THE ZONATION OF LANDSLIDE OCCURRENCE USING OF SUPPORT VECTOR MACHINES ALGORITHM (CASE STUDY: DARAKEH BASIN)

Landslide is a geologic process that occurs over a wide variety of spatial and temporal scales in many mountainous landscapes. Landslides have a correspondingly wide range of effects that depends strongly on their spatial pattern of occurrence and frequency and magnitude of movement. Mass movements can be the dominant source of erosion responsible for the long-term geomorphic evolution of hillslope morphology. A number of different models have been developed for landslide susceptibility mapping, such as heuristic, conditional probabilistic, logistic regression (LR), artificial neural network (ANN), support vector machine (SVM), and deterministic models. These approaches have been reviewed in detail in recent publications (Carrara et al., 1995, 1999; Aleotti and Chowdhury, 1999; Guzzetti et al., 1999; Dai et al., 2002; Guzzetti, 2003; van Westen, 2004; Brenning, 2005; Wang et al., 2005; Chacon et al., 2006; Alexander, 2008; Corominas and Moya, 2008; van Westen et al., 2008). Among these approaches, SVM modeling is becoming increasingly popular. The procedure is based on statistical learning theory, and involves a training phase with associated input and target output values. The trained model is then used to evaluate a separate set of test data (Yao et al., 2008). SVM modeling has been undertaken less frequently than other approaches to landslide susceptibility mapping. Yao et al. (2008) showed that two-class SVM modeling produced more accurate susceptibility maps than one-class SVM and LR modeling on the natural slopes of Hong Kong, China. Brenning (2005) showed the predictive power of LR, SVMs and bootstrap-aggregated classification trees in a case study of the Ecuadorian Andes. In that study, LR with stepwise backward-selection of variables yields the lowest error rates and demonstrates the best generalization capabilities. Landslide is among slope process dominant on South Alborz and especially in Darakeh basin. Identify area that are prone to landslide is very important, because near of basin to Tehran city, tourism aspect and human settlement in the basin. In this research we tried using SVM algorithm is determined and zonation landslide hazard and Areas susceptible to landslides in the basin. Seems that output of research has a role in sustainable environmental management and a document used in future planning for development of infrastructure.