Superiority of artificial neural networks over conventional hydrological models in simulating urban catchment runoff Article Swipe
Harshanth Balacumaresan
,
Monzur Alam Imteaz
,
Iqbal Hossain
,
Md. Abdul Aziz
,
Tanveer Choudhury
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2166/hydro.2024.036
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.2166/hydro.2024.036
The synergistic impacts of climate change and urbanisation have amplified the recurrence and austerity of intense rainfall events, exacerbating persistent flooding risk in urban environments. The intricate topography and inherent non-linearity of urban hydrological processes limit the predictive accuracy of conventional models, leading to significant discrepancies in flow estimation. Recent advancements in artificial neural network (ANNs) have demonstrated remarkable progress in mitigating most limitations, specifically in simulating complex, non-linear relationships, without an intricate comprehension of the underlying physical processes. This paper proposes a deep learning ANN-based flow estimation model for enhanced precision simulation of streamflow in urban catchments, with the research's distinctive contribution involving rigorous comparative evaluation of the developed model against the established Australian hydrological model, RORB. Gardiners Creek catchment, an urban catchment situated in East Melbourne was designated as the study area, with the model being calibrated upon historical storm incidences. The findings reveal that the ANN model substantially outperforms RORB, as evidenced by superior correlation, prediction efficiency, and lower generalisation error. This underscores the ANN's adeptness in accurately replicating non-linear-catchment responses to storm events, marking a substantial advancement over conventional modelling practices and indicating its transformative potential for enhancing flood prediction precision and revolutionising current estimation practices.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2166/hydro.2024.036
- OA Status
- diamond
- Cited By
- 7
- References
- 54
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4402203082Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2166/hydro.2024.036Digital Object Identifier
- Title
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Superiority of artificial neural networks over conventional hydrological models in simulating urban catchment runoffWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-01Full publication date if available
- Authors
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Harshanth Balacumaresan, Monzur Alam Imteaz, Iqbal Hossain, Md. Abdul Aziz, Tanveer ChoudhuryList of authors in order
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https://doi.org/10.2166/hydro.2024.036Publisher landing page
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.2166/hydro.2024.036Direct OA link when available
- Concepts
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Surface runoff, Environmental science, Drainage basin, Artificial neural network, Hydrology (agriculture), Computer science, Geology, Geography, Artificial intelligence, Geotechnical engineering, Ecology, Cartography, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
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2025: 7Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1964425467, https://openalex.org/W2341928794, https://openalex.org/W2913113592, https://openalex.org/W4386333279, https://openalex.org/W4298002331, https://openalex.org/W2414596991, https://openalex.org/W4393411909, https://openalex.org/W2130089609, https://openalex.org/W4210263696, https://openalex.org/W4387024355, https://openalex.org/W2640557513, https://openalex.org/W2887228514, https://openalex.org/W2477041072, https://openalex.org/W2081997761, https://openalex.org/W3172783927, https://openalex.org/W2997655715, https://openalex.org/W4309921291, https://openalex.org/W4200172698, https://openalex.org/W2441507532, https://openalex.org/W4390543976, https://openalex.org/W2248263087, https://openalex.org/W2792906888, https://openalex.org/W44564111, https://openalex.org/W3158464467, https://openalex.org/W4380485481, https://openalex.org/W3082384511, https://openalex.org/W4285042424, https://openalex.org/W3122286376, https://openalex.org/W3087236291, https://openalex.org/W3085509852, https://openalex.org/W4231875629, https://openalex.org/W2898030472, https://openalex.org/W2752634495, https://openalex.org/W4293801764, https://openalex.org/W2899023746, https://openalex.org/W4384929076, https://openalex.org/W2971675004, https://openalex.org/W2907891425, https://openalex.org/W2170396766, https://openalex.org/W2049604804, https://openalex.org/W4317883043, https://openalex.org/W2034247640, https://openalex.org/W2012798012, https://openalex.org/W4386806504, https://openalex.org/W4210966436, https://openalex.org/W2918277459, https://openalex.org/W2593388048, https://openalex.org/W1608317761, https://openalex.org/W3014375006, https://openalex.org/W4383342600, https://openalex.org/W3034135156, https://openalex.org/W4366168939, https://openalex.org/W3092628847, https://openalex.org/W4306970960 |
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