Abbasi, M., Abduli, M., Omidvar, B., and Baghvand, A. (2012). Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model. International Journal of Environmental Research, 7, 27-38.
Abdoli, MA., Noori, R., Jalili, M., and Salehian, A. (2007). Forecasting of Tehran waste production using artificial neural networks and multivariate statistical methods. In: Proceedings of 3th National Congress on Waste Management, 21-22 Oct., Environmental Protection Agency, Tehran, Iran , pp. 61-72. (In Farsi)
Akkaya, E., and Demir, A. (2010). Predicting the heating value of municipal solid waste-based materials: An artificial neural network model. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 32, 1777-1783.
Cay, Y., Cicek, A., Kara, F., and Sağiroğlu, S. (2012). Prediction of engine performance for an alternative fuel using artificial neural network. Applied Thermal Engineering, 37, 217-225.
Çay, Y., Korkmaz, I., Çiçek, A., and Kara, F. (2013). Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy, 50, 177-186.
Damghani, AM., Savarypour, G., Zand, E., and Deihimfard, R. (2008). Municipal solid waste management in Tehran: Current practices, opportunities and challenges. Waste management, 28, 929-934.
Dyson, B., and Chang, NB. (2005). Forecasting municipal solid waste generation in a fast-growing urban region with system dynamics modeling. Waste management, 25, 669-679.
Hyun Il, P., Borinara, P., and Hong, K. (2011). Geotechnical considerations for end-use of old municipal solid waste landfills. International Journal of Environmental Research 5, 573-584.
Jalili Ghazi Zade, M., and Noori, R. (2007). Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad. International Journal of Environmental Research, 2, 13-22.
Karaca, F., and Özkaya, 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, 1190-1197.
Khoshnevisan, B., Rafiee, S., Omid, M., and Mousazadeh, H. (2014a). Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Information processing in agriculture, 1, 14-22.
Khoshnevisan, B., Rafiee, S., Omid, M., and Mousazadeh, H. (2014b). Prediction of potato yield based on energy inputs using multi-layer adaptive neuro-fuzzy inference system. Measurement 47, 521-530.
Khoshnevisan, B., Rafiee, S., and Mousazadeh, H. (2014c). Application of multi-layer adaptive neuro-fuzzy inference system for estimation of greenhouse strawberry yield. Measurement, 47, 903-910.
Khoshnevisan, B., Rafiee, S., Iqbald, J., Shamshirbande, S., Omid, M., Anuarf, N., and Abdul Wahabg, A. (2015). A Comparative Study between Artificial Neural Networks and Adaptive Neuro-fuzzy Inference Systems for Modeling Energy Consumption in Greenhouse Tomato Production-A Case Study in Isfahan Province. Journal of Agricultural Science and Technology, 17, 49-62.
Naderloo, L., Alimardani, R., Omid, M., Sarmadian, F., Javadikia, P., Torabi, MY., and Alimardani, F. (2012). Applicationof ANFIS to predict crop yield based on different energy inputs. Measurement, 45, 1406-1413.
Najafi, G., Ghobadian, B., Tavakoli, T., Buttsworth, D., Yusaf, T., and Faizollahnejad, M. (2009). Performance and exhaust emissions of a gasoline engine with ethanol blended gasoline fuels using artificial neural network. Applied Energy, 86, 630-639.
Noori. R., Abdoli, MA., Farokhnia, A., and Ghaemi, A. (2009a). Prediction of weekly solid waste by using of neural network and hybrid of wavelet. Journal ofEnvironmental Studies,35(49), 25-30. (In Farsi)
Noori, R., Abdoli, MA., Farokhnia, A., and Abbasi, M. (2009b). Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network. Expert Systems with Applications, 36, 9991-9999.
Noori, R., Karbassi, A., and Sabahi, MS. (2010a). Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management, 91, 767-771.
Noori, R., Hoshyaripour, G., Ashrafi, K., and Araabi, BN. (2010b). Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration. Atmospheric Environment, 44, 476-482.
Pahlavan, R., Omid, M., and Akram, A. (2012). Energy input–output analysis and application of artificial neural networks for predicting greenhouse basil production. Energy, 37, 171-176.
Sahoo, G., Ray, C., and De, Carlo E. (2006). Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii. Journal of Hydrology, 327, 525-538.
Shojaeefard, M., Etghani, M., Tahani, M., and Akbari, M. (2012). Artificial Neural Network Based Multi-Objective Evolutionary Optimization of a Heavy-Duty Diesel Engine. International Journal of Automotive Engineering, 2(4), 206-215 (In Farsi)
Singh, R., Kainthola, A., and Singh, T. (2012). Estimation of elastic constant of rocks using an ANFIS approach. Applied Soft Computing, 12, 40-45.
Tehran Waste Management Organization. (2014). Statistics report on 2014. Tehran Municipality, Iran. Retrieved January 12, 2014, from http://pasmand.tehran.ir/Default.aspx?alias=pasmand.tehran.ir/en.
Tiwari, MK., Bajpai, S., and Dewangan, U. (2012). Prediction of industrial solid waste with ANFIS model and its comparison with ANN model-A case study of Durg-Bhilai twin city India. International Journal of Engineering and Innovative Technology (IJEIT), 6, 192-201.
Wahab, SA., and Alawi, SM. (2008). Prediction of sulfur dioxide (so2) concentration levels from the mina al-fahal refinery in oman using artificial neural networks. American Journal of Environmental Sciences, 4, 473.
Yesilnacar, MI., Sahinkaya, E., Naz, M., and Ozkaya, B. (2008). Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. Environmental Geology, 56, 19-25.
Zare, M., and Khaki, JV. (2012). Prediction of mechanical properties of a warm compacted molybdenum prealloy using artificial neural network and adaptive neuro-fuzzy models. Materials & Design, 38, 26-31.
)
