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Rainfall runoff modeling by multilayer perceptron neural network for lui river catchment
Nadeem Nawaz1, Sobri Harun2, Arien Heryansyah3, Rawshan Othman4.
Reliable modeling for the rainfall-runoff processes embedded with high complexity and
non-linearity can overcome the problems associated with managing a watershed.
Physically based rainfall-runoff models need many realistic physical components and
parameters which are sometime missing and hard to be estimated. In last decades the
artificial intelligence (AI) has gained much popularity for calibrating the nonlinear
relationships of rainfall–runoff processes. The AI models have the ability to provide direct
relationship of the input to the desired output without considering any internal processes.
This study presents an application of Multilayer Perceptron neural network (MLPNN) for
the continuous and event based rainfall-runoff modeling to evaluate its performance for
a tropical catchment of Lui River in Malaysia. Five years (1999-2013) daily and hourly
rainfall and runoff data was used in this study. Rainfall-runoff processes were also
simulated with a traditionally used statistical modeling technique known as autoregressive
moving average with exogenous inputs (ARMAX). The study has found that
MLPNN model can be used as reliable rainfall-runoff modeling tool in tropical
catchments.
Affiliation:
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- Universiti Teknologi Malaysia, Malaysia
- Koya Technical Institute, Iraq
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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