Abstract
Landslide is one of the natural disasters in Malaysia. It causes property damages, infrastructure destruction, injuries and causalities. Landslide hazard mapping is one of the efforts to identify the landslide prone areas with the purpose of reducing the risk of landslide hazards. In this paper, landslide hazard map of the study area, Penang Island Malaysia, is produced using artificial neural network model. Penang Island dataset is collected and its data samples are used to train the artificial neural networks. This study deals with the hidden layer of ANNs. The number of hidden neurons in hidden layer is one of the important parameters of the neural network. Although the hidden layer is not interacted with the external environment but it has tremendous influence on the final output. The different number of hidden neurons of artificial neural networks applied on landslide data produce landslide hazard maps with distinct accuracies and computation time. Finally, Receiver of Characteristics curve is applied on whole Penang Island dataset to validate the accuracy and effectiveness of trained artificial neural model.
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Acknowledgements
The author would like to thank Malaysia Education Ministry/Kementerian Pendidikan Malaysia (KPM) for providing the financial support under research grant (FRGS—Geran Penyelidikan Fundamental 203/PELECT/6071390) in this project.
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Huqqani, I.A., Tay, L.T., Mohamad-Saleh, J. (2021). Landslide Hazard Mapping of Penang Island Malaysia Based on Multilayer Perceptron Approach. In: Guzzetti, F., Mihalić Arbanas, S., Reichenbach, P., Sassa, K., Bobrowsky, P.T., Takara, K. (eds) Understanding and Reducing Landslide Disaster Risk. WLF 2020. ICL Contribution to Landslide Disaster Risk Reduction. Springer, Cham. https://doi.org/10.1007/978-3-030-60227-7_21
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