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Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events

1
Wolfson School of Mechanical, Electrical & Manufacturing Engineering, Advanced VR Research Centre, Loughborough University, Loughborough LE11 3TU, UK
2
School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK
3
Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, Devon EX4 4QF, UK
*
Author to whom correspondence should be addressed.
Water 2019, 11(5), 973; https://doi.org/10.3390/w11050973
Received: 12 February 2019 / Revised: 30 April 2019 / Accepted: 6 May 2019 / Published: 9 May 2019
(This article belongs to the Special Issue Flood Risk and Resilience)
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Abstract

The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience. View Full-Text
Keywords: Artificial Intelligence; machine learning; flood; preparedness; resilience; flood resilience Artificial Intelligence; machine learning; flood; preparedness; resilience; flood resilience
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Saravi, S.; Kalawsky, R.; Joannou, D.; Rivas Casado, M.; Fu, G.; Meng, F. Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events. Water 2019, 11, 973.

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