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Evidence-driven disaster risk manage- ment 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.ML algorithms were pioneered in fields like satellite remote sensing and statistical data analysis.
Evidence-driven disaster risk manage- ment 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. The goal of this document is to provide a concise, demystifying ref- erence that readers,from project managers to data scientists, can use to better understand how machine learning can be applied in disaster risk management projects. Over the past few decades, there has been an enormous increase in computational capacity and speed and available sensor data, exponentially increasing the volume of available data for analysis.
ML algorithms were pioneered in fields like satellite remote sensing and statistical data analysis.
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