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Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia

Acceleration; Decision trees; Disasters; Earthquake effects; Forecasting; Machine learning; Neural networks; Artificial neural network models; Depth; Earthquake; Earthquake acceleration; Ground motion parameters; Input parameter; Machine learning models; Malaysia; Neural-networks; Prediction model;...

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Main Authors: Essam Y., Kumar P., Ahmed A.N., Murti M.A., El-Shafie A.
Other Authors: 57203146903
Format: Article
Published: Elsevier Ltd 2023
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spelling my.uniten.dspace-260722023-05-29T17:06:33Z Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia Essam Y. Kumar P. Ahmed A.N. Murti M.A. El-Shafie A. 57203146903 57206939156 57214837520 24734366700 16068189400 Acceleration; Decision trees; Disasters; Earthquake effects; Forecasting; Machine learning; Neural networks; Artificial neural network models; Depth; Earthquake; Earthquake acceleration; Ground motion parameters; Input parameter; Machine learning models; Malaysia; Neural-networks; Prediction model; Velocity; acceleration; artificial intelligence; artificial neural network; depth determination; earthquake prediction; ground motion; reliability analysis; seismic velocity; Malaysia; Terengganu; West Malaysia Earthquakes have been universally recognised as seismological disasters that pose a threat to civilization and need to be monitored through prediction models. The development and usage of traditional statistical predicting models, which require the understanding of underlying physical scientific processes in a system and large amounts of data preparation, can be challenging and costly. Artificial intelligence-based models, specifically machine learning models, are able to easily review mass data volumes and identify complex data trends to make predictions, making them beneficial to be utilized as prediction models. Terengganu, located on the east coast of Peninsular Malaysia, has experienced three earthquakes in the last four decades and has the potential to be hit or affected by earthquakes due to its location within the vicinity of the South China Sea where the seismologically active Manila Trench is situated. This makes the development of machine learning models for the prediction of earthquakes in Terengganu important for future disaster analysis and management. Therefore, this study suggests artificial neural network (ANN) models as a tool to predict ground motion parameters, namely earthquake acceleration, depth, and velocity, in Terengganu. However, this study presents the comparison of the results of ANN with the results of Random Forest (RF). The data used to develop the models were collected by six seismological stations for two channels in Terengganu and provided by the Malaysian Meteorological Department. The data was partitioned into six sets for each ground motion parameter in each channel, with each set utilizing data from a different grouping of five stations for training and one station for testing. Earthquake depth was able to be modelled with accuracy univariately, that is using only the respective output parameter, which is earthquake depth, as the input parameter. Earthquake acceleration and velocity could not be modelled with accuracy univariately, and were improved by adding earthquake depth as an input parameter. Based on the analysis and evaluation of the results using four selected performance criteria, the ANN models show good performance in predicting earthquake acceleration, depth, and velocity. � 2021 Elsevier Ltd Final 2023-05-29T09:06:33Z 2023-05-29T09:06:33Z 2021 Article 10.1016/j.soildyn.2021.106826 2-s2.0-85106890213 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85106890213&doi=10.1016%2fj.soildyn.2021.106826&partnerID=40&md5=37515a82585133d429cb175d5400f3ca https://irepository.uniten.edu.my/handle/123456789/26072 147 106826 Elsevier Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Acceleration; Decision trees; Disasters; Earthquake effects; Forecasting; Machine learning; Neural networks; Artificial neural network models; Depth; Earthquake; Earthquake acceleration; Ground motion parameters; Input parameter; Machine learning models; Malaysia; Neural-networks; Prediction model; Velocity; acceleration; artificial intelligence; artificial neural network; depth determination; earthquake prediction; ground motion; reliability analysis; seismic velocity; Malaysia; Terengganu; West Malaysia
author2 57203146903
author_facet 57203146903
Essam Y.
Kumar P.
Ahmed A.N.
Murti M.A.
El-Shafie A.
format Article
author Essam Y.
Kumar P.
Ahmed A.N.
Murti M.A.
El-Shafie A.
spellingShingle Essam Y.
Kumar P.
Ahmed A.N.
Murti M.A.
El-Shafie A.
Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia
author_sort Essam Y.
title Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia
title_short Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia
title_full Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia
title_fullStr Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia
title_full_unstemmed Exploring the reliability of different artificial intelligence techniques in predicting earthquake for Malaysia
title_sort exploring the reliability of different artificial intelligence techniques in predicting earthquake for malaysia
publisher Elsevier Ltd
publishDate 2023
_version_ 1806427525297995776
score 13.214268

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