A Theoretical Review on the Preventive Measures to Landslide Disaster Occurrences in Penang State, Malaysia

Mohamad Ghozali Hassan*, Che AzlanTaib, Muslim Akanmu and Afif Ahmarofi
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Mohamad Ghozali Hassan*: School of Technology Management and Logistics,Universiti Utara Malaysia, 06010 UUM Sintok,Kedah, Malaysia
Che AzlanTaib: School of Technology Management and Logistics,Universiti Utara Malaysia, 06010 UUM Sintok,Kedah, Malaysia
Muslim Akanmu: School of Technology Management and Logistics,Universiti Utara Malaysia, 06010 UUM Sintok,Kedah, Malaysia
Afif Ahmarofi: School of Quantitative Sciences, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia

The Journal of Social Sciences Research, 2018, 753-759 Special Issue: 6

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 in Malaysia and to suggest a predictive model for landslide disaster occurrence in Malaysia. This research therefore explored modelling disasters occurrences in order to predict, warn, and prevent huge impact of landslide disasters in Penang, Malaysia. This research shall make use of past literatures and data from Malaysian Meteorological department considering climatic parameters such as daily mean temperature and daily rainfall only. Data mining and Artificial Neural Networks (ANN) shall be suggested to predict landslide disaster occurrences in Malaysia. 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.

Keywords: Landslide; Natural disaster; Artificial neural network; Malaysia; Predictive model. (search for similar items in EconPapers)
Date: 2018
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