Artificial neural network applied in forecasting the composition of municipal solid waste in Iasi, Romania
Abstract
Neural network time series (NNTS) tool was used to predict municipal solid waste composition in Iasi, Romania. The nonlinear input output (NIO) time series model and nonlinear autoregressive model with external (exogenous) input (NARX) included in this tool were selected. The coefficient of determination (R2) and root mean square error (RMSE) were chosen for evaluation. By applying NIO, the optimum model is 4-11-6 artificial neural network (ANN, R2 = 0.929) in the case of testing as for the validation, with all 0.849 and 0.885, respectively. Applying NARX, the suitable model became 4-13-6 ANN model, with R2 = 0.999 for training, 0.879 for testing, and 0.931, respectively 0.944 for validation and all. The resulted RMSE is zero for training and 0.0109 for validation in the case of this model which had 4 inputs, 13 neurons and 6 outputs. The four input variables were: number of residents, population aged 15–59 years, urban life expectancy, total municipal solid waste (ton/year). The suitable ANN model revealed the lowest root mean square error and the highest coefficient of determination. Results indicate that NNTS tool is a complex instrument, NARX is more accurate than NIO model, and can be used and applied easily.
Keyword : artificial neural network, environmental processes modeling, population, solid waste, waste composition, waste management technologies
How to Cite
Ghinea, C., Cozma, P., & Gavrilescu, M. (2021). Artificial neural network applied in forecasting the composition of municipal solid waste in Iasi, Romania. Journal of Environmental Engineering and Landscape Management, 29(3), 368-380. https://doi.org/10.3846/jeelm.2021.15553
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abbasi, M., & El Hanandeh, A. (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management, 56, 13–22. https://doi.org/10.1016/j.wasman.2016.05.018
Abbasi, M., Abduli, M.A., Omidvar, B., & Baghvand, A. (2014). Results uncertainty of support vector machine and hybrid of wavelet trans-form‐support vector machine models for solid waste generation forecasting. Environmental Progress & Sustainable Energy, 33(1), 220–228. https://doi.org/10.1002/ep.11747
Adamović, V. M., Antanasijević, D. Z., Ristić, M. Đ., Perić-Grujić, A. A., & Pocajt, V. V. (2016). Prediction of municipal solid waste genera-tion using artificial neural network approach enhanced by structural break analysis. Environmental Science and Pollution Research, 24(1), 299–311. https://doi.org/10.1007/s11356-016-7767-x
Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceu-tical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727. https://doi.org/10.1016/S0731-7085(99)00272-1
Ali, S. A., & Ahmad, A. (2019). Forecasting MSW generation using artifcial neural network time series model: a study from metropolitan city. SN Applied Sciences, 1, 1338. https://doi.org/10.1007/s42452-019-1382-7
Antanasijević, D., Pocajt, V., Popović, I., Redžić, N., & Ristić, M. (2013). The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustainability Science, 8(1), 37–46. https://doi.org/10.1007/s11625-012-0161-9
Batinic, B., Vukmirovic, S., Vujic, G., Stanislavljevic, N., Ubavin, D., & Vukmirovic, G. (2011). Using ANN model to determine future waste characteristics in order to achieve specific waste management targets – case study of Serbia. Journal of Scientific & Industrial Re-search, 70(07), 513–518.
Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Bellaaj, N. M. (2018). A Nonlinear Autoregressive Exogenous (NARX) neural net-work model for the prediction of the daily direct solar radiation. Energies, 11(3), 620. https://doi.org/10.3390/en11030620
Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249–264. https://doi.org/10.1016/S0304-3800(02)00257-0
Ghinea, C. (2012). Waste management models and their application to sustainable management of recyclable waste [PhD Thesis]. Gheorghe Asachi Technical University of Iasi, Romania.
Ghinea, C., & Gavrilescu, M. (2010). Decision support models for solid waste management – an overview. Environmental Engineering and Management Journal, 9(6), 869–880. https://doi.org/10.30638/eemj.2010.116
Ghinea, C., & Gavrilescu, M. (2016). Costs analysis of municipal solid waste management scenarios: IASI – Romania case study. Journal of Environmental Engineering and Landscape Management, 24(3), 185–199. https://doi.org/10.3846/16486897.2016.1173041
Ghinea, C., & Gavrilescu, M. (2019). Solid waste management for circular economy. Challenges and opportunities in Romania – The case study of Iasi County. In M. L. Franco-Garcia, J. Carpio-Aguilar, & H. Bressers (Eds.), Greening of industry networks studies: Vol. 6. To-wards zero waste (pp. 25–60). Springer. https://doi.org/10.1007/978-3-319-92931-6_3
Ghinea, C., Dragoi, E. N., Comanita, E.-D., Gavrilescu, M., Campean, T., Curteanu, S., & Gavrilescu, M. (2016). Forecasting municipal solid waste generation using prognostic tools and regression analysis. Journal of Environmental Management, 182, 80–93. https://doi.org/10.1016/j.jenvman.2016.07.026
Ghinea, C., Petraru, M., Bressers, J. T. A., & Gavrilescu, M. (2012). Environmental evaluation of waste management scenarios – significance of the boundaries. Journal of Environmental Engineering and Landscape Management, 20(1), 76–85. https://doi.org/10.3846/16486897.2011.644665
Goel, S., Ranjan, V. P., Bardhan, B., & Hazra, T. (2017). Forecasting solid waste generation rates. In D. Sengupta & S. Agrahari (Eds.), Mod-elling trends in solid and hazardous waste management (pp. 35–64). Springer, Singapore. https://doi.org/10.1007/978-981-10-2410-8_3
Guthrie, D. (2008). Unsupervised detection of anomalous text. University of Sheffield. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.396.6322&rep=rep1&type=pdf
Han, S. H., Kim, K. W., Kim, S. Y., & Youn, Y. K. (2018). Artificial neural network: Understanding the basic concepts without mathematics. Dementia and Neurocognitive Disorder, 17(3), 83–89. https://doi.org/10.12779/dnd.2018.17.3.83
Henriques, R. S., & Coelho, L. M. G. (2019). A multi-objective optimization model to support the municipal solid waste management. Envi-ronmental Engineering and Management Journal, 18(5), 1077–1088. https://doi.org/10.30638/eemj.2019.104
Ibrahim, O. M. (2013). A comparison of methods for assessing the relative importance of input variables in artificial neural networks. Journal of Applied Sciences Research, 9(11), 5692–5700. http://www.aensiweb.com/old/jasr/jasr/2013/5692-5700.pdf
Kannangara, M., Dua, R., Ahmadi, L., & Bensebaa, F. (2018). Modeling and prediction of regional municipal solid waste generation and diver-sion in Canada using machine learning approaches. Waste Management, 74, 3–15. https://doi.org/10.1016/j.wasman.2017.11.057
Kumar, J. S., Subbaiah, K. V., & Prasada Rao, P. V. V. (2011). Prediction of municipal solid waste with RBF Net Work – A case study of Eluru, A. P, India. International Journal of Innovation, Management and Technology, 2(3), 238–243.
Kumar, A., & Samadder, S. R. (2017). An empirical model for prediction of household solid waste generation rate – a case study of Dhanbad, India. Waste Management, 68, 3–15. https://doi.org/10.1016/j.wasman.2017.07.034
Li, Y. P., Sun, Y., Huang, G. H., & Zhao, F. (2016). An interval-parameter queuing model for planning municipal solid waste management system with cost-effective objective. Environmental Engineering and Management Journal, 15(8), 1673–1687. https://doi.org/10.30638/eemj.2016.180
Marcjasz, G., Uniejewski, B., & Weron, R. (2019). Probabilistic electricity price forecasting with NARX networks: Combine point or proba-bilistic forecasts? International Journal of Forecasting, 36(2), 466–479. https://doi.org/10.1016/j.ijforecast.2019.07.002
MathWorks. (2016). Neural Network Toolbox™ User’s Guide. https://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf/
Menezes, J. M. P. Jr., & Barreto, G. A. (2008). Long-term time series prediction with the NARX network: An empirical evaluation. Neuro-computing, 71(16–18), 3335–3343. https://doi.org/10.1016/j.neucom.2008.01.030
National Institute of Statistics. (2015). Iasi county statistics, county statistics. NIS. http://www.iasi.insse.ro/main.php?id=373/
NIST/SEMATECH. (2015). e-Handbook of statistical methods. National Institutes of Standards and Technologies (NIST), US. http://www.itl.nist.gov/div898/handbook/
Noori, R., Abdoli, M., Jalili Ghazizade, M., & Samieifard, R. (2009). Comparison of Neural Network and Principal Component-Regression Analysis to predict the solid waste generation in Tehran. Iranian Journal of Public Health, 38(1), 74–84.
Noori, R., Karbassi, A., & Salman Sabahi, M. (2010a). Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management, 91(3), 767–771. https://doi.org/10.1016/j.jenvman.2009.10.007
Noori, R., Khakpour, A., Omidvar, B., & Farokhnia, A. (2010b). Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications, 37(8), 5856–5862. https://doi.org/10.1016/j.eswa.2010.02.020
Noori, R., Karbassi, A. R., Mehdizadeh, H., Vesali‐Naseh, M., & Sabahi, M. S. (2011). A framework development for predicting the longitu-dinal dispersion coefficient in natural streams using an artificial neural network. Environmental Progress & Sustainable Energy, 30(3), 439–449. https://doi.org/10.1002/ep.10478
Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: A randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1–2), 135–150. https://doi.org/10.1016/S0304-3800(02)00064-9
Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178(3–4), 389–397. https://doi.org/10.1016/j.ecolmodel.2004.03.013
Ortiz-Rodriguez, O. O., Rivera-Alarcon, H. U., & Villamizar-Gallardo, R. A. (2018). Evaluation of municipal solid waste by means of life cycle assessment: Case study in the south-western region of the department of Norte de Santander, Colombia. Environmental Engineering and Management Journal, 17(3), 611–619. https://doi.org/10.30638/eemj.2018.062
Owusu-Sekyere, E., Harris, E., & Bonyah, E. (2013). Forecasting and planning for solid waste generation in the Kumasi Metropolitan Area of Ghana: An ARIMA time series approach. International Journal of Sciences, 2(04), 69–83. https://ideas.repec.org/a/adm/journl/v2y2013i4p69-83.html
Radaei, E., Moghaddam, M. R. A., & Arami, M. (2017). Application of artificial neural network on modeling of reactive blue 19 removal by modified pomegranate residual. Environmental Engineering and Management Journal, 16(9), 2113–2122. https://doi.org/10.30638/eemj.2017.218
Ranganathan, A. (2004). The Levenberg-Marquardt algorithm. http://ananth.in/docs/lmtut.pdf
Rimaitytė, I., Ruzgas, T., Denafas, G., Račys, V., & Martuzevicius, D. (2012). Application and evaluation of forecasting methods for municipal solid waste generation in an eastern-European city. Waste Management & Research, 30(1), 89–98. https://doi.org/10.1177/0734242X10396754
Romanian National Plan Waste. (2018). Romanian National Waste Management Plan. http://www.mmediu.ro/app/webroot/uploads/files/2018-01-10_MO_11_bis.pdf
Romanian National Strategy Waste. (2013). Romanian National Waste Management Strategy 2014–2020. http://mmediu.ro/new/wp-content/uploads/2014/01/NationalWasteStrategy.pdf
Shahabi, H., Khezri, S., Ahmad, B. B., & 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(2), 336–343.
Shamshiry, E., Mokhtar, M., Abdulai, A.-M., Komoo, I., & Yahaya, N. (2014). Combining artificial neural network- genetic algorithm and response surface method to predict waste generation and optimize cost of solid waste collection and transportation process in Langkawi Is-land, Malaysia. Malaysian Journal of Science, 33(2), 118–140. https://doi.org/10.22452/mjs.vol33no2.1
Shan, C. S. (2010). Projecting municipal solid waste: The case of Hong Kong SAR. Resources Conservation and Recycling, 54(11), 759–768. https://doi.org/10.1016/j.resconrec.2009.11.012
Shen, H.-Y., & Chang, L.-C. (2013). Online multistep-ahead inundation depth forecasts by recurrent NARX networks. Hydrology and Earth System Sciences, 17(3), 935–945. https://doi.org/10.5194/hess-17-935-2013
Simion, I. M., Fortuna, M. E., Bonoli, A., & Gavrilescu, M. (2012). Comparing environmental impacts of natural inert and recycled construc-tion and demolition waste processing using LCA. Journal of Environmental Engineering and Landscape Management, 21(4), 273–287. https://doi.org/10.3846/16486897.2013.852558
Singh, D., & Satija, A. (2016). Municipal solid waste generation forecasting for Faridabad City located in Haryana State, India. In Advances in intelligent systems and computing: Vol. 437. Proceedings of Fifth International Conference on Soft Computing for Problem Solving (pp. 285–292). https://doi.org/10.1007/978-981-10-0451-3_27
Song, J., & He, J. (2014). A multistep chaotic model for municipal solid waste generation prediction. Environmental Engineering Science, 31(8), 461–468. https://doi.org/10.1089/ees.2014.0031
Suliman, A. H. A., & Darus, I. Z. M. (2019). Semi-distributed neural network models for streamflow prediction in a small catchment Pinang. Environmental Engineering and Management Journal, 18(2), 535–544. https://doi.org/10.30638/eemj.2019.050
Vu, H. L., Ng, K. T. W., & Bolingbroke, D. (2019). Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models. Waste Management, 84, 129–140. https://doi.org/10.1016/j.wasman.2018.11.038
Wu, Z., Fan, J., Gao, Y., Shang, H., & Song, H. (2019). Study on prediction model of space-time distribution of air pollutants based on artifi-cial neural network. Environmental Engineering and Management Journal, 18(7), 1575–1590. https://doi.org/10.30638/eemj.2019.148
Younes, M. K., Nopiah, Z. M., Ahmad Basri, N. E., Basri, H., Abushammala, M. F. M., & Maulud, K. N. A. (2015). Prediction of municipal solid waste generation using nonlinear autoregressive network. Environmental Monitoring and Assessment, 187, 753. https://doi.org/10.1007/s10661-015-4977-5
Yu, X., Chen, Z., & Qi, L. (2019). Comparative study of SARIMA and NARX models in predicting the incidence of schistosomiasis in China. Mathematical Biosciences and Engineering, 16(4), 2266–2276. https://doi.org/10.3934/mbe.2019112
Zade, M. J. G., & Noori, R. (2008). Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mash-had. International Journal of Environmental Research, 2(1), 13–22. https://tspace.library.utoronto.ca/bitstream/1807/49358/1/er08002.pdf
Zounemat-Kermani, M., Stephan, D., & Hinkelmann, R. (2019). Multivariate NARX neural network in prediction gaseous emissions within the influent chamber of wastewater treatment plants. Atmospheric Pollution Research, 10(6), 1812–1822. https://doi.org/10.1016/j.apr.2019.07.013
Abbasi, M., Abduli, M.A., Omidvar, B., & Baghvand, A. (2014). Results uncertainty of support vector machine and hybrid of wavelet trans-form‐support vector machine models for solid waste generation forecasting. Environmental Progress & Sustainable Energy, 33(1), 220–228. https://doi.org/10.1002/ep.11747
Adamović, V. M., Antanasijević, D. Z., Ristić, M. Đ., Perić-Grujić, A. A., & Pocajt, V. V. (2016). Prediction of municipal solid waste genera-tion using artificial neural network approach enhanced by structural break analysis. Environmental Science and Pollution Research, 24(1), 299–311. https://doi.org/10.1007/s11356-016-7767-x
Agatonovic-Kustrin, S., & Beresford, R. (2000). Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceu-tical research. Journal of Pharmaceutical and Biomedical Analysis, 22(5), 717–727. https://doi.org/10.1016/S0731-7085(99)00272-1
Ali, S. A., & Ahmad, A. (2019). Forecasting MSW generation using artifcial neural network time series model: a study from metropolitan city. SN Applied Sciences, 1, 1338. https://doi.org/10.1007/s42452-019-1382-7
Antanasijević, D., Pocajt, V., Popović, I., Redžić, N., & Ristić, M. (2013). The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustainability Science, 8(1), 37–46. https://doi.org/10.1007/s11625-012-0161-9
Batinic, B., Vukmirovic, S., Vujic, G., Stanislavljevic, N., Ubavin, D., & Vukmirovic, G. (2011). Using ANN model to determine future waste characteristics in order to achieve specific waste management targets – case study of Serbia. Journal of Scientific & Industrial Re-search, 70(07), 513–518.
Boussaada, Z., Curea, O., Remaci, A., Camblong, H., & Bellaaj, N. M. (2018). A Nonlinear Autoregressive Exogenous (NARX) neural net-work model for the prediction of the daily direct solar radiation. Energies, 11(3), 620. https://doi.org/10.3390/en11030620
Gevrey, M., Dimopoulos, I., & Lek, S. (2003). Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecological Modelling, 160(3), 249–264. https://doi.org/10.1016/S0304-3800(02)00257-0
Ghinea, C. (2012). Waste management models and their application to sustainable management of recyclable waste [PhD Thesis]. Gheorghe Asachi Technical University of Iasi, Romania.
Ghinea, C., & Gavrilescu, M. (2010). Decision support models for solid waste management – an overview. Environmental Engineering and Management Journal, 9(6), 869–880. https://doi.org/10.30638/eemj.2010.116
Ghinea, C., & Gavrilescu, M. (2016). Costs analysis of municipal solid waste management scenarios: IASI – Romania case study. Journal of Environmental Engineering and Landscape Management, 24(3), 185–199. https://doi.org/10.3846/16486897.2016.1173041
Ghinea, C., & Gavrilescu, M. (2019). Solid waste management for circular economy. Challenges and opportunities in Romania – The case study of Iasi County. In M. L. Franco-Garcia, J. Carpio-Aguilar, & H. Bressers (Eds.), Greening of industry networks studies: Vol. 6. To-wards zero waste (pp. 25–60). Springer. https://doi.org/10.1007/978-3-319-92931-6_3
Ghinea, C., Dragoi, E. N., Comanita, E.-D., Gavrilescu, M., Campean, T., Curteanu, S., & Gavrilescu, M. (2016). Forecasting municipal solid waste generation using prognostic tools and regression analysis. Journal of Environmental Management, 182, 80–93. https://doi.org/10.1016/j.jenvman.2016.07.026
Ghinea, C., Petraru, M., Bressers, J. T. A., & Gavrilescu, M. (2012). Environmental evaluation of waste management scenarios – significance of the boundaries. Journal of Environmental Engineering and Landscape Management, 20(1), 76–85. https://doi.org/10.3846/16486897.2011.644665
Goel, S., Ranjan, V. P., Bardhan, B., & Hazra, T. (2017). Forecasting solid waste generation rates. In D. Sengupta & S. Agrahari (Eds.), Mod-elling trends in solid and hazardous waste management (pp. 35–64). Springer, Singapore. https://doi.org/10.1007/978-981-10-2410-8_3
Guthrie, D. (2008). Unsupervised detection of anomalous text. University of Sheffield. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.396.6322&rep=rep1&type=pdf
Han, S. H., Kim, K. W., Kim, S. Y., & Youn, Y. K. (2018). Artificial neural network: Understanding the basic concepts without mathematics. Dementia and Neurocognitive Disorder, 17(3), 83–89. https://doi.org/10.12779/dnd.2018.17.3.83
Henriques, R. S., & Coelho, L. M. G. (2019). A multi-objective optimization model to support the municipal solid waste management. Envi-ronmental Engineering and Management Journal, 18(5), 1077–1088. https://doi.org/10.30638/eemj.2019.104
Ibrahim, O. M. (2013). A comparison of methods for assessing the relative importance of input variables in artificial neural networks. Journal of Applied Sciences Research, 9(11), 5692–5700. http://www.aensiweb.com/old/jasr/jasr/2013/5692-5700.pdf
Kannangara, M., Dua, R., Ahmadi, L., & Bensebaa, F. (2018). Modeling and prediction of regional municipal solid waste generation and diver-sion in Canada using machine learning approaches. Waste Management, 74, 3–15. https://doi.org/10.1016/j.wasman.2017.11.057
Kumar, J. S., Subbaiah, K. V., & Prasada Rao, P. V. V. (2011). Prediction of municipal solid waste with RBF Net Work – A case study of Eluru, A. P, India. International Journal of Innovation, Management and Technology, 2(3), 238–243.
Kumar, A., & Samadder, S. R. (2017). An empirical model for prediction of household solid waste generation rate – a case study of Dhanbad, India. Waste Management, 68, 3–15. https://doi.org/10.1016/j.wasman.2017.07.034
Li, Y. P., Sun, Y., Huang, G. H., & Zhao, F. (2016). An interval-parameter queuing model for planning municipal solid waste management system with cost-effective objective. Environmental Engineering and Management Journal, 15(8), 1673–1687. https://doi.org/10.30638/eemj.2016.180
Marcjasz, G., Uniejewski, B., & Weron, R. (2019). Probabilistic electricity price forecasting with NARX networks: Combine point or proba-bilistic forecasts? International Journal of Forecasting, 36(2), 466–479. https://doi.org/10.1016/j.ijforecast.2019.07.002
MathWorks. (2016). Neural Network Toolbox™ User’s Guide. https://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf/
Menezes, J. M. P. Jr., & Barreto, G. A. (2008). Long-term time series prediction with the NARX network: An empirical evaluation. Neuro-computing, 71(16–18), 3335–3343. https://doi.org/10.1016/j.neucom.2008.01.030
National Institute of Statistics. (2015). Iasi county statistics, county statistics. NIS. http://www.iasi.insse.ro/main.php?id=373/
NIST/SEMATECH. (2015). e-Handbook of statistical methods. National Institutes of Standards and Technologies (NIST), US. http://www.itl.nist.gov/div898/handbook/
Noori, R., Abdoli, M., Jalili Ghazizade, M., & Samieifard, R. (2009). Comparison of Neural Network and Principal Component-Regression Analysis to predict the solid waste generation in Tehran. Iranian Journal of Public Health, 38(1), 74–84.
Noori, R., Karbassi, A., & Salman Sabahi, M. (2010a). Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction. Journal of Environmental Management, 91(3), 767–771. https://doi.org/10.1016/j.jenvman.2009.10.007
Noori, R., Khakpour, A., Omidvar, B., & Farokhnia, A. (2010b). Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Systems with Applications, 37(8), 5856–5862. https://doi.org/10.1016/j.eswa.2010.02.020
Noori, R., Karbassi, A. R., Mehdizadeh, H., Vesali‐Naseh, M., & Sabahi, M. S. (2011). A framework development for predicting the longitu-dinal dispersion coefficient in natural streams using an artificial neural network. Environmental Progress & Sustainable Energy, 30(3), 439–449. https://doi.org/10.1002/ep.10478
Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: A randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling, 154(1–2), 135–150. https://doi.org/10.1016/S0304-3800(02)00064-9
Olden, J. D., Joy, M. K., & Death, R. G. (2004). An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data. Ecological Modelling, 178(3–4), 389–397. https://doi.org/10.1016/j.ecolmodel.2004.03.013
Ortiz-Rodriguez, O. O., Rivera-Alarcon, H. U., & Villamizar-Gallardo, R. A. (2018). Evaluation of municipal solid waste by means of life cycle assessment: Case study in the south-western region of the department of Norte de Santander, Colombia. Environmental Engineering and Management Journal, 17(3), 611–619. https://doi.org/10.30638/eemj.2018.062
Owusu-Sekyere, E., Harris, E., & Bonyah, E. (2013). Forecasting and planning for solid waste generation in the Kumasi Metropolitan Area of Ghana: An ARIMA time series approach. International Journal of Sciences, 2(04), 69–83. https://ideas.repec.org/a/adm/journl/v2y2013i4p69-83.html
Radaei, E., Moghaddam, M. R. A., & Arami, M. (2017). Application of artificial neural network on modeling of reactive blue 19 removal by modified pomegranate residual. Environmental Engineering and Management Journal, 16(9), 2113–2122. https://doi.org/10.30638/eemj.2017.218
Ranganathan, A. (2004). The Levenberg-Marquardt algorithm. http://ananth.in/docs/lmtut.pdf
Rimaitytė, I., Ruzgas, T., Denafas, G., Račys, V., & Martuzevicius, D. (2012). Application and evaluation of forecasting methods for municipal solid waste generation in an eastern-European city. Waste Management & Research, 30(1), 89–98. https://doi.org/10.1177/0734242X10396754
Romanian National Plan Waste. (2018). Romanian National Waste Management Plan. http://www.mmediu.ro/app/webroot/uploads/files/2018-01-10_MO_11_bis.pdf
Romanian National Strategy Waste. (2013). Romanian National Waste Management Strategy 2014–2020. http://mmediu.ro/new/wp-content/uploads/2014/01/NationalWasteStrategy.pdf
Shahabi, H., Khezri, S., Ahmad, B. B., & 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(2), 336–343.
Shamshiry, E., Mokhtar, M., Abdulai, A.-M., Komoo, I., & Yahaya, N. (2014). Combining artificial neural network- genetic algorithm and response surface method to predict waste generation and optimize cost of solid waste collection and transportation process in Langkawi Is-land, Malaysia. Malaysian Journal of Science, 33(2), 118–140. https://doi.org/10.22452/mjs.vol33no2.1
Shan, C. S. (2010). Projecting municipal solid waste: The case of Hong Kong SAR. Resources Conservation and Recycling, 54(11), 759–768. https://doi.org/10.1016/j.resconrec.2009.11.012
Shen, H.-Y., & Chang, L.-C. (2013). Online multistep-ahead inundation depth forecasts by recurrent NARX networks. Hydrology and Earth System Sciences, 17(3), 935–945. https://doi.org/10.5194/hess-17-935-2013
Simion, I. M., Fortuna, M. E., Bonoli, A., & Gavrilescu, M. (2012). Comparing environmental impacts of natural inert and recycled construc-tion and demolition waste processing using LCA. Journal of Environmental Engineering and Landscape Management, 21(4), 273–287. https://doi.org/10.3846/16486897.2013.852558
Singh, D., & Satija, A. (2016). Municipal solid waste generation forecasting for Faridabad City located in Haryana State, India. In Advances in intelligent systems and computing: Vol. 437. Proceedings of Fifth International Conference on Soft Computing for Problem Solving (pp. 285–292). https://doi.org/10.1007/978-981-10-0451-3_27
Song, J., & He, J. (2014). A multistep chaotic model for municipal solid waste generation prediction. Environmental Engineering Science, 31(8), 461–468. https://doi.org/10.1089/ees.2014.0031
Suliman, A. H. A., & Darus, I. Z. M. (2019). Semi-distributed neural network models for streamflow prediction in a small catchment Pinang. Environmental Engineering and Management Journal, 18(2), 535–544. https://doi.org/10.30638/eemj.2019.050
Vu, H. L., Ng, K. T. W., & Bolingbroke, D. (2019). Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models. Waste Management, 84, 129–140. https://doi.org/10.1016/j.wasman.2018.11.038
Wu, Z., Fan, J., Gao, Y., Shang, H., & Song, H. (2019). Study on prediction model of space-time distribution of air pollutants based on artifi-cial neural network. Environmental Engineering and Management Journal, 18(7), 1575–1590. https://doi.org/10.30638/eemj.2019.148
Younes, M. K., Nopiah, Z. M., Ahmad Basri, N. E., Basri, H., Abushammala, M. F. M., & Maulud, K. N. A. (2015). Prediction of municipal solid waste generation using nonlinear autoregressive network. Environmental Monitoring and Assessment, 187, 753. https://doi.org/10.1007/s10661-015-4977-5
Yu, X., Chen, Z., & Qi, L. (2019). Comparative study of SARIMA and NARX models in predicting the incidence of schistosomiasis in China. Mathematical Biosciences and Engineering, 16(4), 2266–2276. https://doi.org/10.3934/mbe.2019112
Zade, M. J. G., & Noori, R. (2008). Prediction of municipal solid waste generation by use of artificial neural network: A case study of Mash-had. International Journal of Environmental Research, 2(1), 13–22. https://tspace.library.utoronto.ca/bitstream/1807/49358/1/er08002.pdf
Zounemat-Kermani, M., Stephan, D., & Hinkelmann, R. (2019). Multivariate NARX neural network in prediction gaseous emissions within the influent chamber of wastewater treatment plants. Atmospheric Pollution Research, 10(6), 1812–1822. https://doi.org/10.1016/j.apr.2019.07.013