Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization
Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
|
| Series: | Geosciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3263/11/2/99 |
| _version_ | 1797395892576190464 |
|---|---|
| author | Yueqi Gu Orhun Aydin Jacqueline Sosa |
| author_facet | Yueqi Gu Orhun Aydin Jacqueline Sosa |
| author_sort | Yueqi Gu |
| collection | DOAJ |
| description | Post-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area. |
| first_indexed | 2024-03-09T00:42:40Z |
| format | Article |
| id | doaj.art-1fae7366d0094b1fbefcc0da59549f00 |
| institution | Directory Open Access Journal |
| issn | 2076-3263 |
| language | English |
| last_indexed | 2024-03-09T00:42:40Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geosciences |
| spelling | doaj.art-1fae7366d0094b1fbefcc0da59549f002023-12-11T17:43:15ZengMDPI AGGeosciences2076-32632021-02-011129910.3390/geosciences11020099Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial OptimizationYueqi Gu0Orhun Aydin1Jacqueline Sosa2Spatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USASpatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USASpatial Sciences Institute, University of Southern California, Los Angeles, CA 90089, USAPost-earthquake relief zone planning is a multidisciplinary optimization problem, which required delineating zones that seek to minimize the loss of life and property. In this study, we offer an end-to-end workflow to define relief zone suitability and equitable relief service zones for Los Angeles (LA) County. In particular, we address the impact of a tsunami in the study due to LA’s high spatial complexities in terms of clustering of population along the coastline, and a complicated inland fault system. We design data-driven earthquake relief zones with a wide variety of inputs, including geological features, population, and public safety. Data-driven zones were generated by solving the p-median problem with the Teitz–Bart algorithm without any a priori knowledge of optimal relief zones. We define the metrics to determine the optimal number of relief zones as a part of the proposed workflow. Finally, we measure the impacts of a tsunami in LA County by comparing data-driven relief zone maps for a case with a tsunami and a case without a tsunami. Our results show that the impact of the tsunami on the relief zones can extend up to 160 km inland from the study area.https://www.mdpi.com/2076-3263/11/2/99tsunamiearthquakerelief zoneresource allocationspatial optimization |
| spellingShingle | Yueqi Gu Orhun Aydin Jacqueline Sosa Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization Geosciences tsunami earthquake relief zone resource allocation spatial optimization |
| title | Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization |
| title_full | Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization |
| title_fullStr | Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization |
| title_full_unstemmed | Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization |
| title_short | Quantifying the Impact of a Tsunami on Data-Driven Earthquake Relief Zone Planning in Los Angeles County via Multivariate Spatial Optimization |
| title_sort | quantifying the impact of a tsunami on data driven earthquake relief zone planning in los angeles county via multivariate spatial optimization |
| topic | tsunami earthquake relief zone resource allocation spatial optimization |
| url | https://www.mdpi.com/2076-3263/11/2/99 |
| work_keys_str_mv | AT yueqigu quantifyingtheimpactofatsunamiondatadrivenearthquakereliefzoneplanninginlosangelescountyviamultivariatespatialoptimization AT orhunaydin quantifyingtheimpactofatsunamiondatadrivenearthquakereliefzoneplanninginlosangelescountyviamultivariatespatialoptimization AT jacquelinesosa quantifyingtheimpactofatsunamiondatadrivenearthquakereliefzoneplanninginlosangelescountyviamultivariatespatialoptimization |