TY - JOUR
T1 - A critical review of real-time modelling of flood forecasting in urban drainage systems
AU - Piadeh, Farzad
AU - Behzadian, Kourosh
AU - Alani, Amir M.
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Artificial intelligence-based models
KW - Data-driven models
KW - Real-time flood forecasting
KW - Urban drainage systems
KW - Urban flood
UR - http://www.scopus.com/inward/record.url?scp=85123799772&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2022.127476
DO - 10.1016/j.jhydrol.2022.127476
M3 - Review article
AN - SCOPUS:85123799772
SN - 0022-1694
VL - 607
JO - Journal of Hydrology
JF - Journal of Hydrology
M1 - 127476
ER -