Urban flood forecasting using a hybrid modeling approach based on a deep learning technique

Hyeontae Moon, Sunkwon Yoon, Youngil Moon

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Climate change is contributing to the increasing frequency and severity of flooding worldwide. Therefore, forecasting and preparing for floods while considering extreme climate conditions are essential for decision-makers to prevent and manage disasters. Although recent studies have demonstrated the potential of long short-term memory (LSTM) models for forecasting rainfall-related runoff, there remains room for improvement due to the lack of observational data. In this study, we developed a flood forecasting model based on a hybrid modeling approach that combined a rainfall-runoff model and a deep learning model. Furthermore, we proposed a method for forecasting flooding time using several representative rainfall variables. The study focused on urban river basins, combined rainfall amounts, duration, and time distribution to create virtual rainfall scenarios. Additionally, the simulated results of the rainfall-runoff model were used as input data to forecast flooding time under extreme and other rainfall conditions. The prediction results achieved high accuracy with a correlation coefficient of >0.9 and a Nash[ndash]Sutcliffe efficiency of >0.8. These results indicated that the proposed method would enable reasonable forecasting of flood occurrences and their timing using only forecasted rainfall information.

Original languageEnglish
Pages (from-to)593-610
Number of pages18
JournalJournal of Hydroinformatics
Issue number2
StatePublished - 1 Mar 2023


  • flood forecasting
  • flooding time
  • long short-term memory neural network
  • storm water management model
  • urban stream


Dive into the research topics of 'Urban flood forecasting using a hybrid modeling approach based on a deep learning technique'. Together they form a unique fingerprint.

Cite this