Track: Decision Sciences

Abstract

Based on the frequently unanticipated occurrences of natural landslide disaster across Malaysia, it can be seen that Malaysia is still not fully prepared for occurrences of natural landslide disaster. The lack of predictive and warning systems for the disaster in the country is creating panic and apprehension among citizens alongside with both economic and property losses. The general objectives of this research are: to identify the meteorological factors that cause landslide natural disaster occurrences and to develop a predictive model for landslide disaster occurrence in Malaysia. This research therefore explored occurrences of disasters in order to develop the predict model, which will serve as warning and prevention to avert landslide disasters in Penang, Malaysia. Data from Malaysian Meteorological department on climatic parameters such as daily mean temperature and daily rainfall were considered.  Data mining and Artificial Neural Networks (ANN) shall be developed to predict landslide disaster occurrences.  Thus, the need for a predictive model for occurrence of landslide natural disaster is imperative to the safety of lives and protection of both environmental and economy of the region.