Next Article in Journal
Organic Pollutants Removal from Olive Mill Wastewater Using Electrocoagulation Process via Central Composite Design (CCD)
Next Article in Special Issue
Synthetic Musk Fragrances in Water Systems and Their Impact on Microbial Communities
Previous Article in Journal
Comparing Experiences of Constitutional Reforms to Enshrine the Right to Water in Brazil, Colombia, and Peru: Opportunities and Limitations
Previous Article in Special Issue
Water Resource Risk Assessment Based on Non-Point Source Pollution
Article

Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning

1
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Devon, Exeter EX4 4QF, UK
2
School of Hydraulic Engineering, Dalian University of Technology, Dalian 116023, China
*
Author to whom correspondence should be addressed.
Academic Editor: Maria Mimikou
Water 2021, 13(24), 3520; https://doi.org/10.3390/w13243520
Received: 10 November 2021 / Revised: 29 November 2021 / Accepted: 7 December 2021 / Published: 9 December 2021
(This article belongs to the Special Issue Environmental Risk Management)
Urban flooding is a devastating natural hazard for cities around the world. Flood risk mapping is a key tool in flood management. However, it is computationally expensive to produce flood risk maps using hydrodynamic models. To this end, this paper investigates the use of machine learning for the assessment of surface water flood risks in urban areas. The factors that are considered in machine learning models include coordinates, elevation, slope gradient, imperviousness, land use, land cover, soil type, substrate, distance to river, distance to road, and normalized difference vegetation index. The machine learning models are tested using the case study of Exeter, UK. The performance of machine learning algorithms, including naïve Bayes, perceptron, artificial neural networks (ANNs), and convolutional neural networks (CNNs), is compared based on a spectrum of indicators, e.g., accuracy, F-beta score, and receiver operating characteristic curve. The results obtained from the case study show that the flood risk maps can be accurately generated by the machine learning models. The performance of models on the 30-year flood event is better than 100-year and 1000-year flood events. The CNNs and ANNs outperform the other machine learning algorithms tested. This study shows that machine learning can help provide rapid flood mapping, and contribute to urban flood risk assessment and management. View Full-Text
Keywords: CNNs; deep learning; flood management; machine learning; surface water flooding CNNs; deep learning; flood management; machine learning; surface water flooding
Show Figures

Figure 1

MDPI and ACS Style

Li, Z.; Liu, H.; Luo, C.; Fu, G. Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning. Water 2021, 13, 3520. https://doi.org/10.3390/w13243520

AMA Style

Li Z, Liu H, Luo C, Fu G. Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning. Water. 2021; 13(24):3520. https://doi.org/10.3390/w13243520

Chicago/Turabian Style

Li, Zhufeng, Haixing Liu, Chunbo Luo, and Guangtao Fu. 2021. "Assessing Surface Water Flood Risks in Urban Areas Using Machine Learning" Water 13, no. 24: 3520. https://doi.org/10.3390/w13243520

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop