ORCID

0000-0002-8822-7167

Abstract

This study has developed forecast models for groundwater flooding in lowland karst region of south Galway (Ireland). It employed neural network models incorporating Bayesian regularization and Scaled Conjugate Gradient training algorithms for model training and optimization. Training datasets include years of field data and outputs from a calibrated hydraulic/hydrological karst model. The Bayesian model achieves Nash-Sutcliffe Efficiency (NSE) of 0.95 up to 45 days ahead, whilst the Scaled Conjugate Gradient models outperform this, maintaining NSE of 0.98 up to 20 days and 0.95 up to 60 days ahead, with reduced training time compared to Bayesian models. Both models exhibit high performance with a Coefficient of Correlation (r) value of 0.98 up to 60 days ahead and Kling Gupta Efficiency of 0.96 up to 15 days ahead. The research shows that integrating diverse data sources and using both daily and hourly models improve such a flood warning system's resilience and reliability.

Disciplines

Civil and Environmental Engineering

DOI

10.1016/j.envsoft.2025.106591

Full Publication Date

30-6-2025

Publication Details

Environmental Modelling & Software

Publisher

Elsevier

Funder Name 1

The Environmental Protection Agency

Award Number 1

EPA ref. EPA-2020-W-CD-3

Resource Type

journal article

Resource Version

http://purl.org/coar/version/c_970fb48d4fbd8a85

Access Rights

open access

Open Access Route

Gold Open Access

License Condition

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Alternative Identifier

https://www.sciencedirect.com/science/article/pii/S1364815225002750#ack0010

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