been validated, the output from the forecast area can be used to assess the sensitivity of the disaster. The data-learned model has an evaluation function that can calculate the magnitude of the landslide susceptibility.

Landslides result from interdependent spatio-temporal processes, including hydrology rainfall, groundwater, vegetation, soil condition, bedrock, topography, and human activities and so on. These can be subdivided into four groups: geomorphological conditions, geological conditions, land use and landcover, and meteorological and hydrological conditions. The factors utilized in this study are variables of both categorical and continuous data. In this study, both quantitative and qualitative variables were subdivided into proper categories, defined on the basis of the influence that they exert on landslide mechanics, and normalized in the interval 0-1. In this paper, based on index factor status and factor correlation analysis results [20], eleven factors were selected. Detailed description of the factors and data sources in the study area are summarized in Table 1. These parameters were transformed into a spatial vector database, and the thematic layers were converted into a raster grid for application in the neural network modeling.

Fig. (3). The shallow landslides.

3.4. Land Use

Many studies have revealed the relationship between human activity and slope stability [23]. Rainfall and human activities are very important triggering factors of the landslide relating to geological condition. In this study, land use layer was extracted from Landsat ETM image by using object-based classification method. There are four different land use types in the region including cultivated, land garden plot, forest land and construction land. The landslide percentage in each land use type is presented in Table 2. As seen from the table, most of the landslides occur in the forest land and cultivated land.

Fig. (4). Input data layers (a) soil types; (b) land use type; (c) NDVI; (d) 24h heaviest rainfall; (e) river; (f) distance to drainage.

In the recent years, logistic regression analysis is one of the most popular multivariate statistical methods. In the recent literature, many studies have been published on the assessment of the landslides by using logistic regression analyses [29, 30]. Logistic regression analysis mainly predicts the probability of occurrence of an event through the multiple regression relationship between a dependent variable and multiple independent variables. In the logistic regression analysis, the dependent variable Y is a dichotomous variable, the values Y = 1 and Y = 0, represent landslide and no landslide, respectively. The independent variables are X₁, X₂,...,X_n respectively. The conditional probability of landslide occurring under the independent variables is P = P (Y = 1 | X1, X2, ..., Xn). Then logistic regression model can be expressed as:

Z_i is intermediate variable parameter, a ₀ is regression constants, a_i is regression coefficients (i=1,2,…n), X_ij is the value of the j_th variable in element i, and P_i is regression prediction value of landslide occurrence in unit i.

The first stage in the application of logistic regression analyses is production of data matrix. For the continuous variable data (altitude, Slope angle, , topographic relief, NDVI, maximum rainfall intensity, distance to rivers), draw the histogram of the frequency distribution of continuous variables, the continuous variable data were normalized in the range of [0, 1] according to the frequency distribution of the histogram. Since the parameter slope aspect, rock types, soil types, land use type are categorical data, they were expressed in binary format with respect to each definitions. Dependent variables of the analyses are also expressed in binary format with respect to presence (1) and absence (0) of

landslide or no landslide cell.

The logistic regression analysis was calculated in SPSS software. As a result regression analysis showed that the average correct classification percentage was 75.4%. Hosmer and Lemeshow test showed that the significance (Sig) is less than 0.05. The logistic regression model of landslide risk factors is shown in Table 6.

The data used are shown in Table 2. Various GIS data layers have been illustrated in Fig. (3). For the convenience of computing, all input layers were converted into raster layers. Then, using the raster calculator, the result of each cell is obtained on the basis of the LR model and the established BP model above. The weights and threshold of each factor estimated by neural network in this study are shown in Tables 3-5. At last, the calculated results were reclassified into three categories according to value: lower sensitive zone, medium sensitive zone and high sensitive zone (Fig. 7).

Fig. (6). Receiver Operating Characteristic (ROC) curve(a. training data;b. test data).

Fig. (7). Landslide Susceptibility Map by BP and LR.

The artificial neural network model and the logistic regression model can be verified mutually. According to the results, the landslide bodies are distributed at each sensitive level. There are 70.8% and 55.21% of the landslides distributed in the high sensitive zone by BP and LR model. The sensitivity level was higher and the bigger proportion of the landslides. The prediction results of ANN model in high sensitive zone is more accurate than the logistic regression model. However, this does not mean that the artificial neural network model is better than the ANN model in other geological environments.

It is difficult to get a complete and detailed shallow landslide map in short-term because the landslides have the characteristics of small size and wide distribution. Hence, it is needed for landslide susceptibility assessment work under the conditions of incomplete records. We employed ANN model and logistic regression to analyze landslide susceptibility and to select the slope, land use and so on eleven factors to establish susceptibility evaluation index system. The results show that the ANN model is feasible to susceptibility map. The susceptibility zoning map was in line with the actual conditions of the area. It can play an important role in the work of landslide hazard and risk assessment of disasters.

In South China, shallow landslides are commonly triggered by high pore-water pressure which results from high-intensity or short-duration rainfall. The deformation modes of slope in red soil hilly region are mainly shallow landslide. Shallow landslides are preferentially distributed on slopes with high-permeable soils overlaying low-permeable soils (Table 7). The landslides are roughly parallel to the ground surface. The shallow landslides are highly correlated to the landform. The results conform to the landslide characteristic in red hilly region in the South China. The research results show that high sensitive zones in the study area locate at the hilly ground area with elevation from150 to 300m,northern aspect, slope gradient among5-10°and topographic relief with 0-40m.

Regarding the application of artificial neural network and logistic regression model, as well as the relative importance and weighting between factors calculated, landslide hazard maps are of great help to planners and engineers when they choose suitable locations to implement development activities. These results can be used as basic data to assist slope management and land-use planning. The models used in the study are valid for generalized planning and assessment purposes, although they may be less useful at the site-specific scale where local geological and geographic heterogeneities may prevail. To make the model more general, more landslide data are needed.

In this study, the neural network model and its cross-application approach was used successfully. Using ANN model established by MATLAB, the landslide susceptibility map was created and verified. The result of verification showed a prediction accuracy of 82.6%. The verification result is of a high value. The conclusion basically matches with the actual situation, so it shows that it is feasible to use the BP network model based on MATLAB neural network toolbox for landslide susceptibility analysis. The results display that slope, rock types and land use type were the main controlling factors in the disaster formation process. The results conform to the disaster mechanism in the South China.