Location models to optimize flood monitoring networks

Detalhes bibliográficos
Ano de defesa: 2023
Autor(a) principal: Simoyama, Felipe
Orientador(a): Salles Neto, Luiz Leduino
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
dARK ID: ark:/48912/001300002tdh2
Idioma: eng
Instituição de defesa: Universidade Federal de São Paulo
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://repositorio.unifesp.br/handle/11600/69543
Resumo: Floods are the most common type of disaster worldwide, accounting for approximately 43% of occurrences of disasters over the past 20 years. Besides the loss of hundreds of thousands of lives globally, it is estimated that over 600 billion dollars in damages have been caused by floods, including damage to infrastructure such as homes, schools, and hospitals. Concurrently, floods have significant impacts on urban mobility. For example, when a road is flooded, it not only affects local residents but also those who use that road and its surroundings for transportation. Furthermore, due to logistical issues and their economic consequences, these problems indirectly affect even those who do not pass through the flooded area. Flash floods are often triggered by extreme rainfall events that occur in areas with drainage problems, such as urban roadways. To minimize the impacts of flooding, transportation departments in various cities and metropolitan regions around the world use sensor data to issue alerts and take actions to mitigate these impacts. One of the most commonly used devices is the rain gauge, as it provides point-specific and factual information about rainfall. These sensors are generally not deployed in isolation but rather forming a network. The placement of these networked sensors is a widely discussed problem in the literature, especially in the last decade, with the number of publications increasing year by year. The four studies that constitute this thesis specifically address this problem through the lenses of operations research. A systematic literature review was conducted on the optimization of rain gauge networks, along with three applications of location models for this problem. These studies, presented in their entirety in this thesis, demonstrate the importance of adjusting classical location models to the specific problem, given its intrinsic uncertainties and the uniqueness of each study area.
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spelling http://lattes.cnpq.br/9147853693310634http://lattes.cnpq.br/3728820959678712Simoyama, Felipehttp://lattes.cnpq.br/4374718104844542Salles Neto, Luiz LeduinoSantos, Leonardo Bacelar Lima2023-11-27T11:10:30Z2023-11-27T11:10:30Z2023-10-31Floods are the most common type of disaster worldwide, accounting for approximately 43% of occurrences of disasters over the past 20 years. Besides the loss of hundreds of thousands of lives globally, it is estimated that over 600 billion dollars in damages have been caused by floods, including damage to infrastructure such as homes, schools, and hospitals. Concurrently, floods have significant impacts on urban mobility. For example, when a road is flooded, it not only affects local residents but also those who use that road and its surroundings for transportation. Furthermore, due to logistical issues and their economic consequences, these problems indirectly affect even those who do not pass through the flooded area. Flash floods are often triggered by extreme rainfall events that occur in areas with drainage problems, such as urban roadways. To minimize the impacts of flooding, transportation departments in various cities and metropolitan regions around the world use sensor data to issue alerts and take actions to mitigate these impacts. One of the most commonly used devices is the rain gauge, as it provides point-specific and factual information about rainfall. These sensors are generally not deployed in isolation but rather forming a network. The placement of these networked sensors is a widely discussed problem in the literature, especially in the last decade, with the number of publications increasing year by year. The four studies that constitute this thesis specifically address this problem through the lenses of operations research. A systematic literature review was conducted on the optimization of rain gauge networks, along with three applications of location models for this problem. 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dc.title.pt_BR.fl_str_mv Location models to optimize flood monitoring networks
dc.title.alternative.pt_BR.fl_str_mv Modelos de localização para otimizar redes de monitoramento de alagamentos
title Location models to optimize flood monitoring networks
spellingShingle Location models to optimize flood monitoring networks
Simoyama, Felipe
Optimization
Location models
Flood monitoring
Rain gauge
Maximum coverage
title_short Location models to optimize flood monitoring networks
title_full Location models to optimize flood monitoring networks
title_fullStr Location models to optimize flood monitoring networks
title_full_unstemmed Location models to optimize flood monitoring networks
title_sort Location models to optimize flood monitoring networks
author Simoyama, Felipe
author_facet Simoyama, Felipe
author_role author
dc.contributor.advisor-coLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/9147853693310634
dc.contributor.advisorLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/3728820959678712
dc.contributor.authorLattes.pt_BR.fl_str_mv http://lattes.cnpq.br/4374718104844542
dc.contributor.author.fl_str_mv Simoyama, Felipe
dc.contributor.advisor1.fl_str_mv Salles Neto, Luiz Leduino
dc.contributor.advisor-co1.fl_str_mv Santos, Leonardo Bacelar Lima
contributor_str_mv Salles Neto, Luiz Leduino
Santos, Leonardo Bacelar Lima
dc.subject.por.fl_str_mv Optimization
Location models
Flood monitoring
Rain gauge
Maximum coverage
topic Optimization
Location models
Flood monitoring
Rain gauge
Maximum coverage
description Floods are the most common type of disaster worldwide, accounting for approximately 43% of occurrences of disasters over the past 20 years. Besides the loss of hundreds of thousands of lives globally, it is estimated that over 600 billion dollars in damages have been caused by floods, including damage to infrastructure such as homes, schools, and hospitals. Concurrently, floods have significant impacts on urban mobility. For example, when a road is flooded, it not only affects local residents but also those who use that road and its surroundings for transportation. Furthermore, due to logistical issues and their economic consequences, these problems indirectly affect even those who do not pass through the flooded area. Flash floods are often triggered by extreme rainfall events that occur in areas with drainage problems, such as urban roadways. To minimize the impacts of flooding, transportation departments in various cities and metropolitan regions around the world use sensor data to issue alerts and take actions to mitigate these impacts. One of the most commonly used devices is the rain gauge, as it provides point-specific and factual information about rainfall. These sensors are generally not deployed in isolation but rather forming a network. The placement of these networked sensors is a widely discussed problem in the literature, especially in the last decade, with the number of publications increasing year by year. The four studies that constitute this thesis specifically address this problem through the lenses of operations research. A systematic literature review was conducted on the optimization of rain gauge networks, along with three applications of location models for this problem. These studies, presented in their entirety in this thesis, demonstrate the importance of adjusting classical location models to the specific problem, given its intrinsic uncertainties and the uniqueness of each study area.
publishDate 2023
dc.date.accessioned.fl_str_mv 2023-11-27T11:10:30Z
dc.date.available.fl_str_mv 2023-11-27T11:10:30Z
dc.date.issued.fl_str_mv 2023-10-31
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
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dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Universidade Federal de São Paulo
publisher.none.fl_str_mv Universidade Federal de São Paulo
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