Articles | Volume 12, issue 1
https://doi.org/10.5194/hess-12-123-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
https://doi.org/10.5194/hess-12-123-2008
© Author(s) 2008. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Comparison of Artificial Intelligence Techniques for river flow forecasting
M. Firat
Research Assistant (PhD), Pamukkale University Civil Engineering Department, Denizli, Turkey
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- Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin J. Noymanee et al. 10.1016/j.procs.2017.11.187
- Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines M. Seyam et al. 10.1051/matecconf/201711101007
- Investigating the effect of previous time on modeling stage–discharge curve at hydrometric stations using GEP and NN models F. Harasami et al. 10.1080/09715010.2017.1308278
55 citations as recorded by crossref.
- A hybrid model of self organizing maps and least square support vector machine for river flow forecasting S. Ismail et al. 10.5194/hess-16-4417-2012
- Data- and Model-Based Discharge Hindcasting over a Subtropical River Basin K. Billah et al. 10.3390/w13182560
- Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al. 10.5194/hess-15-185-2011
- Modeling the effect of meteorological variables on streamflow estimation: application of data mining techniques in mixed rainfall–snowmelt regime Munzur River, Türkiye O. Katipoğlu 10.1007/s11356-023-29220-2
- Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting M. Ghorbani et al. 10.1016/j.jhydrol.2018.04.054
- Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq Z. Yaseen et al. 10.1016/j.jhydrol.2016.09.035
- Computation of Evapotranspiration with Artificial Intelligence for Precision Water Resource Management H. Afzaal et al. 10.3390/app10051621
- Comparison of data-driven modelling techniques for river flow forecasting S. Londhe & S. Charhate 10.1080/02626667.2010.512867
- River flow time series using least squares support vector machines R. Samsudin et al. 10.5194/hess-15-1835-2011
- Modelling hybrid and backpropagation adaptive neuro-fuzzy inference systems for flood forecasting R. Tabbussum & A. Dar 10.1007/s11069-021-04694-w
- Application of Artificial Neural Networks, Support Vector Machine and Multiple Model-ANN to Sediment Yield Prediction S. Meshram et al. 10.1007/s11269-020-02672-8
- Stochastic model stationarization by eliminating the periodic term and its effect on time series prediction H. Moeeni et al. 10.1016/j.jhydrol.2017.02.012
- Predictive accuracy of backpropagation neural network methodology in evapotranspiration forecasting in Dédougou region, western Burkina Faso S. TRAORE et al. 10.1007/s12040-013-0398-4
- Toward Automatic Time-Series Forecasting Using Neural Networks . Weizhong Yan 10.1109/TNNLS.2012.2198074
- Coupling ANFIS with ant colony optimization (ACO) algorithm for 1-, 2-, and 3-days ahead forecasting of daily streamflow, a case study in Poland P. Aghelpour et al. 10.1007/s11356-023-26239-3
- Watershed rainfall forecasting using neuro-fuzzy networks with the assimilation of multi-sensor information F. Chang et al. 10.1016/j.jhydrol.2013.11.011
- Real-time streamflow forecasting: AI vs. Hydrologic insights W. Krajewski et al. 10.1016/j.hydroa.2021.100110
- Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia M. Ghorbani et al. 10.1007/s00500-019-04648-2
- Determination of Deep Learning Model and Optimum Length of Training Data in the River with Large Fluctuations in Flow Rates K. Park et al. 10.3390/w12123537
- Monthly reservoir inflow forecasting using a new hybrid SARIMA genetic programming approach H. Moeeni et al. 10.1007/s12040-017-0798-y
- Hourly runoff forecasting for flood risk management: Application of various computational intelligence models H. Badrzadeh et al. 10.1016/j.jhydrol.2015.07.057
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- Integrated SARIMA with Neuro-Fuzzy Systems and Neural Networks for Monthly Inflow Prediction H. Moeeni et al. 10.1007/s11269-017-1632-7
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- Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data V. Jothiprakash & R. Magar 10.1016/j.jhydrol.2012.04.045
- Prediction of river flow using hybrid neuro-fuzzy models A. Azad et al. 10.1007/s12517-018-4079-0
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- Performance evaluation of artificial intelligence paradigms—artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction R. Tabbussum & A. Dar 10.1007/s11356-021-12410-1
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- Evaluation of stochastic and artificial intelligence models in modeling and predicting of river daily flow time series P. Aghelpour & V. Varshavian 10.1007/s00477-019-01761-4
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- A hybrid forecasting model based on the group method of data handling and wavelet decomposition for monthly rivers streamflow data sets W. Shaikh et al. 10.1515/phys-2022-0066
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- Towards Safer Data-Driven Forecasting of Extreme Streamflows J. Matos et al. 10.1007/s11269-017-1834-z
- Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
- Flow forecasting models using hydrologic and hydrometric data M. Alizadeh et al. 10.1680/jwama.14.00146
- Hydrological and Meteorological Drought Forecasting for the Yesilirmak River Basin, Turkey A. BOUSTANİ HEZARANİ et al. 10.51764/smutgd.993792
- Linear genetic programming for time-series modelling of daily flow rate A. Guven 10.1007/s12040-009-0022-9
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- Prediction of Stream Flow in Humid Tropical Rivers by Support Vector Machines M. Seyam et al. 10.1051/matecconf/201711101007
- Investigating the effect of previous time on modeling stage–discharge curve at hydrometric stations using GEP and NN models F. Harasami et al. 10.1080/09715010.2017.1308278
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