Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS

Accurate forecast of short-term to long-term streamflow prediction is of great importance for water resources management. However, with the advent of novel hybrid machine learning methods, it remains unclear whether these hybrid models can outperform the traditional streamflow forecast models. There...

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Vydáno v:Water resources management Ročník 35; číslo 4; s. 1149 - 1166
Hlavní autoři: Riahi-Madvar, Hossien, Dehghani, Majid, Memarzadeh, Rasoul, Gharabaghi, Bahram
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.03.2021
Springer Nature B.V
Témata:
administrative management
Algorithms
Atmospheric Sciences
Civil Engineering
Damsites
Earth and Environmental Science
Earth Sciences
Environment
equations
Evolutionary algorithms
Evolutionary computation
Forecasting
Genetic algorithms
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrology/Water Resources
Inflow
Iran
Learning algorithms
Machine learning
Mathematical models
model validation
Monte Carlo method
Monte Carlo simulation
Optimization
Particle swarm optimization
prediction
River flow
River forecasting
rivers
Statistical methods
Stream discharge
Stream flow
Streamflow forecasting
Uncertainty
Uncertainty analysis
water
Water resources
Water resources management
Weekly
Iran
Explicit estimation
Karun River
Uncertainty analysis
Streamflow
Prediction horizon
Monte Carlo simulation
ISSN:0920-4741, 1573-1650
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Shrnutí:Accurate forecast of short-term to long-term streamflow prediction is of great importance for water resources management. However, with the advent of novel hybrid machine learning methods, it remains unclear whether these hybrid models can outperform the traditional streamflow forecast models. Therefore, in this study, we trained and tested the performance of several evolutionary algorithms, including Fire-Fly Algorithm(FFA), Genetic Algorithm (GA), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) hybridized with ANFIS. Three forecast horizons, short-term (Daily), mid-term (Weekly and Monthly) and long-term (Annual) with fifteen input-output combinations, a total of 90 models, were developed and tested. A Monte Carlo Simulation (MCS) framework is used for uncertainty analysis. Daily inflow to the Karun III dam, located in the southeast of Iran, for the period of June 2005 to December 2016 were used. Results indicated that: 1) All developed hybrid algorithms significantly outperformed the traditional ANFIS model performance for all prediction horizons. The best hybrid models were ANFIS-GWO1, ANFIS-GWO7 and ANFIS-GWO11 such that the values of R 2 , RMSE, NSE, and RAE were improved by 12%, 10%, 18.5% and 14.3% for the short-term forecasts, 15%, 13%, 20% and 21.1% for the mid-term forecasts, and 10.3%, 7.5%, 10.5% and 14% for the long-term forecasts; 2) Uncertainty analysis indicates that nearly all hybrid models have significantly reduced uncertainty levels compared to the traditional ANFIS model; and 3) A simple explicit equation based on the hybrid ANFIS results was provided for streamflow forecasting, which is a major advantage compared to the classical blackbox machine learning models.
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ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-020-02756-5