GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA

Disaster risk reduction and management (DRRM) not only requires a thorough understanding of hazards but also knowledge of how much built-up structures are exposed and vulnerable to a specific hazard. This study proposed a rapid earthquake exposure and vulnerability mapping methodology using the muni...

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Autores principales:	M. J. D. De Los Santos, J. A. Principe
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
Acceso en línea:	https://doaj.org/article/722e370d15944c4b88bf6f63bf54de3f
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spelling	oai:doaj.org-article:722e370d15944c4b88bf6f63bf54de3f2021-11-19T00:13:16ZGIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA10.5194/isprs-archives-XLVI-4-W6-2021-125-20211682-17502194-9034https://doaj.org/article/722e370d15944c4b88bf6f63bf54de3f2021-11-01T00:00:00Zhttps://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-4-W6-2021/125/2021/isprs-archives-XLVI-4-W6-2021-125-2021.pdfhttps://doaj.org/toc/1682-1750https://doaj.org/toc/2194-9034Disaster risk reduction and management (DRRM) not only requires a thorough understanding of hazards but also knowledge of how much built-up structures are exposed and vulnerable to a specific hazard. This study proposed a rapid earthquake exposure and vulnerability mapping methodology using the municipality of Porac, Pampanaga as a case study. To address the challenges and limitations of data access and availability in DRRM operations, this study utilized Light Detection and Ranging (LiDAR) data and machine learning (ML) algorithms to produce an exposure database and conduct vulnerability estimation in the study area. Buildings were delineated through image thresholding and classification of the normalized Digital Surface Model (nDSM) and an exposure database containing building attributes was created using Geographic Information System (GIS). ML algorithms such as Support Vector Machine (SVM), logistic regression, and Random Forest (RF) were then used to predict the model building type (MBT) of delineated buildings to estimate seismic vulnerability. Results showed that the SVM model yielded the lowest accuracy (53%) while logistic regression and RF models performed fairly (72% and 78% respectively) as indicated by their F-1 scores. To improve the accuracy of the exposure database and vulnerability estimation, this study recommends that the proposed building delineation process be further refined by experimenting with more appropriate thresholds or by conducting point cloud classification instead of pixel-based image classification. Moreover, ground truth MBT samples should be used as training data for MBT prediction. For future work, the methodology proposed in this study can be implemented when conducting earthquake damage assessments.M. J. D. De Los SantosJ. A. PrincipeCopernicus PublicationsarticleTechnologyTEngineering (General). Civil engineering (General)TA1-2040Applied optics. PhotonicsTA1501-1820ENThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVI-4-W6-2021, Pp 125-132 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820
spellingShingle	Technology T Engineering (General). Civil engineering (General) TA1-2040 Applied optics. Photonics TA1501-1820 M. J. D. De Los Santos J. A. Principe GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA
description	Disaster risk reduction and management (DRRM) not only requires a thorough understanding of hazards but also knowledge of how much built-up structures are exposed and vulnerable to a specific hazard. This study proposed a rapid earthquake exposure and vulnerability mapping methodology using the municipality of Porac, Pampanaga as a case study. To address the challenges and limitations of data access and availability in DRRM operations, this study utilized Light Detection and Ranging (LiDAR) data and machine learning (ML) algorithms to produce an exposure database and conduct vulnerability estimation in the study area. Buildings were delineated through image thresholding and classification of the normalized Digital Surface Model (nDSM) and an exposure database containing building attributes was created using Geographic Information System (GIS). ML algorithms such as Support Vector Machine (SVM), logistic regression, and Random Forest (RF) were then used to predict the model building type (MBT) of delineated buildings to estimate seismic vulnerability. Results showed that the SVM model yielded the lowest accuracy (53%) while logistic regression and RF models performed fairly (72% and 78% respectively) as indicated by their F-1 scores. To improve the accuracy of the exposure database and vulnerability estimation, this study recommends that the proposed building delineation process be further refined by experimenting with more appropriate thresholds or by conducting point cloud classification instead of pixel-based image classification. Moreover, ground truth MBT samples should be used as training data for MBT prediction. For future work, the methodology proposed in this study can be implemented when conducting earthquake damage assessments.
format	article
author	M. J. D. De Los Santos J. A. Principe
author_facet	M. J. D. De Los Santos J. A. Principe
author_sort	M. J. D. De Los Santos
title	GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA
title_short	GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA
title_full	GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA
title_fullStr	GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA
title_full_unstemmed	GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA
title_sort	gis-based rapid earthquake exposure and vulnerability mapping using lidar dem and machine learning algorithms: case of porac, pampanga
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/722e370d15944c4b88bf6f63bf54de3f
work_keys_str_mv	AT mjddelossantos gisbasedrapidearthquakeexposureandvulnerabilitymappingusinglidardemandmachinelearningalgorithmscaseofporacpampanga AT japrincipe gisbasedrapidearthquakeexposureandvulnerabilitymappingusinglidardemandmachinelearningalgorithmscaseofporacpampanga
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GIS-BASED RAPID EARTHQUAKE EXPOSURE AND VULNERABILITY MAPPING USING LIDAR DEM AND MACHINE LEARNING ALGORITHMS: CASE OF PORAC, PAMPANGA

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