DOI:​10.1007/S11269-011-9849-3
Corpus ID: 109183738
Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models
O. Kisi, J. Shiri
Published 2011
Engineering

Water Resources Management
Forecasting precipitation as a major component of the hydrological cycle is of primary importance in water resources engineering, planning and management as well as in scheduling irrigation practices… Expand
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147 Citations
Highly Influential Citations
3
Background Citations
43
Methods Citations
35
Results Citations
1
147 Citations
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Letter to the Editor on “Precipitation Forecasting Using Wavelet-Genetic Programming and Wavelet-Neuro-Fuzzy Conjunction Models” by Ozgur Kisi & Jalal Shiri [Water Resources Management 25 (2011) 3135–3152]
D. Beriro, R. Abrahart, Nick J. Mount, C. P. Nathanail
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2012
TLDR
Kisi and Shiri (2011) combined precipitation records and an integrated wavelet-based series according to lag and comment below on issues regarding their GEP precipitation forecasting solution for the rain gauge station at Izmir. Expand
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Forecasting streamflow by combination of a genetic input selection algorithm and wavelet transforms using ANFIS models
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The results indicate considerable improvements when GA selection and wavelet methods are used in both models, and when the wavelet method is added, the performance of new hybrid models shows noticeable enhancements. Expand
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An effort has been made to develop a conjunction model for forecasting the daily flow of a river in northern Algeria using the time series of runoff using wavelet transformation, data-driven models, and genetic algorithm. Expand
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A new conjunction wavelet-gene expression programming (WGEP) method for predicting air temperature values is proposed in this paper. The conjunction method combines the discrete wavelet and genetic… Expand
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2014
Accurate forecasting of rainfall is important in the effective management of water resources, particularly in arid regions. The wavelet analysis-support vector machine coupled model (WA-SVM) was… Expand
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Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals
Bahram Choubin, S. Khalighi-Sigaroodi, A. Malekian, Ö. Kisi
Mathematics
2016
ABSTRACT Nowadays, mathematical models are widely used to predict climate processes, but little has been done to compare the models. In this study, multiple linear regression (MLR), multi-layer… Expand
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Abstract Evaporation, as a major component of the hydrologic cycle, plays a key role in water resources development and management in arid and semi-arid climatic regions. Although there are empirical… Expand
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Genetic programming (GP) functions as an error updating scheme to complement a rainfall-runoff model, MIKE11/NAM, and it is shown that nondimensionalizing the variables enhances the symbolic regression process significantly. Expand
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The forecasts made by the approach of genetic programming indicated that it can be regarded as a promising tool for future applications to ocean predictions. Expand
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