Robustness lake water level prediction using the search heuristic-based artificial intelligence methods

References

• Ali Ghorbani, M., Khatibi, R., Aytek, A., Makarynskyy, O., and Shiri, J. (2010). “Sea water level forecasting using genetic programming and comparing the performance with Artificial Neural Networks.” Comput. Geosci., 36(5), 620–627.10.1016/j.cageo.2009.09.014
   Web of Science ®Google Scholar
• Ashofteh, P.-S., Haddad, O.B., and Loáiciga, H.A. (2015). “Evaluation of climatic-change impacts on multiobjective reservoir operation with multiobjective genetic programming.” J. Water Resour. Plann. Manage., 141(11), 04015030.10.1061/(ASCE)WR.1943-5452.0000540
   Web of Science ®Google Scholar
• Cramer, S., Kampouridis, M., Freitas, A.A., and Alexandridis, A. (2015). “Predicting rainfall in the context of rainfall derivatives using genetic programming.” Proc., Computational Intelligence, 2015 IEEE Symposium Series on, IEEE, 711–718.10.1109/SSCI.2015.108
   Google Scholar
• Dilini, W., Attygalle, D.T., Liyanage, L., and Nandalal, K. (2016). “Ensemble forecast for monthly reservoir inflow: a dynamic neural network approach.” Proc., Proceedings of the 4th Annual International Conference on Operations Research and Statistics (ORS 2016), 18–19 January 2016, Singapor, 84–90.
   Google Scholar
• Emiroglu, M.E., Bilhan, O., and Kisi, O. (2011). “Neural networks for estimation of discharge capacity of triangular labyrinth side-weir located on a straight channel.” Expert. Sys. Appl., 38(1), 867–874.10.1016/j.eswa.2010.07.058
   Web of Science ®Google Scholar
• Formentin, S.M., Zanuttigh, B., and van der Meer, J.W. (2017). “A neural network tool for predicting wave reflection, overtopping and transmission.” Coastal Eng. J., 59(01), 1750006.10.1142/S0578563417500061
   Google Scholar
• Garg, V. (2014). “Modeling catchment sediment yield: a genetic programming approach.” Nat. Hazards, 70(1), 39–50.10.1007/s11069-011-0014-3
   Web of Science ®Google Scholar
• Gaur, S., and Deo, M.C. (2008). “Real-time wave forecasting using genetic programming.” Ocean Eng., 35(11–12), 1166–1172.10.1016/j.oceaneng.2008.04.007
   Web of Science ®Google Scholar
• Gopinath, D.I., and Dwarakish, G. (2015). “Wave prediction using neural networks at New Mangalore Port along west coast of India.” Aquat. Procedia, 4, 143–150.10.1016/j.aqpro.2015.02.020
   Google Scholar
• Güllü, H. (2013). “On the prediction of shear wave velocity at local site of strong ground motion stations: an application using artificial intelligence.” Bull. Earthquake Eng., 11(4), 969–997.10.1007/s10518-013-9425-8
   Web of Science ®Google Scholar
• Guven, A., and Kişi, Ö. (2011). “Daily pan evaporation modeling using linear genetic programming technique.” Irrig. Sci., 29(2), 135–145.10.1007/s00271-010-0225-5
   Web of Science ®Google Scholar
• Holland, J.H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence, U Michigan Press, Cambridge, MA.
   Google Scholar
• Jovic, S., Jovic, S., Vasic, P.S., Vasic, P.S., Jakšic, T.R., and Jakšic, T.R. (2017). “Sensorless estimation of lake level by soft computing approach.” Sens. Rev., 38(1), doi:10.1108/SR-07-2017-0138.
   Web of Science ®Google Scholar
• Kambekar, A.R., and Deo, M.C. (2012). “Wave prediction using genetic programming and model trees.” J. Coastal Res., 279(1), 43–50.10.2112/JCOASTRES-D-10-00052.1
   Google Scholar
• Kaur, T., Kumar, S., and Segal, R. (2016). “Application of artificial neural network for short term wind speed forecasting.” Proc., Power and Energy Systems: Towards Sustainable Energy (PESTSE), 2016 Biennial International Conference on, IEEE, 1–5.
   Google Scholar
• Kisi, O., Dailr, A.H., Cimen, M., and Shiri, J. (2012). “Suspended sediment modeling using genetic programming and soft computing techniques.” J. Hydrol., 450–451, 48–58.10.1016/j.jhydrol.2012.05.031
   Web of Science ®Google Scholar
• Kisi, O., Shiri, J., and Nikoofar, B. (2012). “Forecasting daily lake levels using artificial intelligence approaches.” Comput. Geosci., 41, 169–180.10.1016/j.cageo.2011.08.027
   Web of Science ®Google Scholar
• Koza, J.R. (1992). Genetic programming: on the programming of computers by means of natural selection, MIT Press, Cambridge, MA.
   Google Scholar
• Kumar, B., Jha, A., Deshpande, V., and Sreenivasulu, G. (2014). “Regression model for sediment transport problems using multi-gene symbolic genetic programming.” Comput. Electron. Agric., 103, 82–90.10.1016/j.compag.2014.02.010
   Web of Science ®Google Scholar
• Kurniawan, A., Ooi, S.K., and Babovic, V. (2014). “Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters.” Comput. Geosci., 72, 94–104.10.1016/j.cageo.2014.07.007
   Web of Science ®Google Scholar
• Levenberg, K. (1944). “A method for the solution of certain non-linear problems in least squares.” Q. Appl. Math., 2, 164–168.10.1090/qam/1944-02-02
   Google Scholar
• Mehr, A.D., and Nourani, V. (2017). “A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling.” Environ. Modell. Software, 92, 239–251.10.1016/j.envsoft.2017.03.004
   Web of Science ®Google Scholar
• Moustris, K., Zafirakis, D., Alamo, D., Medina, R.N., and Kaldellis, J. (2017). “24-h ahead wind speed prediction for the optimum operation of hybrid power stations with the use of artificial neural networks.” Perspectives on Atmospheric Sciences, T. Karacostas, A. Bais, and P.T. Nastos, eds., Springer, Thessaloniki, 409–414.
   Google Scholar
• Nitsure, S.P., Londhe, S.N., and Khare, K.C. (2014). “Prediction of sea water levels using wind information and soft computing techniques.” Appl. Ocean Res., 47, 344–351.10.1016/j.apor.2014.07.003
   Web of Science ®Google Scholar
• Partal, T. (2017). “Wavelet regression and wavelet neural network models for forecasting monthly streamflow.” J. Water Clim. Change, 8(1), 48–61.10.2166/wcc.2016.091
   Google Scholar
• Patil, K., and Deo, M.C. (2017). “Prediction of daily sea surface temperature using efficient neural networks.” Ocean Dyn., 67(3–4), 357–368.10.1007/s10236-017-1032-9
   Web of Science ®Google Scholar
• Pierini, J.O., Gomez, E.A., and Telesca, L. (2012). “Prediction of water flows in Colorado River, Argentina.” Lat. Am. J. Aquat. Res., 40(4), 872–880.10.3856/vol40-issue4-fulltext-5
   Web of Science ®Google Scholar
• Puscasu, R. M. (2014). “Integration of artificial neural networks into operational ocean wave prediction models for fast and accurate emulation of exact nonlinear interactions.” Proc., 14th Annual International Conference on Computational Science, ICCS 2014, Elsevier, 1156–1170.
   Google Scholar
• Rani, N.S., Satyanarayana, A., and Bhaskaran, P.K. (2017). “Assessment of climatological trends of sea level over the Indian coast using artificial neural network and wavelet techniques.” Pure Appl. Geophys., 174(4), 1527–1546.10.1007/s00024-017-1501-6
   Web of Science ®Google Scholar
• Rodríguez-Vázquez, K., Arganis-Juárez, M.L., Cruickshank-Villanueva, C., and Domínguez-Mora, R. (2012). “Rainfall-runoff modelling using genetic programming.” J. Hydroinf., 14(1), 108–121.10.2166/hydro.2011.105
   Web of Science ®Google Scholar
• Shamaei, E., and Kaedi, M. (2016). “Suspended sediment concentration estimation by stacking the genetic programming and neuro-fuzzy predictions.” Appl. Soft Comput., 45, 187–196.10.1016/j.asoc.2016.03.009
   Web of Science ®Google Scholar
• Shiri, J., and Kişi, Ö. (2011). “Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations.” Comput. Geosci., 37(10), 1692–1701.10.1016/j.cageo.2010.11.010
   Web of Science ®Google Scholar
• Smith, M. (1993). Neural networks for statistical modeling, Thomson Learning, New York, NY.
   Google Scholar
• Song, S. S., and Li, R. (2014). “Method of wind power prediction based on ANN and improvements.” Proc., 2013 2nd International Conference on Sustainable Energy and Environmental Engineering, ICSEEE 2013, 143–146.
   Google Scholar
• Wang, Y., Guo, S., Chen, H., and Zhou, Y. (2014). “Comparative study of monthly inflow prediction methods for the Three Gorges Reservoir.” Stochastic Environ. Res. Risk Assess., 28(3), 555–570.10.1007/s00477-013-0772-4
   Web of Science ®Google Scholar
• Young, C.-C., Liu, W.-C., and Hsieh, W.-L. (2015). “Predicting the water level fluctuation in an alpine lake using physically based, artificial neural network, and time series forecasting models.” Math. Prob. Eng., 11.
   Google Scholar
• Zadeh, M.R., Amin, S., Khalili, D., and Singh, V.P. (2010). “Daily Outflow Prediction by Multi Layer Perceptron with Logistic Sigmoid and Tangent Sigmoid Activation Functions.” Water. Resour. Manage., 24(11), 2673–2688.10.1007/s11269-009-9573-4
   Web of Science ®Google Scholar
• Zaji, A.H., and Bonakdari, H. (2015). “Application of artificial neural network and genetic programming models for estimating the longitudinal velocity field in open channel junctions.” Flow Meas. Instrum., 41, 81–89.10.1016/j.flowmeasinst.2014.10.011
   Web of Science ®Google Scholar