Introducing an evolutionary-decomposition model for prediction of municipal solid waste flow: application of intrinsic time-scale decomposition algorithm

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/https://doi.org/10.1016/j.wasman.2016.05.018
   PubMed Web of Science ®Google Scholar
• Abbasi, M., Rastgoo, M. N., & Nakisa, B. (2019). Monthly and seasonal modeling of municipal waste generation using radial basis function neural network. Environmental Progress and Sustainable Energy, 38(3), e13033. https://doi.org/https://doi.org/10.1002/ep.13033
   Web of Science ®Google Scholar
• Abdulredha, M., Abdulridha, A., Shubbar, A. A., Alkhaddar, R., Kot, P., & Jordan, D. (2020). Estimating municipal solid waste generation from service processions during the Ashura religious event. IOP Conference Series: Materials Science and Engineering, 671(1), 012075. https://doi.org/https://doi.org/10.1088/1757-899X/671/1/012075
   Google Scholar
• Ali Abdoli, M., Falah Nezhad, M., Salehi Sede, R., & Behboudian, S. (2012). Longterm forecasting of solid waste generation by the artificial neural networks. Environmental Progress & Sustainable Energy, 31(4), 628–636. https://doi.org/https://doi.org/10.1002/ep.10591
   Web of Science ®Google Scholar
• Araiza-Aguilar, J. A., Rojas-Valencia, M. N., & Aguilar-Vera, R. A. (2020). Forecast generation model of municipal solid waste using multiple linear regression. Global Journal of Environmental Science and Management, 6(1), 1–14. https://doi.org/https://doi.org/10.22034/gjesm.2020.01.01
   Google Scholar
• Arena, U., Mastellone, M. L., & Perugini, F. (2003). The environmental performance of alternative solid waste management options: A life cycle assessment study. Chemical Engineering Journal, 96(1–3), 207–222. https://doi.org/https://doi.org/10.1016/j.cej.2003.08.019
   Web of Science ®Google Scholar
• Ashrafian, A., Shokri, F., Taheri Amiri, M. J., Yaseen, Z. M., & Rezaie-Balf, M. (2020). Compressive strength of foamed cellular lightweight concrete simulation: New development of hybrid artificial intelligence model. Construction and Building Materials, 230, 117048. https://doi.org/https://doi.org/10.1016/j.conbuildmat.2019.117048
   Web of Science ®Google Scholar
• Azarmi, S. L., Oladipo, A. A., Vaziri, R., & Alipour, H. (2018). Comparative modelling and artificial neural network inspired prediction of waste generation rates of hospitality industry: The case of North Cyprus. Sustainability (Switzerland), 10(9), 2965. https://doi.org/https://doi.org/10.3390/su10092965
   Web of Science ®Google Scholar
• Bebb, J., & Kersey, J. (2003). Potential impacts of climate change on waste management. Environment Agency. https://assets.publishing.service.gov.uk
   Google Scholar
• Benítez, S. O., Lozano-Olvera, G., Morelos, R. A., & Vega, C. A. (2008). Mathematical modeling to predict residential solid waste generation. Waste Management, 28(Suppl. 1), 7–13. https://doi.org/https://doi.org/10.1016/j.wasman.2008.03.020
   Google Scholar
• Bokde, N. D., Yaseen, Z. M., & Andersen, G. B. (2020). ForecastTB – An R package as a test-bench for time series forecasting – Application of wind speed and solar radiation modeling. Energies, 13(10), 2578. https://doi.org/https://doi.org/10.3390/en13102578
   Web of Science ®Google Scholar
• Cevik, S., Cakmak, R., & Altas, I. H. (2017). A day ahead hourly solar radiation forecasting by artificial neural networks: A case study for Trabzon province. 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), Malatya, Turkey. https://doi.org/https://doi.org/10.1109/idap.2017.8090223
   Google Scholar
• Chhay, L., Reyad, M. A. H., Suy, R., Islam, M. R., & Mian, M. M. (2018). Municipal solid waste generation in China: Influencing factor analysis and multi-model forecasting. Journal of Material Cycles and Waste Management, 20(3), 1761–1770. https://doi.org/https://doi.org/10.1007/s10163-018-0743-4
   Web of Science ®Google Scholar
• Duan, N., Li, D., Wang, P., Ma, W., Wenga, T., Zhong, L., & Chen, G. (2020). Comparative study of municipal solid waste disposal in three Chinese representative cities. Journal of Cleaner Production, 254, 120134. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120134
   Web of Science ®Google Scholar
• El-Fadel, M., Findikakis, A. N., & Leckie, J. O. (1997). Environmental impacts of solid waste landfilling. Journal of Environmental Management, 50(1), 1–25. https://doi.org/https://doi.org/10.1006/jema.1995.0131
   Web of Science ®Google Scholar
• Fadaee, M., Mahdavi-Meymand, A., & Zounemat-Kermani, M. (2020). Seasonal short-term prediction of dissolved oxygen in rivers via nature-inspired algorithms. CLEAN – Soil, Air, Water, 48(2), 1900300. https://doi.org/https://doi.org/10.1002/clen.201900300
   Web of Science ®Google Scholar
• Feng, Y., Cui, N., Zhao, L., Hu, X., & Gong, D. (2016). Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China. Journal of Hydrology, 536, 376–383. https://doi.org/https://doi.org/10.1016/j.jhydrol.2016.02.053
   Web of Science ®Google Scholar
• Fernandez Martinez, R., Okariz, A., Ibarretxe, J., Iturrondobeitia, M., & Guraya, T. (2014). Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates. Computational Materials Science, 92, 102–113. https://doi.org/https://doi.org/10.1016/j.commatsci.2014.05.038
   Web of Science ®Google Scholar
• Ferreira, C. (2001). Gene expression programming in problem solving. Soft Computing in Industry – Recent Applications, 1996(7), 641–660. https://doi.org/https://doi.org/10.1007/978-1-4471-0123-9_54
   Google Scholar
• Fox, J. L. (2002, May). Comparison of a Monte Carlo calculation of the escape rate of C from Mars to that obtained using the exobase approximation. AGU Spring Meeting Abstracts (Vol. 2002, pp. SA41A-23).
   Google Scholar
• Frei, M. G., & Osorio, I. (2007). Intrinsic time-scale decomposition: Time–frequency–energy analysis and real-time filtering of non-stationary signals. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 463(2078), 321–342. https://doi.org/https://doi.org/10.1098/rspa.2006.1761
   Web of Science ®Google Scholar
• Ghaemi, A., Rezaie-Balf, M., Adamowski, J., Kisi, O., & Quilty, J. (2019). On the applicability of maximum overlap discrete wavelet transform integrated with MARS and M5 model tree for monthly pan evaporation prediction. Agricultural and Forest Meteorology, 278, 107647. https://doi.org/https://doi.org/10.1016/j.agrformet.2019.107647
   Web of Science ®Google Scholar
• Heddam, S., & Kisi, O. (2017). Extreme learning machines: A new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors. Environmental Science and Pollution Research, 11(8), 1–23. https://doi.org/https://doi.org/10.1007/s11356-017-9283-z
   Google Scholar
• Hoque, M., & Rahman, M. T. U. (2020). Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options. Journal of Cleaner Production, 256, 120387. https://doi.org/https://doi.org/10.1016/j.jclepro.2020.120387
   Web of Science ®Google Scholar
• Huang, G.-B., & Chen, L. (2006). Universial approximation using incremental constructive feedforward neural networks with random hidden nodes. Transactions on Neural Networks, 17(4), 879–892. https://doi.org/https://doi.org/10.1109/TNN.2006.875977
   PubMed Web of Science ®Google Scholar
• Iqbal, M. F., Feng, Q., Azim, I., Zhu, X., Yang, J., Javed, M. F., & Rauf, M. (2020). Prediction of mechanical properties of Green concrete incorporating waste foundry sand based on gene expression programming. Journal of Hazardous Materials, 384, 121322. https://doi.org/https://doi.org/10.1016/j.jhazmat.2019.121322
   PubMed Web of Science ®Google Scholar
• 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. https://doi.org/https://doi.org/10.1016/j.wasman.2017.11.057
   PubMed Web of Science ®Google Scholar
• Kisi, O., & Kilic, Y. (2015). An investigation on generalization ability of artificial neural networks and M5 model tree in modeling reference evapotranspiration. Theoretical and Applied Climatology, 6(10). https://doi.org/https://doi.org/10.1007/s00704-015-1582-z
   Google Scholar
• Kisi, O., & Parmar, K. S. (2016). Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution. Journal of Hydrology, 534, 104–112. https://doi.org/https://doi.org/10.1016/j.jhydrol.2015.12.014
   Web of Science ®Google Scholar
• Kisi, O., Parmar, K. S., Soni, K., & Demir, V. (2017). Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Quality, Atmosphere & Health, 10, 873–883. https://doi.org/https://doi.org/10.1007/s11869-017-0477-9
   Google Scholar
• Kisi, O., Sanikhani, H., & Cobaner, M. (2017). Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques. Theoretical and Applied Climatology, 129, 833–848. https://doi.org/https://doi.org/10.1007/s00704-016-1810-1
   Web of Science ®Google Scholar
• Koza, J. R. (2007). What is genetic programming (GP). How Genetic Programming Works.
   Google Scholar
• Kumar, A., Samadder, S. R., Kumar, N., & Singh, C. (2018). Estimation of the generation rate of different types of plastic wastes and possible revenue recovery from informal recycling. Waste Management, 79, 781–790. https://doi.org/https://doi.org/10.1016/j.wasman.2018.08.045
   PubMed Web of Science ®Google Scholar
• Liu, J., Li, Q., Gu, W., & Wang, C. (2019). The impact of consumption patterns on the generation of municipal solid waste in China: Evidences from provincial data. International Journal of Environmental Research and Public Health, 16(10), 1717. https://doi.org/https://doi.org/10.3390/ijerph16101717
   Web of Science ®Google Scholar
• Miche, Y., Sorjamaa, A., Bas, P., Simula, O., Jutten, C., & Lendasse, A. (2010). OP-OPELM: Optimally pruned extreme learning machine. IEEE Transactions on Neural Networks, 21(1), 158–162. https://doi.org/https://doi.org/10.1109/TNN.2009.2036259
   PubMed Web of Science ®Google Scholar
• Modaresi, F., Araghinejad, S., & Ebrahimi, K. (2018). A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resources Management, 32(1), 243–258. https://doi.org/https://doi.org/10.1007/s11269-017-1807-2
   Web of Science ®Google Scholar
• Mohammadi, K., Shamshirband, S., Petković, D., Yee, P. L., & Mansor, Z. (2016). Using ANFIS for selection of more relevant parameters to predict dew point temperature. Applied Thermal Engineering, 96, 311–319. https://doi.org/https://doi.org/10.1016/J.APPLTHERMALENG.2015.11.081
   Web of Science ®Google Scholar
• Mohammadpour, R., Shaharuddin, S., Chang, C. K., Zakaria, N. A., Ghani, A. A., & Chan, N. W. (2015). Prediction of water quality index in constructed wetlands using support vector machine. Environmental Science and Pollution Research, 22(8), 6208–6219. https://doi.org/https://doi.org/10.1007/s11356-014-3806-7
   PubMed Web of Science ®Google Scholar
• Mosavi, A., Ozturk, P., Vajda, I., Torabi, M., Varkonyi-Koczy, A., & Istvan, V. (2019). A hybrid machine learning approach for daily prediction of solar radiation design optimization of electric machines view project quantification of margins and uncertainties view project a hybrid machine learning approach for daily prediction of solar radiation. https://doi.org/https://doi.org/10.1007/978-3-319-99834-3_35
   Google Scholar
• Mozhiarasi, V., Raghul, R., Speier, C. J., Benish Rose, P. M., Weichgrebe, D., & Srinivasan, S. V. (2020). Composition analysis of major organic fractions of municipal solid waste generated from Chennai. In Sustainable waste management: Policies and case studies (pp. 143–152). https://doi.org/https://doi.org/10.1007/978-981-13-7071-7_13
   Google Scholar
• Naghibi, S. A., Pourghasemi, H. R., & Abbaspour, K. (2018). A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theoretical and Applied Climatology, 131(3–4), 967–984. https://doi.org/https://doi.org/10.1007/s00704-016-2022-4
   Web of Science ®Google Scholar
• Najafzadeh, M., & Zeinolabedini, M. (2019). Prognostication of waste water treatment plant performance using efficient soft computing models: An environmental evaluation. Measurement: Journal of the International Measurement Confederation, 138, 690–701. https://doi.org/https://doi.org/10.1016/j.measurement.2019.02.014
   Web of Science ®Google Scholar
• Nęcka, K., Szul, T., & Knaga, J. (2019). Identification and analysis of sets variables for of municipal waste management modelling. Geosciences, 9(11), 458. https://doi.org/https://doi.org/10.3390/geosciences9110458
   Web of Science ®Google Scholar
• Olejnik, S., Mills, J., & KesOPELMan, H. (2000). Using Wherry’s adjusted R2 and Mallow’s CP for model selection from all possible regressions. Journal of Experimental Education, 68(4), 365–380. https://doi.org/https://doi.org/10.1080/00220970009600643
   Web of Science ®Google Scholar
• Oliveira, V., Sousa, V., & Dias-Ferreira, C. (2019). Artificial neural network modelling of the amount of separately-collected household packaging waste. Journal of Cleaner Production, 210, 401–409. https://doi.org/https://doi.org/10.1016/j.jclepro.2018.11.063
   Web of Science ®Google Scholar
• Pan, Z., Chan, W. P., Veksha, A., Giannis, A., Dou, X., Wang, H., Lisak, G., & Lim, T. T. (2019). Thermodynamic analyses of synthetic natural gas production via municipal solid waste gasification, high-temperature water electrolysis and methanation. Energy Conversion and Management, 202, 112160. https://doi.org/https://doi.org/10.1016/j.enconman.2019.112160
   Google Scholar
• Şahin, M. (2013). Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data. International Journal of Remote Sensing, 34(21), 7508–7533. https://doi.org/https://doi.org/10.1080/01431161.2013.822597
   Web of Science ®Google Scholar
• Samal, B., Mani, S., & Madguni, O. (2020). Open dumping of waste and its impact on our water resources and health – A case of New Delhi, India. https://doi.org/https://doi.org/10.1007/978-981-15-0990-2_10
   Google Scholar
• Sattar, A. A., Elhakeem, M., Rezaie-Balf, M., Gharabaghi, B., & Bonakdari, H. (2019). Artificial intelligence models for prediction of the aeration efficiency of the stepped weir. Flow Measurement and Instrumentation, 65, 78–89. https://doi.org/https://doi.org/10.1016/j.flowmeasinst.2018.11.017
   Web of Science ®Google Scholar
• Shiri, J., Keshavarzi, A., Kisi, O., Karimi, S., & Iturraran-Viveros, U. (2017). Modeling soil bulk density through a complete data scanning procedure: Heuristic alternatives. Journal of Hydrology, 549, 592–602. https://doi.org/https://doi.org/10.1016/j.jhydrol.2017.04.035
   Web of Science ®Google Scholar
• Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Marti, P., Fard, A. F., & Landeras, G. (2013). Evaluation of different data management scenarios for estimating daily reference evapotranspiration. Hydrology Research, 44(6), 1058–1070. https://doi.org/https://doi.org/10.2166/nh.2013.154
   Web of Science ®Google Scholar
• Silva, L. J., da Santos, V. B., dos, I. F. S., Mensah, J. H. R., Gonçalves, A. T. T., & Barros, R. M. (2020). Incineration of municipal solid waste in Brazil: An analysis of the economically viable energy potential. Renewable Energy, 149, 1386–1394. https://doi.org/https://doi.org/10.1016/j.renene.2019.10.134
   Web of Science ®Google Scholar
• Soni, U., Roy, A., Verma, A., & Jain, V. (2019). Forecasting municipal solid waste generation using artificial intelligence models – A case study in India. SN Applied Sciences, 1(2), 162. https://doi.org/https://doi.org/10.1007/s42452-018-0157-x
   Web of Science ®Google Scholar
• Tenodi, S., Krčmar, D., Agbaba, J., Zrnić, K., Radenović, M., Ubavin, D., & Dalmacija, B. (2020). Assessment of the environmental impact of sanitary and unsanitary parts of a municipal solid waste landfill. Journal of Environmental Management, 258, 110019. https://doi.org/https://doi.org/10.1016/j.jenvman.2019.110019
   PubMed Web of Science ®Google Scholar
• Trang, P. T. T., Dong, H. Q., Toan, D. Q., Hanh, N. T. X., & Thu, N. T. (2017). The effects of socio-economic factors on household solid waste generation and composition: A case study in Thu Dau Mot, Vietnam. Energy Procedia, 107, 253–258. https://doi.org/https://doi.org/10.1016/j.egypro.2016.12.144
   Google Scholar
• 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/https://doi.org/10.1016/j.wasman.2018.11.038
   PubMed Web of Science ®Google Scholar
• Wei, C.-C. (2017). Predictions of surface solar radiation on tilted solar panels using machine learning models: A case study of Tainan city, Taiwan. Energies, 10(10), 1660. https://doi.org/https://doi.org/10.3390/en10101660
   Web of Science ®Google Scholar
• Wen, Z., Chen, C., Ai, N., Bai, W., Zhang, W., & Wang, Y. (2019). Environmental impact of carbon cross-media metabolism in waste management: A case study of municipal solid waste treatment systems in China. Science of the Total Environment, 674, 512–523. https://doi.org/https://doi.org/10.1016/j.scitotenv.2019.04.154
   PubMed Web of Science ®Google Scholar
• Zamani Sabzi, H., King, J. P., & Abudu, S. (2017). Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study. Expert Systems with Applications, 83, 145–163. https://doi.org/https://doi.org/10.1016/j.eswa.2017.04.039
   Web of Science ®Google Scholar
• Zeng, J., Wang, G., Zhang, F., & Ye, J. (2012). The de-noising algorithm based on intrinsic time-scale decomposition. Advanced Materials Research, 422, 347–352. https://doi.org/https://doi.org/10.4028/www.scientific.net/AMR.422.347
   Google Scholar
• Zeng, W., Ismail, S. A., & Pappas, E. (2020). '. Artificial Intelligence Review, 53, 3231–3253. https://doi.org/https://doi.org/10.1007/s10462-019-09761-0
   Google Scholar
• Zeng, W., Li, M., Yuan, C., Wang, Q., Liu, F., & Wang, Y. (2020). Identification of epileptic seizures in EEG signals using time-scale decomposition (ITD), discrete wavelet transform (DWT), phase space reconstruction (PSR) and neural networks. Artificial Intelligence Review, 53, 3059–3088. https://doi.org/https://doi.org/10.1007/s10462-019-09755-y
   Web of Science ®Google Scholar