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Discussion papers
https://doi.org/10.5194/nhess-2019-341
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-341
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: invited perspectives 16 Oct 2019

Submitted as: invited perspectives | 16 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

Invited perspectives: How machine learning will change flood risk and impact assessment

Dennis Wagenaar1,2, Alex Curran1,3, Mariano Balbi4, Alok Bhardwaj5, Robert Soden6,7,8, Emir Hartato9, Gizem Mestav Sarica10, Laddaporn Ruangpan11, Giuseppe Molinario7, and David Lallemant5,8 Dennis Wagenaar et al.
  • 1Deltares, Delft, the Netherlands
  • 2VU University, Amsterdam, the Netherlands
  • 3Delft University of Technology, Delft, the Netherlands
  • 4University of Buenos Aires, Buenos Aires, Argentina
  • 5Earth Observatory of Singapore, Nanyang Technological University, Singapore
  • 6Columbia University, New York City, New York, United States of America
  • 7GFDRR, World Bank Group, Washington D.C., United States of America
  • 8Co-Risk Labs, Oakland, California, United States of America
  • 9Planet, San Francisco, United States of America
  • 10Institute of Catastrophe Risk Management, Nanyang Technological University, Singapore
  • 11IHE Delft Institute for water education, Delft, the Netherlands

Abstract. Increasing amounts of data, together with more computing power and better machine learning algorithms to analyse the data are causing changes in almost every aspect of our lives. This trend is expected to continue as more data becomes available, computing power increases and machine learning algorithms improve. Flood risk and impact assessments are also being influenced by this trend, particularly in areas such as the development of mitigation measures, emergency response preparation, and flood recovery planning. Machine learning methods have the potential to improve accuracy as well as reduce calculating time and model development cost. It is expected that in the future more applications become feasible and many process models and traditional observation methods will be replaced by machine learning. Examples of this include the use of machine learning on remote sensing data to estimate exposure or on social media data to improve flood response. Some improvements may require new data collection efforts, such as for the modelling of flood damages or defence failures. In other fields, machine learning may not be suitable or should be applied complementary to process models, for example in hydrodynamic applications. Overall, machine learning is likely to drastically improve future flood risk and impact assessments, but issues such as applicability, bias and ethics must be considered carefully. This paper presents some of the current developments on the application of machine learning for flood risk and impact assessment, and highlights some key needs and challenges.

Dennis Wagenaar et al.
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Status: open (until 11 Dec 2019)
Status: open (until 11 Dec 2019)
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Short summary
This invited perspective paper adresses how machine learning may change flood risk and impact assessments. It goes through different modelling components and provide an analysis of how current assessments are done without machine learning, current applications of machine learning and potential future improvements. It is based on a 2 week long intensive collaboration among experts from around the world during the Understanding Risk Field lab on urban flooding in June 2019.
This invited perspective paper adresses how machine learning may change flood risk and impact...
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