AI-driven predictive modelling of flow over sloping porous weirs for sustainable hydraulic design
Mohd. Danish, Mujib. A. Ansari, Ajmal. Hussain, Sanan. H. Khan
Abstract. Porous broad-crested weirs (PBCWs) with sloping crests are gaining recognition as sustainable and hydraulically efficient alternatives to conventional impermeable weirs. Their porous structure enhances energy dissipation and discharge capacity, while offering ecological and economic benefits. However, accurately predicting the discharge coefficient (Cd) of such weirs remains a challenge due to the nonlinear interactions between hydraulic and structural parameters. This study investigates the potential of artificial intelligence (AI) techniques, namely Artificial Neural Networks (ANNs) and the Group Method of Data Handling (GMDH), to model and predict Cd for sloping porous weirs under free-flow conditions. A dataset obtained from the literature was used for training, validation, and testing of both models. The ANN model, trained with a backpropagation algorithm using the Levenberg–Marquardt optimization method, achieved superior predictive performance (MSE = 5.13 × 10⁻⁵, RMSE = 0.007, R² = 0.994) compared to the GMDH model (MSE = 1.63 × 10-4, RMSE = 0.013, R² = 0.982). A comparative analysis with empirical and GEP-based equations from the literature further confirmed the robustness of the ANN model. The results highlight the potential of AI-driven approaches, particularly ANN, as reliable predictive tools for hydraulic design of porous weirs, contributing to environmentally adaptive and sustainable water infrastructure.
Keywords
Gabion Weir, Artificial Neural Network, Levenberg-Marquardt ALGORITHM, Sustainable Structure, AI MODELLING
Published online 6/20/2026, 9 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mohd. Danish, Mujib. A. Ansari, Ajmal. Hussain, Sanan. H. Khan, AI-driven predictive modelling of flow over sloping porous weirs for sustainable hydraulic design, Materials Research Proceedings, Vol. 67, pp 456-464, 2026
DOI: https://doi.org/10.21741/9781644904176-61
The article was published as article 61 of the book Climate Action and Sustainability
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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