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Literature Cited

  • 1
    Chang, N.B., & Lin, Y.T. (1997). An analysis of recycling impacts on solid waste generation by time series intervention modeling, Resources Conservation and Recycling, 19, 165186.
  • 2
    Grossman D.H.J., & Mark D.H. (1974). Waste generation methods for solid waste collection, Journal of Environmental Engineering ASCE, 6, 12191230.
  • 3
    Dyson, B., & Chang, N.B. (2005). Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling, Waste Management, 25, 669679.
  • 4
    Svozil, D., Kvasnicka, V., & Pospichal, J. (1997). Introduction to multilayer feed-forward neural networks, Chemometrics and Intelligent Laboratory Systems, 39, 4362.
  • 5
    Zhu, Z., & ReVelle, C. (1993). A cost allocation method for facilities siting with fixed-charge cost functions, Civil Engineering Systems, 7, 2935.
  • 6
    Blasco, J.A., Fueyo, N., Dopazo, C., & Ballester, J. (1998). Modeling the temporal evolution of a reduced combustion chemical system with an artificial neural network, Combustion and Flame, 113, 3852.
  • 7
    Abbasi, M., Abduli, M.A., Omidvar, B., & Baghvand, A. (2013). Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model, International Journal of Environmental Research, 7, 2738.
  • 8
    Dong, C.Q., Jin, B.S. & Li, D.J. (2003). Predicting the heating value of MSW with a feed forward neural network, Waste Management, 23, 103106.
  • 9
    Karaca, F., & Ozkaya, B. (2006). NN-LEAP: A neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site, Environmental Modelling & Software, 21, 11901197.
  • 10
    Liu, H., Liu, T., Liu, L., Guo, H.C., Yu, Y.J., & Wang, Z. (2010). Integrated simulation and optimization approach for studying urban transportation-environment systems in Beijing, Journal of Environmental Informatics, 15, 99110.
  • 11
    Ping, J., Chen, Y., Chen, B., & Howboldt, K. (2010). A robust statistical analysis approach for pollutant loadings in urban rivers, Journal of Environmental Informatics, 16, 3542.
  • 12
    Abdoli, M.A., Falah Nezhad, M., Salehi Sede, R., & Behboudian, S. (2011). Longterm forecasting of solid waste generation by the artificial neural networks. Environmental Progress & Sustainable Energy, 31(4), 628636.
  • 13
    Vapnik, V. (1995). Nature of Statistical Learning Theory, New York: Springer.
  • 14
    Broomhead, D., & Lowe, D. (1998). Multivariable functional interpolation and adaptive networks, Complex Systems, 2, 321355.
  • 15
    Mukherjee, S., Osuna, E., & Girosi, F. (1997). Nonlinear prediction of chaotic time series using a support vector machine. IEEE Workshop on Neural Networks and Signal Processing, Amelia Island, FL.
  • 16
    Vapnik, V., Golowich, S., & Smola, A. (1997). Support method for function approximation regression estimation, and signal processingReport, Cambridge, MA: MIT Press.
  • 17
    Müller, K. R., Smola, A. J., Rätsch, G., Schölkopf, B., Kohlmorgen, J. and Vapnik, V. (1997). Predicting time series with support vector machines. In W. Gerstner, A. Germond, M. Hasler, J.D. Nicoud, (Eds.) Artificial Neural Networks, ICANN'97 (pp. 9991004), Springer Berlin Heidelberg.
  • 18
    Yeganeh, B., Motlagh, M.S.P., Rashidi, Y., & Kamalan, H. (2012). Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model, Atmospheric Environment, 55, 357365.
  • 19
    Nason, G.P., & Von Sachs, R. (1999). Wavelets in time series analysis, Philosophical Transactions of the Royal Society of London Series A, 357, 25112526.
  • 20
    Marce, R., Comerma, M., Garcia, J.C., & Armengol, J. (2004). A neuro-fuzzy modeling tool to estimate fluvial nutrient loads in watersheds under time-varying human impact, Limnology and Oceanography Methods, 2, 342355.
  • 21
    Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2007). A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff, Journal of Hydrology, 337, 2234.
  • 22
    Aqil, M., Kita, I., Yano, A., & Nishiyama, S. (2007). Analysis and prediction of flow from local source in a river basin using a neuro-fuzzy modeling tool, Journal of Hydrology Environmental Management, 85, 215223.
  • 23
    Abdoli, M.A. (1995). Solid Waste Management in Tehran, Waste Management Resource, 13, 519531.
  • 24
    Noori, R., Karbassi, A., & Salman Sabahi, M. (2010). Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction, Journal of Environmental Management, 91, 767771.
  • 25
    Mallat, S. (1989). A theory for multiresolution signal decomposition: The wavelet representation, IEEE Transactions of the Pattern Analysis and Machine Intelligence, 11, 674693.
  • 26
    Daubechies, I. (1992). Ten Lectures on Wavelets, Society for Industrial and Applied Mathematics.
  • 27
    Vapnik, V. (1998). Statistical Learning Theory, New York: Wiley.
  • 28
    Platt, J.C. (1999). Fast training of support vector machines using sequential minimal optimization. In Advances in kernel methods (pp. 185208). MIT Press.
  • 29
    Schölkopf, B., & Smola, A.J. (2002). Learning with Kernels, Cambridge, MA: MIT Press.
  • 30
    Yu, X., Liong, S.-Y., & Babovic, V. (2004). EC-SVM approach for realtime hydrologic forecasting, Journal of Hydroinformatics, 6, 209223.
  • 31
    Brito, N.S.P., Souza, B.A., & Pires, F.A.C. (1998). Daubechies wavelets in quality of electrical power, 8th International conference on harmonics and quality of power, Athens. (pp. 511515).