Marhain, Suzlyana and Ahmed, Ali Najah and Murti, Muhammad Ary and Kumar, Pavitra and El-Shafie, Ahmed (2021) Investigating the application of artificial intelligence for earthquake prediction in Terengganu. Natural Hazards, 108 (1). pp. 977-999. ISSN 0921-030X, DOI https://doi.org/10.1007/s11069-021-04716-7.
Full text not available from this repository.Abstract
Earthquake is one of the devastating and frightening natural disasters that caused big casualties in a small duration. Earthquake caused lots of damage in just a few minutes and the casualties of the earthquake increase as the population increase which also contribute to higher amount of property and buildings. Therefore, by developing model capable of detecting the recurrence behaviour of earthquake helps in predicting earthquake as well as minimizing the casualties caused by the earthquake. In this report, a few of artificial intelligence algorithms such as support vector machine, boosted decision tree regression, random forest and multivariate adaptive regression spline will be used in the development of best model algorithm in earthquake prediction. Meteorological data are collected from several stations in Terengganu and processed for normalization and the data will be analysed using algorithms and its performance will be evaluated. Terengganu is situated on the east coast of Peninsular Malaysia and is bordered on the north-west and south-west by Kelantan and Pahang. Terengganu's east side is bordered by the South China Sea. Terengganu is located within the vicinity of the South China Sea, which is possible to be affected by the Marina Trench Earthquake. The subduction zone of Manila Trench is capable of producing a high magnitude of earthquake activity that can create a deadliest tsunami disaster. Therefore, Terengganu is studied for the investigation of artificial intelligence in earthquake prediction. The model algorithms are then analysed to measure its sensitivity and accuracy in prediction and consistency of the result.
| Item Type: | Article |
|---|---|
| Funders: | Innovation & Research Management Center (iRMC), Universiti Tenaga Nasional (UNITEN) in Malaysia (2020105TELCO), Telkom University, Bandung in Indonesia |
| Uncontrolled Keywords: | Earthquake prediction; Artificial intelligence; Machine learning algorithm; Support vector machine; Boosted decision tree regression; Random forest; Multivariate adaptive regression spline; Pearson correlation coefficient; Uncertainty analysis |
| Subjects: | Q Science > QE Geology T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 28 Jun 2022 04:33 |
| Last Modified: | 28 Jun 2022 04:33 |
| URI: | http://eprints.um.edu.my/id/eprint/27989 |
Actions (login required)
 |
View Item |
