Earthquake risk assessment in NE India using deep learning and geospatial analysis
Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various trad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-05-01
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| Series: | Geoscience Frontiers |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1674987120302504 |
| _version_ | 1797763670953951232 |
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| author | Ratiranjan Jena Biswajeet Pradhan Sambit Prasanajit Naik Abdullah M. Alamri |
| author_facet | Ratiranjan Jena Biswajeet Pradhan Sambit Prasanajit Naik Abdullah M. Alamri |
| author_sort | Ratiranjan Jena |
| collection | DOAJ |
| description | Earthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km2), vulnerability (480.98 km2), and risk (34,586.10 km2) was estimated. |
| first_indexed | 2024-03-12T19:44:44Z |
| format | Article |
| id | doaj.art-e6b7fc2470cc4228a2c27b3cafebdb63 |
| institution | Directory Open Access Journal |
| issn | 1674-9871 |
| language | English |
| last_indexed | 2024-03-12T19:44:44Z |
| publishDate | 2021-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Geoscience Frontiers |
| spelling | doaj.art-e6b7fc2470cc4228a2c27b3cafebdb632023-08-02T03:37:00ZengElsevierGeoscience Frontiers1674-98712021-05-01123101110Earthquake risk assessment in NE India using deep learning and geospatial analysisRatiranjan Jena0Biswajeet Pradhan1Sambit Prasanajit Naik2Abdullah M. Alamri3The Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, AustraliaThe Center for Advanced Modeling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology, University of Technology Sydney, NSW 2007, Australia; Department of Energy and Mineral Resources Engineering, Sejong University, Choongmu-gwan, 209, Neungdong-roGwangin-gu, Seoul 05006, Republic of Korea; Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600 UKM, Bangi, Selangor, Malaysia; Corresponding author.Active Fault and Earthquake Hazard Mitigation Research Institute, Pukyong National University, Busan 48513, South KoreaDepartment of Geology & Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi ArabiaEarthquake prediction is currently the most crucial task required for the probability, hazard, risk mapping, and mitigation purposes. Earthquake prediction attracts the researchers' attention from both academia and industries. Traditionally, the risk assessment approaches have used various traditional and machine learning models. However, deep learning techniques have been rarely tested for earthquake probability mapping. Therefore, this study develops a convolutional neural network (CNN) model for earthquake probability assessment in NE India. Then conducts vulnerability using analytical hierarchy process (AHP), Venn's intersection theory for hazard, and integrated model for risk mapping. A prediction of classification task was performed in which the model predicts magnitudes more than 4 Mw that considers nine indicators. Prediction classification results and intensity variation were then used for probability and hazard mapping, respectively. Finally, earthquake risk map was produced by multiplying hazard, vulnerability, and coping capacity. The vulnerability was prepared by using six vulnerable factors, and the coping capacity was estimated by using the number of hospitals and associated variables, including budget available for disaster management. The CNN model for a probability distribution is a robust technique that provides good accuracy. Results show that CNN is superior to the other algorithms, which completed the classification prediction task with an accuracy of 0.94, precision of 0.98, recall of 0.85, and F1 score of 0.91. These indicators were used for probability mapping, and the total area of hazard (21,412.94 km2), vulnerability (480.98 km2), and risk (34,586.10 km2) was estimated.http://www.sciencedirect.com/science/article/pii/S1674987120302504EarthquakeConvolutional neural networkGeospatial information systemsHazardVulnerabilityRisk |
| spellingShingle | Ratiranjan Jena Biswajeet Pradhan Sambit Prasanajit Naik Abdullah M. Alamri Earthquake risk assessment in NE India using deep learning and geospatial analysis Geoscience Frontiers Earthquake Convolutional neural network Geospatial information systems Hazard Vulnerability Risk |
| title | Earthquake risk assessment in NE India using deep learning and geospatial analysis |
| title_full | Earthquake risk assessment in NE India using deep learning and geospatial analysis |
| title_fullStr | Earthquake risk assessment in NE India using deep learning and geospatial analysis |
| title_full_unstemmed | Earthquake risk assessment in NE India using deep learning and geospatial analysis |
| title_short | Earthquake risk assessment in NE India using deep learning and geospatial analysis |
| title_sort | earthquake risk assessment in ne india using deep learning and geospatial analysis |
| topic | Earthquake Convolutional neural network Geospatial information systems Hazard Vulnerability Risk |
| url | http://www.sciencedirect.com/science/article/pii/S1674987120302504 |
| work_keys_str_mv | AT ratiranjanjena earthquakeriskassessmentinneindiausingdeeplearningandgeospatialanalysis AT biswajeetpradhan earthquakeriskassessmentinneindiausingdeeplearningandgeospatialanalysis AT sambitprasanajitnaik earthquakeriskassessmentinneindiausingdeeplearningandgeospatialanalysis AT abdullahmalamri earthquakeriskassessmentinneindiausingdeeplearningandgeospatialanalysis |