TY - JOUR
T1 - An integrated approach to seismic risk assessment using random forest and hierarchical analysis
T2 - Pisco, Peru
AU - Izquierdo-Horna, Luis
AU - Zevallos, Jose
AU - Yepez, Yustin
N1 - Publisher Copyright:
© 2022 The Author(s)
PY - 2022/10
Y1 - 2022/10
N2 - As Peru is subject to large seismic movements owing to its geographic condition, determining seismic risk levels is a priority task for designing appropriate management plans. These actions become especially relevant when analyzing Pisco, a Peruvian city which has been heavily affected by various seismic events through the years. Hence, this project aims at estimating the associated seismic risk level and its previous requirements, such as hazard and vulnerability. To this end, a hybrid approach of machine learning (i.e., Random Forest) and hierarchical analysis (i.e., the Saaty matrix) was used. Risk levels were calculated through a double-entry table that establishes the relation between hazard and vulnerability levels. Results suggest that the city of Pisco exhibits both medium (lower city areas) and high (higher city areas) hazard levels in similar proportion. In addition, the coast area is considered a very-high hazard zone. Regarding vulnerability, the central area of the city exhibits a medium vulnerability level, whereas the periphery denotes high and very-high vulnerability levels. The interrelation of these components results in overall high-risk levels, with very-high levels in some central areas of the city. Finally, the results from this research study are expected to be useful for the authorities in charge of fostering specific activities in each sector and, simultaneously, as a motivator for future studies within this field.
AB - As Peru is subject to large seismic movements owing to its geographic condition, determining seismic risk levels is a priority task for designing appropriate management plans. These actions become especially relevant when analyzing Pisco, a Peruvian city which has been heavily affected by various seismic events through the years. Hence, this project aims at estimating the associated seismic risk level and its previous requirements, such as hazard and vulnerability. To this end, a hybrid approach of machine learning (i.e., Random Forest) and hierarchical analysis (i.e., the Saaty matrix) was used. Risk levels were calculated through a double-entry table that establishes the relation between hazard and vulnerability levels. Results suggest that the city of Pisco exhibits both medium (lower city areas) and high (higher city areas) hazard levels in similar proportion. In addition, the coast area is considered a very-high hazard zone. Regarding vulnerability, the central area of the city exhibits a medium vulnerability level, whereas the periphery denotes high and very-high vulnerability levels. The interrelation of these components results in overall high-risk levels, with very-high levels in some central areas of the city. Finally, the results from this research study are expected to be useful for the authorities in charge of fostering specific activities in each sector and, simultaneously, as a motivator for future studies within this field.
KW - Analytic hierarchy Process–Saaty
KW - Disaster risk reduction
KW - Hazard
KW - Peru
KW - Random forest
KW - Vulnerability
UR - http://www.scopus.com/inward/record.url?scp=85139723254&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2022.e10926
DO - 10.1016/j.heliyon.2022.e10926
M3 - Article
AN - SCOPUS:85139723254
SN - 2405-8440
VL - 8
JO - Heliyon
JF - Heliyon
IS - 10
M1 - e10926
ER -