A Data-Driven Multi-Step Flood Inundation Forecast System

Felix Schmid and Jorge Leandro ()
Additional contact information
Felix Schmid: Chair of Hydromechanics and Hydraulic Engineering, Research Institute Water and Environment, University of Siegen, 57076 Siegen, Germany
Jorge Leandro: Chair of Hydromechanics and Hydraulic Engineering, Research Institute Water and Environment, University of Siegen, 57076 Siegen, Germany

Forecasting, 2024, vol. 6, issue 3, 1-21

Abstract: Inundation maps that show water depths that occur in the event of a flood are essential for protection. Especially information on timings is crucial. Creating a dynamic inundation map with depth data in temporal resolution is a major challenge and is not possible with physical models, as these are too slow for real-time predictions. To provide a dynamic inundation map in real-time, we developed a data-driven multi-step inundation forecast system for fluvial flood events. The forecast system is based on a convolutional neural network (CNN), feature-informed dense layers, and a recursive connection from the predicted inundation at timestep t as a new input for timestep t + 1. The forecast system takes a hydrograph as input, cuts it at desired timesteps (t), and outputs the respective inundation for each timestep, concluding in a dynamic inundation map with a temporal resolution (t). The prediction shows a Critical Success Index (CSI) of over 90%, an average Root Mean Square Error (RMSE) of 0.07, 0.12, and 0.15 for the next 6 h, 12 h, and 24 h, respectively, and an individual RMSE value below 0.3 m, for all test datasets when compared with the results from a physically based model.

Keywords: real-time forecasting; urban flooding; artificial neural network; convolutional neural network; temporal and spatial distribution (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2571-9394/6/3/39/pdf (application/pdf)
https://www.mdpi.com/2571-9394/6/3/39/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jforec:v:6:y:2024:i:3:p:39-781:d:1477866

Access Statistics for this article

Forecasting is currently edited by Ms. Joss Chen

More articles in Forecasting from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().