A Novel Interannual Rainfall Runoff Equation Derived from Ol’Dekop’s Model Using Artificial Neural Networks Article Swipe
Omar Mimeche
,
Amir Aieb
,
Antonio Liotta
,
Khodir Madani
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22124349
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/s22124349
In water resources management, modeling water balance factors is necessary to control dams, agriculture, irrigation, and also to provide water supply for drinking and industries. Generally, conceptual and physical models present challenges to find more hydro-climatic parameters, which show good performance in the assessment of runoff in different climatic regions. Accordingly, a dynamic and reliable model is proposed to estimate inter-annual rainfall-runoff in five climatic regions of northern Algeria. This is a new improvement of Ol’Dekop’s equation, which models the residual values obtained between real and predicted data using artificial neuron networks (ANNs), namely by ANN1 and ANN2 sub-models. In this work, a set of climatic and geographical variables, obtained from 16 basins, which are inter-annual rainfall (IAR), watershed area (S), and watercourse (WC), were used as input data in the first model. Further, the ANN1 output results and De Martonne index (I) were classified, and were then processed by ANN2 to further increase reliability, and make the model more dynamic and unaffected by the climatic characteristic of the area. The final model proved the best performance in the entire region compared to a set of parametric and non-parametric water balance models used in this study, where the R2Adj obtained from each test gave values between 0.9103 and 0.9923.
Related Topics
Concepts
Environmental science
Surface runoff
Water balance
Watershed
Hydrology (agriculture)
Artificial neural network
Irrigation
Parametric statistics
Reliability (semiconductor)
Water resources
Computer science
Mathematics
Statistics
Ecology
Engineering
Geotechnical engineering
Biology
Power (physics)
Physics
Machine learning
Quantum mechanics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s22124349
- https://www.mdpi.com/1424-8220/22/12/4349/pdf?version=1654840238
- OA Status
- gold
- Cited By
- 1
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282561466
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4282561466Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s22124349Digital Object Identifier
- Title
-
A Novel Interannual Rainfall Runoff Equation Derived from Ol’Dekop’s Model Using Artificial Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-08Full publication date if available
- Authors
-
Omar Mimeche, Amir Aieb, Antonio Liotta, Khodir MadaniList of authors in order
- Landing page
-
https://doi.org/10.3390/s22124349Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/22/12/4349/pdf?version=1654840238Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/22/12/4349/pdf?version=1654840238Direct OA link when available
- Concepts
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Environmental science, Surface runoff, Water balance, Watershed, Hydrology (agriculture), Artificial neural network, Irrigation, Parametric statistics, Reliability (semiconductor), Water resources, Computer science, Mathematics, Statistics, Ecology, Engineering, Geotechnical engineering, Biology, Power (physics), Physics, Machine learning, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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42Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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