Machine Learning for Disaster Risk Management

Vivien Deparday, C.M. Gevaert, Giuseppe Molinario, Robert Soden, Simone Balog-Way

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Abstract

Evidence-driven disaster risk management (DRM) relies upon many different data types, information sources, and types of models to be effective. Tasks such as weather modelling, earthquake fault line rupture, or the development of dynamic urban exposure measures involve complex science and large amounts of data from a range of sources. Even experts can struggle to develop models that enable the understanding of the potential impacts of a hazard on the built environment and society.In this context, this guidance note explores how new approaches in machine learning can provide newways of looking into these complex relationships and provide more accurate, efficient, and useful answers. The goal of this document is to provide a concise, demystifying reference that readers, from project managers to data scientists, can use to better understand how machine learning can be applied in disaster risk management projects. There are many sources of information on this complex and evolving set of technologies. Therefore, this guidance note is aimed to be as focused as possible, providing basic information and DRM-specific case studies and directing readers to additional resources including online videos, infographics, courses, and articles for further reference. A machine learning (ML) algorithm is a type of computer program that learns to perform specific tasks based on various data inputs or rules provided by its designer. Machine learning is a subset of artificial intelligence (AI), but the two terms are often used interchangeably. For a thorough discussion of the differences and similarities of the terms ML and AI, see Section 2. As the name implies, an ML algorithm’s purpose is to “learn” from previous data and output a result that adds information and insight that was not previously known. This approach enables actions to be taken on the information gathered from the data; sometimes in near real time, like suggested web search results, and sometimes with longer term human input, like many of the DRM case studies presented in this document. Over the past few decades, there has been an enormous increase incomputational capacity and speed and available sensor data, exponentially increasing the volume of available data for analysis. This has allowed the capabilities of ML algorithms to advance to nearly ubiquitous impact on many aspects of society. Machine learning and artificial intelligencehave become household terms, crossing from academia and specialized industry applications into everyday interactions with technology—from image, sound, and voice recognition features of our smartphones to seamlessly recommending items in online shopping, from mail sorting to ranking results of a search engine. The same technology is being leveraged to answer bigger questions in society, including questions about sustainable development, humanitarian assistance, and disaster risk management.
Original languageEnglish
PublisherWorld Bank
Number of pages51
Publication statusPublished - 16 Jan 2019

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Risk management
Disasters
Learning systems
Learning algorithms
Artificial intelligence
Smartphones
Search engines
Speech recognition
Sorting
Computer program listings
Sustainable development
Earthquakes
Hazards
Managers
Acoustic waves
Sensors
Industry

Keywords

  • ITC-GOLD

Cite this

Deparday, V., Gevaert, C. M., Molinario, G., Soden, R., & Balog-Way, S. (2019). Machine Learning for Disaster Risk Management. World Bank.
Deparday, Vivien ; Gevaert, C.M. ; Molinario, Giuseppe ; Soden, Robert ; Balog-Way, Simone. / Machine Learning for Disaster Risk Management. World Bank, 2019. 51 p.
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Deparday, V, Gevaert, CM, Molinario, G, Soden, R & Balog-Way, S 2019, Machine Learning for Disaster Risk Management. World Bank.

Machine Learning for Disaster Risk Management. / Deparday, Vivien; Gevaert, C.M.; Molinario, Giuseppe; Soden, Robert; Balog-Way, Simone.

World Bank, 2019. 51 p.

Research output: Book/ReportReportProfessional

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N2 - Evidence-driven disaster risk management (DRM) relies upon many different data types, information sources, and types of models to be effective. Tasks such as weather modelling, earthquake fault line rupture, or the development of dynamic urban exposure measures involve complex science and large amounts of data from a range of sources. Even experts can struggle to develop models that enable the understanding of the potential impacts of a hazard on the built environment and society.In this context, this guidance note explores how new approaches in machine learning can provide newways of looking into these complex relationships and provide more accurate, efficient, and useful answers. The goal of this document is to provide a concise, demystifying reference that readers, from project managers to data scientists, can use to better understand how machine learning can be applied in disaster risk management projects. There are many sources of information on this complex and evolving set of technologies. Therefore, this guidance note is aimed to be as focused as possible, providing basic information and DRM-specific case studies and directing readers to additional resources including online videos, infographics, courses, and articles for further reference. A machine learning (ML) algorithm is a type of computer program that learns to perform specific tasks based on various data inputs or rules provided by its designer. Machine learning is a subset of artificial intelligence (AI), but the two terms are often used interchangeably. For a thorough discussion of the differences and similarities of the terms ML and AI, see Section 2. As the name implies, an ML algorithm’s purpose is to “learn” from previous data and output a result that adds information and insight that was not previously known. This approach enables actions to be taken on the information gathered from the data; sometimes in near real time, like suggested web search results, and sometimes with longer term human input, like many of the DRM case studies presented in this document. Over the past few decades, there has been an enormous increase incomputational capacity and speed and available sensor data, exponentially increasing the volume of available data for analysis. This has allowed the capabilities of ML algorithms to advance to nearly ubiquitous impact on many aspects of society. Machine learning and artificial intelligencehave become household terms, crossing from academia and specialized industry applications into everyday interactions with technology—from image, sound, and voice recognition features of our smartphones to seamlessly recommending items in online shopping, from mail sorting to ranking results of a search engine. The same technology is being leveraged to answer bigger questions in society, including questions about sustainable development, humanitarian assistance, and disaster risk management.

AB - Evidence-driven disaster risk management (DRM) relies upon many different data types, information sources, and types of models to be effective. Tasks such as weather modelling, earthquake fault line rupture, or the development of dynamic urban exposure measures involve complex science and large amounts of data from a range of sources. Even experts can struggle to develop models that enable the understanding of the potential impacts of a hazard on the built environment and society.In this context, this guidance note explores how new approaches in machine learning can provide newways of looking into these complex relationships and provide more accurate, efficient, and useful answers. The goal of this document is to provide a concise, demystifying reference that readers, from project managers to data scientists, can use to better understand how machine learning can be applied in disaster risk management projects. There are many sources of information on this complex and evolving set of technologies. Therefore, this guidance note is aimed to be as focused as possible, providing basic information and DRM-specific case studies and directing readers to additional resources including online videos, infographics, courses, and articles for further reference. A machine learning (ML) algorithm is a type of computer program that learns to perform specific tasks based on various data inputs or rules provided by its designer. Machine learning is a subset of artificial intelligence (AI), but the two terms are often used interchangeably. For a thorough discussion of the differences and similarities of the terms ML and AI, see Section 2. As the name implies, an ML algorithm’s purpose is to “learn” from previous data and output a result that adds information and insight that was not previously known. This approach enables actions to be taken on the information gathered from the data; sometimes in near real time, like suggested web search results, and sometimes with longer term human input, like many of the DRM case studies presented in this document. Over the past few decades, there has been an enormous increase incomputational capacity and speed and available sensor data, exponentially increasing the volume of available data for analysis. This has allowed the capabilities of ML algorithms to advance to nearly ubiquitous impact on many aspects of society. Machine learning and artificial intelligencehave become household terms, crossing from academia and specialized industry applications into everyday interactions with technology—from image, sound, and voice recognition features of our smartphones to seamlessly recommending items in online shopping, from mail sorting to ranking results of a search engine. The same technology is being leveraged to answer bigger questions in society, including questions about sustainable development, humanitarian assistance, and disaster risk management.

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Deparday V, Gevaert CM, Molinario G, Soden R, Balog-Way S. Machine Learning for Disaster Risk Management. World Bank, 2019. 51 p.