VMD-GP: A New Evolutionary Explicit Model for Meteorological Drought Prediction at Ungauged Catchments
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- @Article{danandehmehr:2023:Water,
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author = "Ali {Danandeh Mehr} and Masoud Reihanifar and
Mohammad Mustafa Alee and
Mahammad Amin {Vazifehkhah Ghaffari} and Mir Jafar Sadegh Safari and Babak Mohammadi",
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title = "{VMD-GP:} A New Evolutionary Explicit Model for
Meteorological Drought Prediction at Ungauged
Catchments",
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journal = "Water",
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year = "2023",
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volume = "15",
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number = "15",
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pages = "Article No. 2686",
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keywords = "genetic algorithms, genetic programming, gene
expression programming",
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ISSN = "2073-4441",
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URL = "https://www.mdpi.com/2073-4441/15/15/2686",
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DOI = "doi:10.3390/w15152686",
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abstract = "Meteorological drought is a common hydrological hazard
that affects human life. It is one of the significant
factors leading to water and food scarcity. Early
detection of drought events is necessary for
sustainable agricultural and water resources
management. For the catchments with scarce
meteorological observatory stations, the lack of
observed data is the main leading cause of unfeasible
sustainable watershed management plans. However,
various earth science and environmental databases are
available that can be used for hydrological studies,
even at a catchment scale. In this study, the Global
Drought Monitoring (GDM) data repository that provides
real-time monthly Standardized Precipitation and
Evapotranspiration Index (SPEI) across the globe was
used to develop a new explicit evolutionary model for
SPEI prediction at ungauged catchments. The proposed
model, called VMD-GP, uses an inverse distance
weighting technique to transfer the GDM data to the
desired area. Then, the variational mode decomposition
(VMD), in conjunction with state-of-the-art genetic
programming, is implemented to map the intrinsic mode
functions of the GMD series to the subsequent SPEI
values in the study area. The suggested model was
applied for the month-ahead prediction of the SPEI
series at Erbil, Iraq. The results showed a significant
improvement in the prediction accuracy over the classic
GP and gene expression programming models developed as
the benchmarks.",
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notes = "also known as \cite{w15152686}",
- }
Genetic Programming entries for
Ali Danandeh Mehr
Masoud Reihanifar
Mohammad Mustafa Alee
Mahammad Amin Vazifehkhah Ghaffari
Mir Jafar Sadegh Safari
Babak Mohammadi
Citations