A critical review of real-time modelling of flood forecasting in urban drainage systems

Piadeh, Farzad ORCID: https://orcid.org/0000-0002-4958-6968, Behzadian, Kourosh ORCID: https://orcid.org/0000-0002-1459-8408 and Alani, Amir (2022) A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607. p. 127476. ISSN 0022-1694

[thumbnail of Piadeh,_Behzadian,_and_Alani_2022_jhydrol._A_critical_review_of_real-time_modelling_of_flood_forecasting_in_urban_drainage_systems.pdf]
Preview
PDF
Piadeh,_Behzadian,_and_Alani_2022_jhydrol._A_critical_review_of_real-time_modelling_of_flood_forecasting_in_urban_drainage_systems.pdf - Published Version
Available under License Creative Commons Attribution.

Download (6MB) | Preview
[thumbnail of HYDROL43153_R1.pdf]
Preview
PDF
HYDROL43153_R1.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

There has been a strong tendency in recent decades to develop real-time urban flood prediction models for early warning to the public due to a large number of worldwide urban flood occurrences and their disastrous consequences. While a significant breakthrough has been made so far, there are still some potential knowledge gaps that need further investigation. This paper presents a comprehensive review of the current state-of-the-art and future trends of real-time modelling of flood forecasting in urban drainage systems. Findings showed that the combination of various real-time sources of rainfall measurement and the inclusion of other real-time data such as soil moisture, wind flow patterns, evaporation, fluvial flow and infiltration should be more investigated in real-time flood forecasting models. Additionally, artificial intelligence is also present in most of the new RTFF models in UDS and consequently further developments of this technique are expected to appear in future works.

Item Type: Article
Identifier: 10.1016/j.jhydrol.2022.127476
Keywords: Artificial intelligence-based models, Data-driven models, Real-time flood forecasting, Urban drainage systems, Urban flood
Subjects: Construction and engineering > Civil and environmental engineering
Related URLs:
Depositing User: Kourosh Behzadian
Date Deposited: 14 Jan 2022 21:34
Last Modified: 06 Feb 2024 16:08
URI: https://repository.uwl.ac.uk/id/eprint/8575

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item

Menu