A Hybrid Deep Learning Approach for Flood Prediction: Integrating ICENet’s Spatial-Temporal Learning with DRAW Optimizer’s Adaptive Weighting

S. Sathees Babu (), J. Jeba Sonia (), S. Aarthee (), R. Chithambaramani () and P. Ganeshkumar ()
Additional contact information
S. Sathees Babu: PSNA College of Engineering and Technology, Department of Computer Science and Engineering
J. Jeba Sonia: SRM Institute of Science and Technology, Department of Data Science and Business Systems, School of Computing, Faculty of Engineering and Technology
S. Aarthee: Sastra Deemed University, School of Computing
R. Chithambaramani: RMK Engineering College, Department of Computer Science and Engineering
P. Ganeshkumar: Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Department of Computer Science, College of Computer and Information Sciences

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 14, No 2, 7385-7415

Abstract: Abstract Effective forecasting of the flood depth continues to be the essential element of the proper implementation of disaster management, preservation of infrastructure, and human life. Although accurate simulations are provided by traditional hydrodynamic models, their significant CPU demands usually make them unusable in real-life situations such as in big urban cities that have dense populations and so high spatial resolution is required. In recent years data driven approaches based on deep learning technology have gained traction as attractive alternatives but current approaches are not sufficiently moving towards simulating the complex spatial-temporal interaction of flood dynamics to enable accurate prediction. In order to resolve these identifications, this paper proposes the use of ICENet- a new breed of hybrid deep learning architecture characterized to enhance flood depth prediction. ICENet combines Inception modules and Convolutional Recurrent Units (CRU), thus making it able to recognize multi-scale spatial maps and important temporal correlations associated with floodwater dynamics. This structural composition overcomes important functional challenges that are critical in the existing methods, especially in time based modeling systematic environmental patterns. To maximize learning efficiency and precision further it is optimized by use of DRAW Optimizer which is a specialty-designed, adaptive weighting procedure inspired by rime ice formation and which is used to modify the fusion layers in the network. It guarantees such accurate blending of the different feature streams and boosts convergence. The connection between the hybrid architecture of the model and the task of its functionality consists in the fact that it allows to combine the spatial complexity and the continuity of time and, as a result, the most complete representation of the scene using satellite or aerial images is possible. In comparison, the given ICENet-DRAW framework is compared and shown to perform much better than those available, such as ConvLSTM, U-Net, and traditional CNN-RNN hybrids, with an accuracy of up to 99% in naming prediction on test data. These findings demonstrate the generalized ability of the proposed model in different flood conditions, and thus the proposed model is a scalable model, which provides a good candidate in solving flood depth estimation problem.

Keywords: Flood prediction; Image processing; Deep learning; Optimization; Classification; And object detection (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11269-025-04330-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:waterr:v:39:y:2025:i:14:d:10.1007_s11269-025-04330-3

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11269

DOI: 10.1007/s11269-025-04330-3

Access Statistics for this article

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) is currently edited by G. Tsakiris

More articles in Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA) from Springer, European Water Resources Association (EWRA)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().