270
Views
18
CrossRef citations to date
0
Altmetric
Articles

Multi-objective genetic algorithm optimization of artificial neural network for estimating suspended sediment yield in Mahanadi River basin, India

&
Pages 207-215 | Received 29 Sep 2018, Accepted 11 Dec 2019, Published online: 18 Jan 2020

References

  • Adib, A. and Jahanbakhshan, H., 2013. Stochastic approach to determination of suspended sediment concentration in tidal rivers by artificial neural network and genetic algorithm. Canadian Journal of Civil Engineering, 40, 299–312.
  • Adib, A. and Mahmoodi, A., 2017. Prediction of suspended sediment load using ANN GA conjunction model with markov chain approach at flood conditions. KSCE Journal of Civil Engineering, 21 (1), 447–457.
  • Alizadeh, M.J., et al., 2017. Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models. Environmental Science and Pollution Research, 24 (36), 28017–28025.
  • Altun, H., Bilgil, A., and Fidan, B.C., 2007. Treatment of multidimensional data to enhance neural network estimators in regression problems. Expert Systems with Applications, 32 (2), 599–605.
  • Bastia, F. and Equeenuddin, S.M., 2016. Spatio-temporal variation of water flow and sediment discharge in the Mahanadi River, India. Global and Planetary Change, 144, 51–66.
  • Bishop, M, 1998. Neural networks for pattern recognition. Oxford: Clarendon Press.
  • Boukhrissa, Z.A., et al. 2013. Compare the Ann and Sediment rating curve model for prediction of suspended sediment load in EI Kebir catchment, Algeria. Journal of Earth Systems Science, 122 (5), 1303–1312.
  • CWC (Central Water Commission), 2012. Integrated hydrological data book. Hydrological data directorate, information systems organization, Water planning and projects wing. In: Central water commission. New Delhi: Hydrological Data Directorate, Information System Organization, Water Planning and Projects Wing, Central Water Commission (CWC).
  • Chatterjee, S. and Bandopadhyay, S., 2007. Global neural network learning using genetic algorithm for ore grade prediction of iron ore deposit. Mining and Resource Engineering, 12 (4), 258–269.
  • Chatterjee, S. and Bandopadhyay, S., 2011. Goodnews bay platinum resource estimation using least square support vector regression with selection of input space dimension and hyperparameters. Natural Resources Research, 20, 117–129.
  • Chatterjee, S. and Bandopadhyay, S., 2012. Reliability estimation using a genetic algorithm-based artificial neural network: an application to a laud-haul-dump machine. Expert Systems and Applications, 39, 10943–10951.
  • Chen, X.Y. and Chau, K.W., 2016. A hybrid double feedforward neural network for suspended sediment load estimation. Water Resources Management, 30 (7), 2179–2194.
  • Cigizoglu, H.K., 2004. Estimation and forecasting of daily suspended sediment data by multilayer perceptrons. Advances in Water Resources, 27, 185–195.
  • Cobaner, M., Unal, B., and Kisi, O., 2009. Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. Journal of Hydrology, 367, 52–61.
  • Cybenko, G., 1989. Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals and Systems, 2, 303–314.
  • Geman, S., Bienenstock, E., and Doursat, R., 1992. Neural network and bias/variance dilemma. Neural Computation, 4 (1), 1–58.
  • Grenander, U., 1952. On empirical spectral analysis of stochastic processes. Arkiv för Matematik, 1 (6), 503–531.
  • Gupta, S.C. and Kapoor, V.K., 2013. Fundamental of mathematical statistics. New Delhi: Sultan Chand and Sons.
  • Hagan, M.T. and Menhaj, M.B., 1994. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks, 5 (6), 989–993.
  • Hauke, J. and Kossowski, T., 2011. Comparison of values of pearson's and spearman's correlation coefficients on the same sets of data. Quaestiones Geographicae, 30, 87–93.
  • Heng, S. and Suetsugi, T., 2013. Using artificial neural network to estimate sediment load in ungauged catchments of the Tonle Sap River Basin, Cambodia. Journal of Water Resource and Protection, 5, 111–123. Available from: http://dx.doi.org/10.4236/jwarp.2013.52013.
  • Holland, J., 1975. Adaptation in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
  • Hornik, K., et al. 1989. Multilayer feed forward networks are universal approximators. Neural Networks, 2, 359–366.
  • Hui, L. and Jijan, L., 2008. Multiobjective optimization of water-sedimentation-power in reservoir based on pareto-optimal solution. Transactions of Tianjin University, 14, 282–288.
  • Jain, S.K., 2001. Development of integrated sediment rating curves using ANNs. Journal of Hydraulic Engineering, 127 (1), 30–37.
  • Kant, A., et al., 2013. Comparison of multi-objective evolutionary neural network, adaptive neuro-inference system and bootstrap-based neural network for flood forecasting. Neural Computing and Applications, 23, 231–246.
  • Karl, A.K. and Lohani, A.K., 2010. Development of flood forecasting system using statistical and ANN techniques in the downstream catchment of Mahanadi basin, India. Journal of Water Resource and Protection, 2, 880–887.
  • Kashid, S.S., Ghosh, S., and Maity, R., 2010. Streamflow prediction using multisite rainfall obtained from hydro climatic teleconnection. Journal of Hydrology, 395, 23–38.
  • Kisi, O., 2012. Modeling discharge-suspended sediment relationship using least square support vector machine. Journal of Hydrology, 456, 110–120.
  • Kisi, O. and Shiri, J., 2012. River suspended sediment estimation by climatic variables implication: comparative study among soft computing techniques. Computers & Geosciences, 43, 73–82.
  • Kulasiri, D. and Verwoerd, V., 2002. Stochastic dynamics: modeling solute transport in porous media. North Holland series in applied mathematics and mechanics, Vol. 44. Amsterdam: Elsevier.
  • Legates, D.R. and McCabe, G.J., 1999. Evaluating the use of goodness-of-fit measures in hydrology and hydroclimatic model validation. Water Resources Research, 35 (1), 233–241.
  • Levin, S.A., 1976. Population dynamics in models in heterogeneous environments. Annual Review of Ecology and Systematics, 7, 287–310.
  • Meher, J., 2014. Rainfall and runoff estimation using hydrological models and ANN techniques. Thesis (PhD). Rourkela: National Institute of Technology, 1–218.
  • Meher, J. and Jha, R., 2013. Time series analysis of monthly rainfall data for the Mahanadi river Basin, India. Sciences in Cold and Arid Regions, 5 (1), 73–84.
  • Melesse, A.M., et al., 2011. Suspended sediment load prediction of river systems: an artificial neural network approach. Agricultural Water Management, 98 (5), 855–866. doi: 10.1016/j.agwat.2010.12.012
  • Olyaie, E., et al., 2015. A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environmental Monitoring and Assessment, 187 (4), 189.
  • Packianather, M., Drake, P., and Rowlands, H., 2000. Optimizing the parameters of multilayered feedforward neural networks through Taguchi design of experiments. Quality and Reliability Engineering International, 16 (6), 461–473.
  • Peng, Y., Ji, C., and Gu, R., 2014. Multiobjective optimization model for coordinated regulation of water flow and sediment in cascade reservoirs. Water Resources Management, 28, 4019–4033.
  • Pramanik, N. and Panda, R.K., 2009. Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences Journal, 54 (2), 247–260.
  • Rajaee, T., et al., 2009. Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Science of The Total Environment, 407, 4916–4927.
  • Sakai, K, Osawa, K, and Yoshinaga, A., 2005. Development of suspended sediment concentration analysis model and its application with multi-objective optimization. Paddy and Water Environment, 3, 201–209.
  • Samarasinghe, S., 2016. Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. Boca Raton, Florida: CRC Press.
  • Tang, Z., Almeida, D.C., and Fishwick, P.A., 1991. Time series forecasting using neural networks vs. Box- Jenkins methodology. Journal of Simulation, 57, 303–310.
  • Tahmasebi, P. and Hezarkhani, A., 2009. Application of optimized neural network by genetic algorithm, IAMG09. Stanford, CA: Stanford University.
  • Taormina, R., Chau, K.W., and Sivakumar, B., 2015. Neural network river forecasting through baseflow separation and binary-coded swarm optimization. Journal of Hydrology, 529, 1788–1797.
  • Udo, G.J., 1992. Neural networks applications in manufacturing processes. Computers and Industrial Engineering, 23 (1–4), 97–100.
  • Udo, G.J. and Gupta, Y.P., 1994. Applications of neural networks in manufacturing management systems. Production Planning and Control, 5 (3), 258–270.
  • Walling, D.E., 2009. The impact of global change on erosion and sediment transport by rivers: current progress and future challenges. Paris: United Nations Educational, Scientific and Cultural Organization.
  • White, S., 2005. Sediment yield prediction and modelling. Hydrological Processes, 19 (15), 3053–3057.
  • Wu, C.L. and Chau, K.W., 2011. Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. Journal of Hydrology, 399 (3–4), 394–409.
  • Yadav, A., Chatterjee, S., and Equeenuddin, S.M., 2017. Prediction of suspended sediment yield by artificial neural network and traditional mathematical model in Mahanadi river basin, India. Sustainable Water Resources Management, 4 (4), 745–759. doi:10.1007/s40899-017-0160-1.
  • Yadav, A., Chatterjee, S., and Equeenuddin, S.M., 2018. Suspended sediment yield estimation using genetic algorithm-based artificial intelligence models: case study of Mahanadi river, India. Hydrological Sciences Journal, 63 (8), 1162–1182. doi:10.1080/02626667.2018.1483581.
  • Zanaganeh, M., Mousavi, S.J., and Sahidi, A.F.E., 2009. A hybrid genetic algorithm-adaptive neural network based fuzzy inference system in prediction of wave parameter. EngineeringApplications of Artificial Intelligence, 22, 1194–1202.
  • Zhang, D., et al., 2015. A genetic algorithm based support vector machine model for blood-brain barrier penetration prediction. BioMed Research International, 2015, 1–13.
  • Zhu, Y.M., Lu, X.X., and Zhou, Y., 2007. Suspended sediment flux modeling with artificial neural network: an example of the Longchuanjiang river in the Upper Yangtze catchment, China. Geomorphology, 84 (1–2), 111–125.
  • Zhu, Y.M., Lu, X.X., and Zhou, Y., 2008. Sediment flux sensitivity to climate change: A case study in the Longchuanjiang catchment of the upper Yangtze River, China. Global and Planetary Change, 60 (3), 429–442.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.