Afkhamifar, S., Sarraf, A. (2020). Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization. , 12(2), 351-364. doi: 10.22092/ijwmse.2019.126515.1669
Saeid Afkhamifar; Amirpouya Sarraf. "Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization". , 12, 2, 2020, 351-364. doi: 10.22092/ijwmse.2019.126515.1669
Afkhamifar, S., Sarraf, A. (2020). 'Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization', , 12(2), pp. 351-364. doi: 10.22092/ijwmse.2019.126515.1669
Afkhamifar, S., Sarraf, A. Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization. , 2020; 12(2): 351-364. doi: 10.22092/ijwmse.2019.126515.1669

Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization

مهندسی و مدیریت آبخیز
Article 1, Volume 12, Issue 2, July 2020, Pages 351-364    PDF (1.59 M)
Document Type: Research Paper
DOI: 10.22092/ijwmse.2019.126515.1669
Authors
Saeid Afkhamifar1; Amirpouya Sarraf* 2
1M.Sc. Student of Water Resource Management Engineering, Department of Civil Engineering, Roudehen Branch, Islamic Azad university, Roudehen, Iran.
2Department of Civil Engineering, Roudehen Branch, Islamic Azad university, Roudehen, Iran.
Abstract
Today, due to the importance of sustainable groundwater management, groundwater level modeling and forecasting are used to assess and evaluate water resources. The purpose of this study is to evaluate the performance of two models of Extreme Learning Machines (ELM) and Artificial Neural Network (ANN) and the combination of two models with wavelet transmission algorithms (W-ELM and W-ANN), which ultimately to increases the predictive power and optimization of input weights (the weights between the input and hidden layers) of models, Quantum Particle Swarm Optimization algorithm (QPSO) has been used. Also, in this study, the data of Ground Water Level of observation wells (GWL), precipitation (P) and average temperature (T) of Urmia Plain aquifer with a time series of 36 years (1981 – 2017) which were collected on monthly scale, are used. Also, in order to evaluate the performance of models, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used. In this regard, 80% of the data (September 1981 to August 2010) are used for training section and 20% of data (September 2010 to August 2017) used for the test section of models. Based on the results of this study, the hybrid model of W-ELM-QPSO with correlation coefficient (R) 0.991, 0.983 and 0.975, respectively for periods of one, two and three months in the test section, have a better performance than other models and also in addition to predicting power, this model has a high speed in terms of training and testing speed than other models.
Keywords
Artificial neural network; Extreme learning machine; Groundwater level; Quantum particle swarm optimization; Wavelet transforms
References
  1. Acharya, N., N.A. Shrivasta, B.K. Panigrahi and U.C. Mohanty. 2013. Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine. Climate Dynamics, 43: 1303–1310.
  2. Adamowski, J. and H. Fung Chan. 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4): 28-40.
  3. Adamowski, K., A. Prokoph and J. Adamowski. 2009. Development of a new method of wavelet aided trend detection and estimation. Hydrological Processes, 23(18): 2686-2696.
  4. Alizamir, M. and S. Sobhanardakani. 2018. An Artificial Neural Network-Particle Swarm Optimization (ANN-PSO) approach to predict heavy metals contamination in groundwater resources. Jundishapur Journal of Health Sciences, doi: 10.5812/jjhs.67544.
  5. Cheng, C.T., W.J. Niu, Z.K. Feng, J.J. Shen and K.W. Chau. 2015. Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization. Water, 7(8): 4232-4246.
  6. Coulibaly, P., F. Anctil, R. Aravena and B. Bobde. 2001. Artificial neural network modeling of water table depth fluctuations. Water Resources Research, 37(4): 885-896.
  7. Deo, R.C. and M. Şahin. 2014. Application of the extreme learning machine algorithm for the prediction of monthly effective drought index in eastern Australia. Atmospheric Research, 153: 512-525.
  8. Fang, W., L. Juan, Y. Ding and X. Wu. 2010. A Review of quantum-behaved particle swarm optimization. IETE Technical Review, 27(4): 10-26.
  9. Feng, Z.K., W.J. Niu and C.T. Cheng. 2017. Multi-objective quantum-behaved particle swarm optimization for economic environmental hydrothermal energy system scheduling. Energy, 131: 15-26.
  10. Huang, G., G.B. Huang, S. Song and K. You. 2015. Trends in extreme learning machines: a review. Neural Networks, 61: 32-48.
  11. Huang, G.B., L. Chen and C.K. Siew. 2006. Universal approximation using incremental constructive feedforward networks with random hidden nodes. Ieee Transactions on Neural Networks, 17(4): 879-892.
  12. Huang, G.B., H. Zhou, X. Ding and R. Zhang. 2012. Extreme learning machine for regression and multiclass classification. Ieee Transactions on Systems, Man and Cybernetics, 42(2): 513-529.
  13. Huang, G.B., Q.Y. Zhu and C.K. Siew. 2006. Extreme learning machine: theory and applications. Neurocomputing, 70: 489-501.
  14. Jang, J.S.R., C.T. Sun and E. Mizutani. 1997. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. IEEE Transactions on Automatic Control, 42(10): 1482-1484.
  15. Kisi, O., M. Alizamir and M. Zounemat-Kermani. 2017. Modeling groundwater fluctuations by three different evolutionary neural network techniques using hydroclimatic data. Natural Hazards, 87: 367–381.
  16. Li, Z., L. Ye, Y. Zhao, X. Song, J. Teng and J. Jin. 2016. Short-term wind power prediction based on extreme learning machine with error correction. Protection and Control of Modern Power Systems. doi: 10.1186/s41601-016-0016-y.
  17. Liu, T., Y. Ding, X. Cai and X. Zhang. 2017. Extreme learning machine based on particle swarm optimization for estimation of reference evapotranspiration. Proceedings of the 36th Chinese Control Conference, July 26-28, 2017, Dalian, China.
  18. Mokhtari, Z., A. Nazemi and A. Nadiri. 2012. Prediction of groundwater level using artificial neural networks, case study: Shabestar Plain. Journal of Geotechnical Geology, 8(4): 345-353.
  19. Nalley, D., J. Adamowski, B. Khalil and B. Ozga-Zielin. 2013. Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967–2006 using the discrete wavelet transform. Atmospheric Research, 132–133(2013): 375-398.
  20. Pingale, S.M., D. Khare, M.K. Jat and J. Adamowski. 2014. Patial and temporal trends of mean and extreme rainfall and temperature for the 33 urban centers of the arid and semi-arid state of Rajasthan, India. Atmospheric Research, 138: 73-90.
  21. Rajaee, T., V. Nourani, M. Zounemat-Kermani and O. Kisi. 2011. River suspended sediment load prediction: application of ANN and wavelet conjunction model. Journal of Hydrologic Engineering, 16(8): 10-26.
  22. Rajaei, T. and A. Zeinivand. 2014. Modeling of groundwater level using a wavelet-hybrid model-artificial neural network, case study: Sharifabad Plain. Journal of Civil and Environmental Engineering, 44(4): 12-36.
  23. Sezen, C. and T. Partal. 2017. A wavelet transformation-genetic algorithm-artificial neural network combined model for precipitation forecasting. The Eurasia Proceedings of Science, Technology, Engineering and Mathematics (EPSTEM), 1: 372-378.
  24. Shi, Y. and R. Eberhart. 2001. Fuzzy adaptive particle swarm optimization. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, South Korea, doi: 10.1109/CEC. 2001. 934377
  25. SultanAbdulla, S., M. Malek, N. SultanAbdullah, O. Kisi and K. Siah Yap. 2015. Extreme learning machines: a new approach for prediction of reference evapotranspiration. Journal of Hydrology, 527: 184-195.
  26. Taormina, R., K.W. Chau and B. Sivakumar. 2015. Neural network river forecasting through baseflow separation and binary-coded swarm optimization. Journal of Hydrology, 529(3): 1788-1797.
  27. Yang, Z., W. Lu, Y. Long and P. Li. 2009. Application and comparison of two prediction models for groundwater levels, a case study in Western Jilin Province, China. Journal of Arid Environments, 73: 487-492.
  28. Yoon, H., Y. Hyun, K. Ha, K.K. Lee and G.B. Kim. 2016. A method to improve the stability and accuracy of ANN and SVM-based time series models for longterm groundwater level predictions. Computers and Geosciences, 90: 144-155.
  29. Zhu, Q.Y., A. Qin, P. Suganthan and G.B. Hu. 2005. Evolutionary extreme learning machine. Pattern Recognition, 38: 1759-1763.

 

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