Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation
Earthquakes are among the most challenging natural phenomena to predict. Most of these unpredictable earthquakes result in the loss of human lives and property. Seismologists can estimate the probable location and magnitude of such earthquakes. However, the actual time and extent of their impact rem...
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Istanbul University Press
2022-12-01
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| Series: | Acta Infologica |
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| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/E18862D014CD4BC0A26C839AD994E066 |
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| author | Ayşe Berika Varol Malkoçoğlu Zeynep Orman Rüya Şamlı |
| author_facet | Ayşe Berika Varol Malkoçoğlu Zeynep Orman Rüya Şamlı |
| author_sort | Ayşe Berika Varol Malkoçoğlu |
| collection | DOAJ |
| description | Earthquakes are among the most challenging natural phenomena to predict. Most of these unpredictable earthquakes result in the loss of human lives and property. Seismologists can estimate the probable location and magnitude of such earthquakes. However, the actual time and extent of their impact remain unknown. If the effects of possible earthquakes can be predicted, quick and accurate decisions can be made. For this purpose, developing predictive models about earthquakes is a prevalent and vital issue in the literature. In this study, various Machine Learning (ML) algorithms were compared on a public dataset of earthquakes, which had occurred worldwide and had a local magnitude Ml ≥ 3, and the algorithm with the highest performance was selected and optimized with various other algorithms. The performances of the models were compared using different performance evaluation metrics such as accuracy, Mean Square Error, Root-Mean Square Error, precision, recall, and f1 score. As a result, it was observed that the Artificial Neural Network (ANN) algorithm optimized with the Particle Swarm Optimization (PSO) algorithm produced the most successful result with an accuracy value of 0.82. Based on the obtained results, it is believed that this model can be used in different earthquake damage prediction studies and as a guide in emergency planning. |
| format | Article |
| id | doaj-art-a7bfbc7070b64bc1aecdf581e4e29195 |
| institution | DOAJ |
| issn | 2602-3563 |
| language | English |
| publishDate | 2022-12-01 |
| publisher | Istanbul University Press |
| record_format | Article |
| series | Acta Infologica |
| spelling | doaj-art-a7bfbc7070b64bc1aecdf581e4e291952025-08-20T03:11:49ZengIstanbul University PressActa Infologica2602-35632022-12-016226528110.26650/acin.1146097123456Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage EstimationAyşe Berika Varol Malkoçoğlu0https://orcid.org/0000-0003-1856-9636Zeynep Orman1https://orcid.org/0000-0002-0205-4198Rüya Şamlı2https://orcid.org/0000-0002-8723-1228Beykoz University, Istanbul, TurkiyeIstanbul-Cerrahpasa University,, Istanbul, TurkiyeIstanbul-Cerrahpasa University,, Istanbul, TurkiyeEarthquakes are among the most challenging natural phenomena to predict. Most of these unpredictable earthquakes result in the loss of human lives and property. Seismologists can estimate the probable location and magnitude of such earthquakes. However, the actual time and extent of their impact remain unknown. If the effects of possible earthquakes can be predicted, quick and accurate decisions can be made. For this purpose, developing predictive models about earthquakes is a prevalent and vital issue in the literature. In this study, various Machine Learning (ML) algorithms were compared on a public dataset of earthquakes, which had occurred worldwide and had a local magnitude Ml ≥ 3, and the algorithm with the highest performance was selected and optimized with various other algorithms. The performances of the models were compared using different performance evaluation metrics such as accuracy, Mean Square Error, Root-Mean Square Error, precision, recall, and f1 score. As a result, it was observed that the Artificial Neural Network (ANN) algorithm optimized with the Particle Swarm Optimization (PSO) algorithm produced the most successful result with an accuracy value of 0.82. Based on the obtained results, it is believed that this model can be used in different earthquake damage prediction studies and as a guide in emergency planning.https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/E18862D014CD4BC0A26C839AD994E066earthquakedamage predictionmachine learningoptimization algorithmsartificial neural networksparticle swarm optimization |
| spellingShingle | Ayşe Berika Varol Malkoçoğlu Zeynep Orman Rüya Şamlı Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation Acta Infologica earthquake damage prediction machine learning optimization algorithms artificial neural networks particle swarm optimization |
| title | Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation |
| title_full | Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation |
| title_fullStr | Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation |
| title_full_unstemmed | Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation |
| title_short | Comparison of Different Heuristics Integrated with Neural Networks: A Case Study for Earthquake Damage Estimation |
| title_sort | comparison of different heuristics integrated with neural networks a case study for earthquake damage estimation |
| topic | earthquake damage prediction machine learning optimization algorithms artificial neural networks particle swarm optimization |
| url | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/E18862D014CD4BC0A26C839AD994E066 |
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