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

• Published in: Natural hazards and earth system sciences Vol. 20; no. 4; pp. 1149 - 1161
• Main Authors: Wagenaar, Dennis, Curran, Alex, Balbi, Mariano, Bhardwaj, Alok, Soden, Robert, Hartato, Emir, Mestav Sarica, Gizem, Ruangpan, Laddaporn, Molinario, Giuseppe, Lallemant, David
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 29.04.2020; Copernicus Publications
• Subjects: Algorithms; Computation; Data; Data collection; Data mining; Digital media; Disaster planning; Emergency preparedness; Emergency response; Environmental risk; Ethical standards; Ethics; Flood damage; Flood relief; Flood risk; Floods; Flow velocity; Hydrodynamics; International organizations; Learning algorithms; Machine learning; Mitigation; Neural networks; Observation methods; Power (Philosophy); Prejudice; Recovery plans; Remote sensing; Risk assessment; Rural areas; Social media; Storm damage; Time; Trends
• ISSN: 1684-9981; 1561-8633; 1684-9981
• DOI: 10.5194/nhess-20-1149-2020

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 keep becoming available, computing power keeps improving and machine learning algorithms keep improving as well. 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 will 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 and 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 components, machine learning may not always 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 to avoid misuse. This paper presents some of the current developments on the application of machine learning in this field and highlights some key needs and challenges.