Monthly streamflow forecasting using continuous wavelet and multi-gene genetic programming combination
Created by W.Langdon from
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- @Article{HADI:2018:JH,
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author = "Sinan Jasim Hadi and Mustafa Tombul",
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title = "Monthly streamflow forecasting using continuous
wavelet and multi-gene genetic programming
combination",
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journal = "Journal of Hydrology",
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volume = "561",
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pages = "674--687",
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year = "2018",
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keywords = "genetic algorithms, genetic programming, Wavelet
coherence transformation, Continuous wavelet
transformation, Artificial neural network, Data-driven
models",
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ISSN = "0022-1694",
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DOI = "doi:10.1016/j.jhydrol.2018.04.036",
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URL = "http://www.sciencedirect.com/science/article/pii/S0022169418302890",
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abstract = "Streamflow is an essential component of the hydrologic
cycle in the regional and global scale and the main
source of fresh water supply. It is highly associated
with natural disasters, such as droughts and floods.
Therefore, accurate streamflow forecasting is
essential. Forecasting streamflow in general and
monthly streamflow in particular is a complex process
that cannot be handled by data-driven models (DDMs)
only and requires pre-processing. Wavelet
transformation is a pre-processing technique; however,
application of continuous wavelet transformation (CWT)
produces many scales that cause deterioration in the
performance of any DDM because of the high number of
redundant variables. This study proposes multigene
genetic programming (MGGP) as a selection tool. After
the CWT analysis, it selects important scales to be
imposed into the artificial neural network (ANN). A
basin located in the southeast of Turkey is selected as
case study to prove the forecasting ability of the
proposed model. One month ahead downstream flow is used
as output, and downstream flow, upstream, rainfall,
temperature, and potential evapotranspiration with
associated lags are used as inputs. Before modeling,
wavelet coherence transformation (WCT) analysis was
conducted to analyze the relationship between variables
in the time-frequency domain. Several combinations were
developed to investigate the effect of the variables on
streamflow forecasting. The results indicated a high
localized correlation between the streamflow and other
variables, especially the upstream. In the models of
the standalone layout where the data were entered to
ANN and MGGP without CWT, the performance is found
poor. In the best-scale layout, where the best scale of
the CWT identified as the highest correlated scale is
chosen and enters to ANN and MGGP, the performance
increased slightly. Using the proposed model, the
performance improved dramatically particularly in
forecasting the peak values because of the inclusion of
several scales in which seasonality and irregularity
can be captured. Using hydrological and meteorological
variables also improved the ability to forecast the
streamflow",
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keywords = "genetic algorithms, genetic programming, Wavelet
coherence transformation, Continuous wavelet
transformation, Artificial neural network, Data-driven
models",
- }
Genetic Programming entries for
Sinan Jasim Hadi
Mustafa Tombul
Citations