Simulating monthly streamflow using a hybrid feature selection approach integrated with an intelligence model

References

• Adnan, R., et al., 2017. Streamflow forecasting using artificial neural network and support vector machine model. American Scientific Research Journal for Engineering, Technology, and Science, 29 (1), 286–294.
   Google Scholar
• Ahani, A., Shourian, M., and Rahimi Rad, P., 2018. Performance assessment of the linear, nonlinear and nonparametric data driven models in river flow forecasting. Water Resources Management, 32 (2), 383–399. doi:10.1007/s11269-017-1792-5
   Web of Science ®Google Scholar
• Alizadeh, Z., et al., 2018. Assessment of machine learning techniques for monthly flow prediction. Water, 10 (11), 1676. doi:10.3390/w10111676
   Web of Science ®Google Scholar
• Colin Cameron, A. and Windmeijer, F.A.G., 1997. An R-squared measure of goodness of fit for some common nonlinear regression models. Journal of Economics, 77 (2), 329–342. doi:10.1016/S0304-4076(96)01818-0
   Google Scholar
• Diop, L., et al., 2018. The influence of climatic inputs on stream-flow pattern forecasting: case study of Upper Senegal River. Environmental Earth Sciences, 77 (5), 182. doi:10.1007/s12665-018-7376-8
   Web of Science ®Google Scholar
• Ebrahimi, E. and Shourian, M., 2020. River flow prediction using dynamic method for selecting and prioritizing K-nearest neighbors based on data features. Journal of Hydrologic Engineering (ASCE), 25 (5), 04020010. doi:10.1061/(ASCE)HE.1943-5584.0001905
   Web of Science ®Google Scholar
• Engelbrecht, A.P., 2007. Computational intelligence: an introduction. 2nd ed. New York: Wiley.
   Google Scholar
• Galelli, S., et al., 2014. An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental Modeling and Software, 62, 33–51. doi:10.1016/j.envsoft.2014.08.015
   Web of Science ®Google Scholar
• Ghorbani, M.A., et al., 2017. Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey. Stochastic Environment Research and Risk Assessment, 1–15. doi:10.1007/s00477-017-1474-0
   Web of Science ®Google Scholar
• Guajardo, J.A., Weber, R., and Miranda, J., 2010. A model updating strategy for predicting time series with seasonal patterns. Applied Soft Computing, 10 (1), 276–283. doi:10.1016/j.asoc.2009.07.005
   Web of Science ®Google Scholar
• Guyon, I. and Elisseeff, A.E.A., 2003. An introduction to variable and feature selection. Journal of Machine Learning Research, 3 (2), 1157–1182.
   Google Scholar
• Hall, M., 1999. Correlation-based feature selection for machine learning. Dissertation in Univ. Waikato.
   Google Scholar
• Hastie, T., Tibshirani, R., and Friedman, J., 2009. The Elements of Statistical Learning. New York: Springer-Verlag. doi:10.1007/978-0-387-84858-7
   Google Scholar
• Hosseini, S.M. and Mahjouri, N., 2016. Integrating support vector regression and a geomorphologic artificial neural network for daily rainfall–runoff modeling. Applied Soft Computing, 38, 329–345. doi:10.1016/j.asoc.2015.09.049
   Web of Science ®Google Scholar
• Humphrey, G.B., et al., 2016. A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540, 623–640. doi:10.1016/j.jhydrol.2016.06.026
   Web of Science ®Google Scholar
• Jain, A. and Kumar, A.M., 2007. Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 7 (2), 585–592. doi:10.1016/j.asoc.2006.03.002
   Web of Science ®Google Scholar
• James, G., et al., 2013. Springer texts in statistics an introduction to statistical learning. New York: Springer-Verlag N.Y. doi:10.1007/978-1-4614-7138-7
   Google Scholar
• Kalteh, A.M., 2013. Monthly river flow forecasting using artificial neural network and support vector regression models coupled with wavelet transform. Computational Geosciences, 54, 1–8. doi:10.1016/j.cageo.2012.11.015
   Web of Science ®Google Scholar
• Khazaee Poul, A., Shourian, M., and Ebrahimi, H., 2019. A comparative study of MLR, KNN, ANN and ANFIS models with wavelet transform in monthly stream flow prediction. Water Resources Management, 33 (8), 2907–2923. doi:10.1007/s11269-019-02273-0
   Web of Science ®Google Scholar
• Kisi, O., 2015. Streamflow forecasting and estimation using least square support vector regression and adaptive Neuro-Fuzzy embedded fuzzy c-means clustering. Water Resources Management, 29 (14), 5109–5127. doi:10.1007/s11269-015-1107-7
   Web of Science ®Google Scholar
• Kisi, O., et al., 2019. Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models. Hydrological Sciences Journal, 64 (10), 1240–1252. doi:10.1080/02626667.2019.1632460
   Web of Science ®Google Scholar
• Kisi, O. and Cimen, M., 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399 (1–2), 132–140. doi:10.1016/j.jhydrol.2010.12.041
   Web of Science ®Google Scholar
• Lin, J., et al., 2006. Using support vector machines for long-term discharge prediction Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 51 (4), 599–612. doi:10.1623/hysj.51.4.599
   Web of Science ®Google Scholar
• Luo, X., et al., 2019. A hybrid support vector regression framework for streamflow forecast. Journal of Hydrology, 568, 184–193. doi:10.1016/j.jhydrol.2018.10.064
   Web of Science ®Google Scholar
• Maity, R., Bhagwat, P.P., and Bhatnagar, A., 2010. Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrological Processes, 24 (7), 917–923. doi:10.1002/hyp.7535
   Web of Science ®Google Scholar
• Maroufpoor, S., et al., 2019. Soil moisture simulation using hybrid artificial intelligent model: hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. Journal of Hydrology, 575, 544–556. doi:10.1016/j.jhydrol.2019.05.045
   Web of Science ®Google Scholar
• May, R., Dandy, G., and Maier, H., 2011. Review of input variable selection methods for artificial neural networks. Artificial Neural Networks-Methodological Advances and Biomedical Applications. doi:10.5772/16004
   Google Scholar
• Mirjalili, Z., et al., 2018. Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48 (4), 805–820. doi:10.1007/s10489-017-1019-8
   Web of Science ®Google Scholar
• Misra, D., et al., 2009. Application and analysis of support vector machine based simulation for runoff and sediment yield. Biosystems Engineering, 103 (4), 527–535. doi:10.1016/j.biosystemseng.2009.04.017
   Web of Science ®Google Scholar
• Moriasi, D.N., et al., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Transactions ASABE, 50 (3), 885–900. doi:10.13031/2013.23153
   Web of Science ®Google Scholar
• Nash, J.E. and Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I: A discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6
   Google Scholar
• Parsopoulos, K.E. and Vrahatis, M.N., 2002. Particle swarm optimization method in multiobjective problems. Proceedings of 2002 ACM Symposium of Applied Computing, 3, 603.
   Google Scholar
• Quilty, J., et al., 2016. Bootstrap rank-ordered conditional mutual information (broCMI): A nonlinear input variable selection method for water resources modeling. Water Resources Research, 52 (3), 2299–2326. doi:10.1002/2015WR016959
   Web of Science ®Google Scholar
• Raghavendra, S. and Deka, P.C., 2014. Support vector machine applications in the field of hydrology: A review. Applied Soft Computing, 19, 372–386. doi:10.1016/j.asoc.2014.02.002
   Web of Science ®Google Scholar
• Rasouli, K., Hsieh, W.W., and Cannon, A.J., 2012. Daily streamflow forecasting by machine learning methods with weather and climate inputs. Journal of Hydrology, 414–415, 284–293. doi:10.1016/j.jhydrol.2011.10.039
   Web of Science ®Google Scholar
• Saremi, S., Mirjalili, S., and Lewis, A., 2017. Grasshopper Optimisation Algorithm: theory and application. Advances in Engineering and Softwares, 105, 30–47. doi:10.1016/j.advengsoft.2017.01.004
   Web of Science ®Google Scholar
• Schölkopf, B. and Smola, A.J., 2002. Support vector machines and kernel algorithms. AI Magazine, 23 (3), 31–41.
   Web of Science ®Google Scholar
• Sidney Burus, C., Gopinath, R.A., and Guo, H., 1997. Introduction to Wavelets and Wavelet Transforms: A Primer. New Jersey: Pearson.
   Google Scholar
• Ssegane, H., et al., 2012. Advances in variable selection methods I: causal selection methods versus stepwise regression and principal component analysis on data of known and unknown functional relationships. Journal of Hydrology, 438–439, 16–25. doi:10.1016/j.jhydrol.2012.01.008
   Web of Science ®Google Scholar
• Suykens, J., et al., 2003. Advances in learning theory: methods, models and applications. Amsterdam: IOS Press.
   Google Scholar
• Taormina, R. and Chau, K.W., 2015. Data-driven input variable selection for rainfall–runoff modeling using binary-coded particle swarm optimization and extreme learning machines. Journal of Hydrology, 529, 1617–1632. doi:10.1016/j.jhydrol.2015.08.022
   Web of Science ®Google Scholar
• Tibshirani, R., 2011. Regression shrinkage and selection via the Lasso: a retrospective. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 73 (3), 273–282. doi:10.1111/j.1467-9868.2011.00771.x
   Web of Science ®Google Scholar
• Tongal, H. and Booij, M.J., 2018. Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. Journal of Hydrology, 564, 266–282. doi:10.1016/j.jhydrol.2018.07.004
   Web of Science ®Google Scholar
• Van Den Bosch, S., 2017. Automatic feature generation and selection in predictive analytics solutions. Nijmegen, Netherlands: Radboud University.
   Google Scholar
• Wu, C.L., Chau, K.W., and Li, Y.S., 2008. River stage prediction based on a distributed support vector regression. Journal of Hydrology, 358 (1–2), 96–111. doi:10.1016/j.jhydrol.2008.05.028
   Web of Science ®Google Scholar
• Yaseen, Z., et al., 2019a. Implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region: implementation of univariate paradigm for streamflow simulation using hybrid data-driven model: case study in tropical region. IEEE Access, 7, 74471–74481. doi:10.1109/ACCESS.2019.2920916
   Web of Science ®Google Scholar
• Yaseen, Z.M., et al., 2016. Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. Journal of Hydrology, 542, 603–614. doi:10.1016/j.jhydrol.2016.09.035
   Web of Science ®Google Scholar
• Yaseen, Z.M., et al., 2018. Complementary data-intelligence model for river flow simulation. Journal of Hydrology, 567, 180–190. doi:10.1016/j.jhydrol.2018.10.020
   Web of Science ®Google Scholar
• Yaseen, Z.M., et al., 2015. Artificial intelligence based models for stream-flow forecasting: 2000–2015. Journal of Hydrology, 530, 829–844. doi:10.1016/j.jhydrol.2015.10.038
   Web of Science ®Google Scholar
• Yaseen, Z.M., et al., 2019b. An enhanced extreme learning machine model for river flow forecasting: state-of-the-art, practical applications in water resource engineering area and future research direction. Journal of Hydrology, 569, 387–408. doi:10.1016/j.jhydrol.2018.11.069
   Web of Science ®Google Scholar
• Yu, P.S., Chen, S.T., and Chang, I.F., 2006. Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 328 (3–4), 704–716. doi:10.1016/j.jhydrol.2006.01.021
   Web of Science ®Google Scholar
• Zhang, H., et al., 2016. CEREF: A hybrid data-driven model for forecasting annual streamflow from a socio-hydrological system. Journal of Hydrology, 540, 246–256. doi:10.1016/j.jhydrol.2016.06.029
   Web of Science ®Google Scholar