[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119773-en":3,"doc-seo-119773-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119773,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Machine Learning Approaches to Assessing Future Flood - & Storm Risk","This thesis applies machine learning to hydrology problems under both imminent and long-term climate change, framed by data minimalism. It addresses the limitations of traditional empirical models in regions with sparse observational coverage and proposes models that generalize from the data that is available. The work develops dimensionality-compressing strategies, evaluates multiple model architectures with feature engineering as the dominant factor, and extends the framework using externally assessable catchment descriptors such as topography and geology plus proxy variables for human impacts. It also connects flood risk to storm prediction via a multi-task approach for cyclonic activity identification, domain localization, and probabilistic precipitation impact forecasting. Finally, the thesis refines Neural Process models for challenging temporal dependencies and explores scenario impact modelling with climate projections.","Machine Learning Approaches to Assessing Future Flood & Storm Risk  \nRobert Edwin Rouse  \nDepartment of Engineering  \nUniversity of Cambridge  \nThis thesis is submitted for the degree of Doctor of Philosophy  \nDarwin College January 2023  \nDeclaration  \nI hereby declare that except where specific reference is made to the work of others, the contents of this dissertation are original and have not been submitted in whole or in part for consideration for any other degree or qualification in this, or any other university. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and Acknowledgements. This dissertation contains fewer than 65,000 words including appendices, bibliography, footnotes, tables and equations and has fewer than 150 figures.  \nRobert Edwin Rouse  \nJanuary 2023  \nAcknowledgements  \nFirst and foremost, I would like to thank my supervisors Professor Allan McRobie, Professor Emily Shuckburgh, and Dr Scott Hosking; without their help and guidance this work would have been far lesser, if not impossible. They helped shape the narrative arc, pruned back the ideas that were not viable, and gave impetus and ambition to the ones that were.  \nSecondly, I would like to thank my industrial supervisors, Professor David Viner and Dr Sun Yan Evans, who highlighted how to make research impactful in a real world context and how to make it transferable.  \nProfessor Mark Girolami, Will Tebbutt, Risa Ueno, Marc Girona-Mata, Rachel Furner, Tom Andersson, and John Bronskill provided much needed sanity checks throughout my doctorate; the countless discussions that I had with each of them were enjoyable and thought provoking and the wider research group as a whole was also a source of inspiration.  \nMy penultimate thanks goes to my family, who have always supported my research and studies and provided both a respite from my academic life at Cambridge and advice when needed.  \nFinally, I would like to acknowledge my partner, Phoebe, for her eternal patience and love and without whom I would not be the person I am today, nor would I have come close to the finish line.  \nMy doctorate was funded by a studentship in the Future Infrastructure and Build Environment Centre for Doctoral Training from the Engineering and Physical Sciences Research Council; an Industrial Fellowship from the Royal Commission for the Great Exhibition of 1851, in conjunction with Mott MacDonald; and in affiliation with the Artificial Intelligence for Environmental Risk Centre for Doctoral Training. The support of all these institutions made my research possible.  \nMachine Learning Approaches to Assessing Future Flood  \n& Storm Risk  \nRobert Edwin Rouse  \nAbstract  \nThis thesis describes the application of machine learning to hydrology problems in the face of imminent and long term climate change, in particular through the lens of data minimalism. First, we note that with the dawn of the Anthropocene the world’s climate is changing, primarily due to human activity. That change is having and will continue to have profound effects on climatic, geological, and biological systems, including the world’s hydrological systems with which we are concerned. We further note that there are large swathes of the world where there is insufficient data for the development of traditional empirical models, so a new approach is required, one that can generalise from the data we do have.  \nThe opening chapters begin with a treatment of the relevant features and a strategy for compressing the dimensionality to create a model that does not rely on internal measurements from a hydrological system to make predictions about streamflow. We then take this framework and examine the performance of different machine learning model architectures within it and note that whilst some machine learning methods offer greater flexibility, simplicity, and performance, this is subordinate to the feature engineeri","cbCaievoZdoJeYOy","https://ap.wps.com/l/cbCaievoZdoJeYOy","pdf",10669540,1,226,"English","en",105,"# Abstract\n## Data minimalism and climate change context\n## Dimensionality compression and feature engineering\n## Generalisation across hydrological systems\n## Storm prediction and flood risk connection\n## Neural Process extensions for temporal structure\n## Scenario impact modelling with climate projections","[{\"question\":\"What is the thesis’ core idea for flood and storm risk assessment?\",\"answer\":\"It uses machine learning for hydrology and storm prediction while emphasizing data minimalism, so models can work even where traditional empirical data is limited.\"},{\"question\":\"How does the thesis handle prediction when measurements from hydrological systems are limited?\",\"answer\":\"It compresses dimensionality to build models that avoid relying on internal hydrological measurements, then evaluates architectures under that framework with feature engineering as a key driver.\"},{\"question\":\"How is storm prediction linked to flood risk in the research?\",\"answer\":\"The thesis develops a multi-task approach that identifies cyclonic activity, locates it within a domain, and predicts its likely precipitative impact, thereby connecting storm behavior to flood risk.\"}]","Machine Learning Approaches to Assessing Future Flood - 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