Coastal solid waste prediction by applying machine learning approaches (Case study: Noor, Mazandaran Province, Iran)

Document Type : Research Paper


1 Department of Environmental Science and Engineering, Faculty of Natural Resources, University of Guilan, Guilan, Iran

2 Department of Forestry, Faculty of Natural Resources, University of Guilan, Guilan, Iran

3 Department of Environmental Science and Engineering, University of Tehran, Hamedan Governorate, Hamedan, Iran


Nowadays, intelligent systems are used as innovative tools in different environmental issues. However, the prediction of short-term waste, unlike the long-term scale, is less developed due to more uncertainties and the difficulty in determining measurable independent parameters. In this study, two types of artificial neural networks (MLP and RBF) and two decision tree algorithms (CHAID and CART) have been used as effective tools for short-term forecasting of total waste production in coastal areas of Noor in Mazandaran Province, Iran. So that, average temperature, daily rainfall, sunny hours, maximum relative humidity and maximum wind speed were determined as the most important independent parameters, while the amount of waste produced in the coastal areas of Noor was considered as the dependent variable. Wastes from the coastal areas were gathered and their weights were analysed during 12 months from July 2017 through June 2018. Samplings were carried out twice a week, three weeks of a month and 12 months of a year, overall 72 times a year. The required meteorological data was gathered from the meteorological station in Noor. Then the sensitivity analysis was performed to check the independency of the major independent parameters. Thereafter, the mentioned machine learning approaches were applied to predict the short-term total waste production in IBM SPSS Modeler version 18 environment. In the applied models, 60% of data were used in training the model and the other 40% were used for model evaluation.  The results indicated that the CHAID tree algorithm exhibits a better performance in predicting total solid waste production compared to CART, MLP and RBF models. The mean absolute error and the correlation coefficient (R) of CHAID algorithm was 0.067 and 0.828, respectively.


Abbasi, M, Abduli, M, Omidvar, B & 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, M, Mehrdadi, M & Rezazadeh, M 2015, Coastal solid waste management in Mazandaran province. Journal of Environmental Studies, 40: 861-874 (In Persian).
Abdoli, MA, Noori, R, Jalili, M & 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, 61-72 (In Persian).
Alimohamadi, AM, Abbasimehr, MH & Javaheri, A 2014, Prediction of stock return using financial ratios: A decision tree approach. Journal of Financial Management Strategy, 11: 129-151 (In Persian).
Breiman, L, Friedman, JH, Olshen, RA & Stone, CJ 1984, Classification and regression trees, New York: Chapman & Hall/CRC.
Cay, Y, Cicek, A, Kara, F & Sağiroğlu, S 2012, Prediction of engine performance for an alternative fuel using artificial neural network. Applied Thermal Engineering, 37: 217-225.
Cay, Y, Korkmaz, I, Çiçek, A & Kara, F 2013, Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network. Energy, 50: 177-186.
Ghazi Zade, MJ & Noori, R 2008, Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mashhad. International Journal of Environmental Research, 21: 13-22.
Hand, D, Heikki, M & Padhraic, S 2001, Principles of data mining, The MIT Press.
Hyun Il, P, Borinara, P & Hong, K 2011, Geotechnical considerations for end-use of old municipal solid waste landfills. International Journal of Environmental Research, 5: 573-584.
Johnson, NE, Ianiuk, O, Cazap, D, Liu, L, Starobin, D, Dobler, G & Ghandehari, M 2017, Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City. Waste Management, 62: 3-11.
Jozi, SA, Dehghani, M & Zarei, M 2012, Rural waste management strategic plan by A 'WOT Method (Case study: Minab). Journal of Environmental Studies, 384: 93-108 (In Persian).
Kalogirou, SA, 2003, Artificial intelligence for the modeling and control of combustion processes: a review. Progress in Energy and Combustion Science, 296: 515-566.
Kannangara, M, Dua, R, Ahmadi, L & Bensebaa, F 2018, Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Management, 74: 3-15.
Kass, GV 1980, An exploratory technique for investigating large quantities of categorical data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 29: 119-127.
Kontokosta, CE, Hong, B, Johnson, NE & Starobin, D 2018, Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities. Computers, Environment and Urban Systems, 70: 151-162.
Minousepehr, M, Alizadeh, MR & Talebbeydokhti, N 2018, Performance assessment of computational intelligence techniques in solid waste generation forecasting (A Case Study). Journal of Civil and Environmental Engineering, 48: 67-75 (In Persian).
Mohanbaba, S, Shanthi, V & Mohanbaba, S 2012, Application of decision tree algorithm in e-waste land filling. Indian Journal of Education and Information Management, 1: 40-48.
Nasrollahi-Sarvaghaji, S, Alimardani, R, Sharifi, M & Taghizadeh Yazdi, MR 2016, Prediction of Tehran solid waste production by using of neural network and adaptive neuro-fuzzy inference system. Iranian Journal of Biosystem Engineering, 47: 175-183 (In Persian).
Noori. R, Abdoli, MA, Farokhnia, A & Ghaemi, A 2009, Prediction of weekly solid waste by using of neural network and hybrid of wavelet. Journal of Environmental Studies, 35: 25-30 (In Persian).
Omidvar, K, Shafie, S & Taghizade, Z 2014, Assessing the performance of decision tree model in predicting precipitation in Kermanshah synoptic station. Journal of Applied Researches in Geographical Sciences, 34: 89-110 (In Persian).
Panahi, M & Mirhashemi, SH 2015, Assessment among two data mining algorithms CART and CHAID in forecast air temperature of the synoptic station of Arak. Environmental Sciences, 13: 53-58 (In Persian).
Patel, V & Meka, S 2013, Forecasting of municipal solid waste generation for medium scale towns located in the state of Gujarat, India. International Journal of Innovative Research and Science, 2: 4707-4716.
Shahabi, H, Khezri, S, Ahmad, BB & Zabihi, H 2012, Application of artificial neural network in prediction of municipal solid waste generation (Case study: Saqqez City in Kurdistan Province). World Applied Sciences Journal, 20: 336-343.
Shamshiry, E, Mokhtar, MB & Abdulai, AM 2014, Comparison of artificial neural network (ANN) and multiple regression analysis for predicting the amount of solid waste generation in a tourist and tropical area-Langkawi Island. In: proceeding of International Conference on Biological, Civil, Environmental Engineering (BCEE): 161-166.
Sodanil, M & Chatthong, P 2014, September. Artificial neural network-based time series analysis forecasting for the amount of solid waste in Bangkok. In: 9th International Conference on Digital Information Management (ICDIM 2014) (16-20). IEEE.
Zarkami, R, Guethlas, P & De Pauw, N 2010, Use of classification tree methods to study the habitat requirements of tench (Tinca tinca) (L., 1758). Caspian Journal of Environmental Sciences, 8: 55-63.