hybrid model to improve reference evapotranspiration prediction: Integrating ANN and PSO
DOI:
https://doi.org/10.31185/ejuow.Vol11.Iss3.450Keywords:
Reference evapotranspiration, ANN, PSO, MI, Al KutAbstract
Reference evapotranspiration (ETo), one of the key elements of the hydrological cycle, is crucial for managing irrigation and drainage systems. In order to estimate monthly ETo, this study tested the prediction abilities of a unique hybrid methodology that coupled data pre-processing with a hybrid model composed of an artificial neural network (ANN) and particle swarm optimisation (PSO). In order to train and evaluate the model, monthly meteorological data were collected in Al-Kut City, Iraq, from 1990 to 2020. A range of statistical indicators were used to assess the model, including RMSE, NSE, and R2. The outcomes showed that the model, with a coefficient of determination of 0.93, is effective and has good simulation levels.
References
Adnan, R.M., et al., Estimating reference evapotranspiration using hybrid adaptive fuzzy inferencing coupled with heuristic algorithms. Computers and Electronics in Agriculture, 2021. 191.
Ahmadi, F., et al., Application of an artificial intelligence technique enhanced with intelligent water drops for monthly reference evapotranspiration estimation. Agricultural Water Management, 2021. 244.
Alizamir, M., et al., Modelling reference evapotranspiration by combining neuro-fuzzy and evolutionary strategies. Acta Geophysica, 2020. 68(4): p. 1113-1126.
Chia, M.Y., Y.F. Huang, and C.H. Koo, Swarm-based optimization as stochastic training strategy for estimation of reference evapotranspiration using extreme learning machine. Agricultural Water Management, 2021. 243.
Chia, M.Y., Y.F. Huang, and C.H. Koo, Improving reference evapotranspiration estimation using novel inter-model ensemble approaches. Computers and Electronics in Agriculture, 2021. 187.
Jiao, P. and S.-J. Hu, Optimal Alternative for Quantifying Reference Evapotranspiration in Northern Xinjiang. Water, 2021. 14(1).
Dong, J., et al., Comparison of four bio-inspired algorithms to optimize KNEA for predicting monthly reference evapotranspiration in different climate zones of China. Computers and Electronics in Agriculture, 2021. 186.
El-Kenawy, E.M., et al., Improved weighted ensemble learning for predicting the daily reference evapotranspiration under the semi-arid climate conditions. Environ Sci Pollut Res Int, 2022.
Ashrafzadeh, A., et al., Comparative Study of Time Series Models, Support Vector Machines, and GMDH in Forecasting Long-Term Evapotranspiration Rates in Northern Iran. Journal of Irrigation and Drainage Engineering, 2020. 146(6).
Lu, X., et al., Forecasting Multi-Step Ahead Monthly Reference Evapotranspiration Using Hybrid Extreme Gradient Boosting with GreyWolf Optimization Algorithm. Computer Modeling in Engineering & Sciences, 2020. 125(2): p. 699-723.
Majhi, B. and D. Naidu, Differential evolution based radial basis function neural network model for reference evapotranspiration estimation. SN Applied Sciences, 2021. 3(1).
Khairan, H.E., et al., Parameter Optimisation Based Hybrid Reference Evapotranspiration Prediction Models A Systematic Review of Current Implementations and Future Research Directions. Atmosphere, 2022. 14(1): p. 77.
Eiben, A.E. and C.A. Schippers, On evolutionary exploration and exploitation. Fundamenta Informaticae, 1998. 35(1-4): p. 35-50.
F. Balket, S. and N. M. Asmael, Study the Characteristics of Public Bus Routes in Al Kut City. Journal of Engineering and Sustainable Development, 2021. 25(Special): p. 3-186-3-194.
Al-Abadi, A. and A.H.D. Al-Aboodi, Optimum rain-gauges network design of some cities in Iraq. Journal of Babylon University/Engineering Sciences, 2014. 22(4): p. 946-958.
Roy, D.K., et al., Optimization algorithms as training approaches for prediction of reference evapotranspiration using adaptive neuro fuzzy inference system. Agricultural Water Management, 2021. 255.
Allen, R.G., et al., Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 1998. 300(9): p. D05109.
Alawsi, M.A., et al., Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting. Atmosphere, 2022. 13(9): p. 1436.
Kennedy, J. and R. Eberhart. Particle swarm optimization. in Proceedings of ICNN'95-international conference on neural networks. 1995. IEEE.
Yu, J., et al., A PSO-XGBoost Model for Estimating Daily Reference Evapotranspiration in the Solar Greenhouse. Intelligent Automation & Soft Computing, 2020. 26(5): p. 989-1003.
Nieto, P.G., et al., A new predictive model for the filtered volume and outlet parameters in micro-irrigation sand filters fed with effluents using the hybrid PSO–SVM-based approach. 2016. 125: p. 74-80.
Xu, Y., et al., Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation. 2022. 608: p. 127553.
Tikhamarine, Y., et al., Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches. Hydrological Sciences Journal, 2019. 64(15): p. 1824-1842.
Gocić, M. and M. Arab Amiri, Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags. Water Resources Management, 2021. 35(6): p. 1913-1926.
Maroufpoor, S., O. Bozorg-Haddad, and E. Maroufpoor, Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm. Journal of Hydrology, 2020. 588.
Stergiou, N., Nonlinear analysis for human movement variability. 2018: CRC press.
Dawson, C.W., R.J. Abrahart, and L.M. See, HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environmental Modelling Software, 2007. 22(7): p. 1034-1052.
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