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Landslide presents a significant constraint to development in many parts of Malaysia which experiences frequent landslides, with the most recent occurring in 2000, 2001, 2004, 2007 and 2008. Damages and losses are regularly incurred because; historically there has been too little consideration of the potential problems in land use planning and slope stability analysis. Landslides are mostly occurred in Malaysia mainly due to heavy tropical rainfall. In recent years greater awareness of landslide problems has led to significant changes in the control of development on unstable land. So far, few attempts have been made to predict these landslides or preventing the damage caused by them. In last few years, landslide susceptibility analysis using GIS and data mining such as fuzzy logic, and artificial neural network methods have been applied by researchers in different countries (Akgun et al., 2008; Ercanoglu & Gokceoglu 2002; Gomez & Kavzoglu, 2005; Pistocchi et al. 2002; Lee et al. 2003a, 2003b, 2004a, 2004b). But their result output can not be directly used in the Malaysian landslide susceptibility analysis. This is due to the changes in the geographical environment set up, litho types and different climatic conditions. The local geographical settings cause different landslide types based on completely different mechanisms and are absolute incomparable. Through scientific analysis of landslides, we can assess and predict landslide-susceptible areas, and thus decrease landslide damage through proper preparation. To achieve this aim, landslide susceptibility analysis techniques have been applied, and validated in the study area using five different training strategies with the aid of artificial neural network. In landslide literature, there have been many studies carried out on landslide susceptibility and hazard mapping using GIS. There are number of different approaches for the measurement of landslide hazard, including direct and indirect heuristic approaches, and deterministic, probabilistic, statistical and data mining approaches. Recently, there have been studies on landslide susceptibility mapping using GIS, and many of these studies have applied probabilistic models (Baeza and Corominas, 2001; Clerici et al., 2002; Dahal et al., 2008; Dai et al., 2001; Lee & Dan, 2005; Lee & Lee, 2006; Lee & Min, 2001; Lee & Sambath,

Landslide Susceptibility Mapping: an Assessment of the Use of an Advanced Neural Network Model with Five Different Training Strategies