A Comparative Analysis of Data-Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates
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- @Article{Zakwan:2021:Complexity,
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author = "Mohammad Zakwan and Majid Niazkar",
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title = "A Comparative Analysis of Data-Driven Empirical and
Artificial Intelligence Models for Estimating
Infiltration Rates",
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journal = "Complexity",
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year = "2021",
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pages = "Article ID 9945218",
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note = "Special Issue: Frontiers in Data-Driven Methods for
Understanding, Prediction, and Control of Complex
Systems 2021",
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keywords = "genetic algorithms, genetic programming",
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bibsource = "OAI-PMH server at oai.repec.org",
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identifier = "RePEc:hin:complx:9945218",
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oai = "oai:RePEc:hin:complx:9945218",
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URL = "https://downloads.hindawi.com/journals/complexity/2021/9945218.pdf",
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DOI = "doi:10.1155/2021/9945218",
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size = "13 pages",
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abstract = "Infiltration is a vital phenomenon in the water cycle,
and consequently, estimation of infiltration rate is
important for many hydrologic studies. In the present
paper, different data-driven models including Multiple
Linear Regression (MLR), Generalized Reduced Gradient
(GRG), two Artificial Intelligence (AI) techniques
(Artificial Neural Network (ANN) and Multigene Genetic
Programming (MGGP)), and the hybrid MGGP-GRG have been
applied to estimate the infiltration rates. The
estimated infiltration rates were compared with those
obtained by empirical infiltration models (Horton{'}s
model, Philip{'}s model, and modified Kostiakov{'}s
model) for the published infiltration data. Among the
conventional models considered, Philip{'}s model
provided the best estimates of infiltration rate. It
was observed that the application of the hybrid
MGGP-GRG model and MGGP improved the estimates of
infiltration rates as compared to conventional
infiltration model, while ANN provided the best
prediction of infiltration rates. To be more specific,
the application of ANN and the hybrid MGGP-GRG reduced
the sum of square of errors by 97.86 percent and 81.53
percent, respectively. Finally, based on the
comparative analysis, implementation of AI-based
models, as a more accurate alternative, is suggested
for estimating infiltration rates in hydrological
models.",
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notes = "Civil Engineering Department, Maulana Azad National
Urdu University, Hyderabad, India",
- }
Genetic Programming entries for
Mohammad Zakwan
Majid Niazkar
Citations