Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran
The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron (ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categori...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/5/1248 |
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| author | Rasoul Afsari Saman Nadizadeh Shorabeh Amir Reza Bakhshi Lomer Mehdi Homaee Jamal Jokar Arsanjani |
| author_facet | Rasoul Afsari Saman Nadizadeh Shorabeh Amir Reza Bakhshi Lomer Mehdi Homaee Jamal Jokar Arsanjani |
| author_sort | Rasoul Afsari |
| collection | DOAJ |
| description | The purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron (ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categories: exposure, sensitivity, and adaptability capacity attributed to a total of 16 spatial criteria, which were inputted into the neural network. To train the neural network and compute an earthquake vulnerability map, we used a combined Multi-Criteria Decision Analysis (MCDA) process with 167 vulnerable locations as training data, of which 70% (117 points) were used for training, and 30% (50 points) were used for testing and validation. The Mean Average Error (<i>MAE</i>) of the implemented neural network was 0.085, which proves the efficacy of the designed model. The results showed that 29% of Tehran’s total area is extremely vulnerable to earthquakes. Our factor importance analysis showed that factors such as proximity to fault lines, high population density, and environmental factors gained higher importance scores for earthquake vulnerability assessment of the given case study. This methodical approach and the choice of data and methods can provide insight into scaling up the study to other regions. In addition, the resultant outcomes can help decision makers and relevant stakeholders to mitigate risks through resilience building. |
| first_indexed | 2024-03-11T07:12:37Z |
| format | Article |
| id | doaj.art-f5529b958d4f4e499c1e33334cd52f82 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T07:12:37Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f5529b958d4f4e499c1e33334cd52f822023-11-17T08:30:28ZengMDPI AGRemote Sensing2072-42922023-02-01155124810.3390/rs15051248Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of TehranRasoul Afsari0Saman Nadizadeh Shorabeh1Amir Reza Bakhshi Lomer2Mehdi Homaee3Jamal Jokar Arsanjani4Department of Passive Defense (Urban Planning of Passive Defense), Superme National Defense University, Tehran 1698613411, IranDepartment of GIS and Remote Sensing, Faculty of Geography, University of Tehran, Tehran 1417935840, IranDepartment of Geography, Birkbeck, University of London, London WC1E 7HX, UKDepartment of Mining and Environmental Engineering, Faculty of Engineering, Tarbiat Modares University, Tehran 14115, IranGeoinformatics Research Group, Department of Planning and Development, Aalborg University Copenhagen, 2450 Copenhagen, DenmarkThe purpose of this study is to assess the vulnerability of urban blocks to earthquakes for Tehran as a city built on geological faults using an artificial neural network—multi-layer perceptron (ANN-MLP). Therefore, we first classified earthquake vulnerability evaluation criteria into three categories: exposure, sensitivity, and adaptability capacity attributed to a total of 16 spatial criteria, which were inputted into the neural network. To train the neural network and compute an earthquake vulnerability map, we used a combined Multi-Criteria Decision Analysis (MCDA) process with 167 vulnerable locations as training data, of which 70% (117 points) were used for training, and 30% (50 points) were used for testing and validation. The Mean Average Error (<i>MAE</i>) of the implemented neural network was 0.085, which proves the efficacy of the designed model. The results showed that 29% of Tehran’s total area is extremely vulnerable to earthquakes. Our factor importance analysis showed that factors such as proximity to fault lines, high population density, and environmental factors gained higher importance scores for earthquake vulnerability assessment of the given case study. This methodical approach and the choice of data and methods can provide insight into scaling up the study to other regions. In addition, the resultant outcomes can help decision makers and relevant stakeholders to mitigate risks through resilience building.https://www.mdpi.com/2072-4292/15/5/1248vulnerabilityearthquakerisk assessmentartificial neural networksTehran |
| spellingShingle | Rasoul Afsari Saman Nadizadeh Shorabeh Amir Reza Bakhshi Lomer Mehdi Homaee Jamal Jokar Arsanjani Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran Remote Sensing vulnerability earthquake risk assessment artificial neural networks Tehran |
| title | Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran |
| title_full | Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran |
| title_fullStr | Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran |
| title_full_unstemmed | Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran |
| title_short | Using Artificial Neural Networks to Assess Earthquake Vulnerability in Urban Blocks of Tehran |
| title_sort | using artificial neural networks to assess earthquake vulnerability in urban blocks of tehran |
| topic | vulnerability earthquake risk assessment artificial neural networks Tehran |
| url | https://www.mdpi.com/2072-4292/15/5/1248 |
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