Disaster management system based on Levenberg-Marquardt algorithm artificial neural network / W Ahmad Syafiq Hilmi Wan Abdull Hamid ...[et al.]

• Main Authors: Wan Abdull Hamid, W Ahmad Syafiq Hilmi, Hussin, Mohamad Fahmi, Samsudin, Khairilmizal, Mohd Yassin, Ahmad Ihsan, Mohd Rafi, Anas
• Format: Article
• Language: English
• Published: UiTM Press 2017
• Subjects: Computer engineering. Computer hardware
• Online Access: https://ir.uitm.edu.my/id/eprint/63009/1/63009.pdf; https://ir.uitm.edu.my/id/eprint/63009/; https://jeesr.uitm.edu.my/v1/
• Institution: Universiti Teknologi Mara

This paper presents Disaster Management System Based on Levenberg-Marquardt Algorithm Artificial Neural Network. Although Malaysia is located outside the “Pacific Rim of Fire” and protected from severe ravages caused by natural disasters, however, Malaysia do still experience other disasters. In Malaysia, the disaster management is laid out under integrated system called the Malaysia National Security Council Directive No. 20 (MNSC No. 20). Unfortunately, the policy introduced in the year 1997 is not enough to help the responders managing disasters efficiently. Study shows, a computerized system was identified as one of the best tools in supporting the responders in Malaysia especially the lead responding agency to manage disasters. Thus, the Disaster Management System Based on Levenberg-Marquardt Algorithm Artificial Neural Network was developed with the aim to help and assisting responders (FRDM first responders) in Malaysia to manage disaster particularly during early stage of response phase. The objective of this paper is to analyse the system in terms of accuracy of system (MLP model). Mean Square Error (MSE) value was used to identify the suitable model for the ANN system. The analysis of the results shows that the best model of ANN is at 15 neurons with the MSE of 0.0159 which will be discussed thoroughly in this paper.