0
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Use of Socio-economic, Climatic, and Land use Land Cover Patterns in Solid Waste Forecasting with Integrated Gradient LSTNet Based Model in Lomé, Togo

ORCID Icon, , &
Article: 2387504 | Received 09 Feb 2024, Accepted 25 Jul 2024, Published online: 05 Aug 2024

References

  • Abbasi, M., and M. A. Abduli. 2013. Forecasting municipal solid waste generation by hybrid support vector machine and partial least square model. International Journal of Environmental Research 7 (1):27–21.
  • Abbasi, M., M. A. Abduli, B. Omidvar, and A. Baghvand. 2014. Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environmental Progress & Sustainable Energy 33 (1):220–28. doi:10.1002/EP.11747.
  • Abbasi, M., and A. El Hanandeh. 2016. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management 56:13–22. doi:10.1016/J.WASMAN.2016.05.018.
  • Abdulkareem, K. H., M. A. Subhi, M. A. Mohammed, M. Aljibawi, J. Nedoma, R. Martinek, M. Deveci, W. L. Shang, and W. Pedrycz. 2024. A manifold intelligent decision system for fusion and benchmarking of deep waste-sorting models. Engineering Applications of Artificial Intelligence 132:107926. doi:10.1016/J.ENGAPPAI.2024.107926.
  • Adusei, K. K., K. T. W. Ng, N. Karimi, T. S. Mahmud, and E. Doolittle. 2022. Modeling of municipal waste disposal behaviors related to meteorological seasons using recurrent neural network LSTM models. Ecological Informatics 72:101925. doi:10.1016/J.ECOINF.2022.101925.
  • Ali Abdoli, M., M. Falah Nezhad, R. Salehi Sede, and S. Behboudian. 2012. Longterm forecasting of solid waste generation by the artificial neural networks. Environmental Progress & Sustainable Energy 31 (4):628–36. doi:10.1002/EP.10591.
  • Al-Mashhadani, I. B. 2023. Waste material classification using performance evaluation of deep learning models. Journal of Intelligent Systems 32 (1). doi:10.1515/jisys-2023-0064.
  • Brown, C. F., S. P. Brumby, B. Guzder-Williams, T. Birch, S. B. Hyde, J. Mazzariello, W. Czerwinski, V. J. Pasquarella, R. Haertel, S. Ilyushchenko, et al. 2022. Dynamic world, near real-time global 10 m land use land cover mapping. Scientific Data 2022 9:1, 9 (1):1–17. doi:10.1038/s41597-022-01307-4.
  • Cai, J., K. Xu, Y. Zhu, F. Hu, and L. Li. 2020. Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Applied Energy 262:114566. doi:10.1016/j.apenergy.2020.114566.
  • Choi, H., C. Jung, T. Kang, H. J. Kim, and I. Y. Kwak. 2022. Explainable time-series prediction using a residual network and gradient-based methods. Institute of Electrical and Electronics Engineers Access 10:108469–82. doi:10.1109/ACCESS.2022.3213926.
  • Chung, J., C. Gulcehre, K. Cho, and Y. Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv preprint arXiv:1412.3555v1:1–9. doi:10.48550/arXiv.1412.3555.
  • Cubillos, M. 2020. Multi-site household waste generation forecasting using a deep learning approach. Waste Management 115:8–14. doi:10.1016/J.WASMAN.2020.06.046.
  • Dunkel, J., D. Dominguez, Ó. G. Borzdynski, and Á. Sánchez. 2022. Solid waste analysis using open-access socio-economic data. Sustainability (Switzerland) 14 (3). doi:10.3390/su14031233.
  • Friedman, J. H. 2001. Greedy function approximation: A gradient boosting machine. Annals of Statistics 29 (5):1189–232. doi:10.1214/aos/1013203451.
  • Friedman, J. H. 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis 38 (4):367–78. doi:10.1016/S0167-9473(01)00065-2.
  • Géron, A. 2017. Hands-on machine learning with scikit-learn and TensorFlow, ed. T. Nicole, 1st ed. Sebastopol, CA, USA: O’Reilly Media, Inc.
  • Goh, G. S. W., S. Lapuschkin, L. Weber, W. Samek, & A. Binder. 2020. Understanding integrated gradients with smoothtaylor for deep neural network attribution. Proceedings - International Conference on Pattern Recognition, 4949–56. doi:10.1109/ICPR48806.2021.9413242.
  • Han, Z., Y. Liu, M. Zhong, G. Shi, Q. Li, D. Zeng, Y. Zhang, Y. Fei, and Y. Xie. 2018. Influencing factors of domestic waste characteristics in rural areas of developing countries. Waste Management 72:45–54. doi:10.1016/J.WASMAN.2017.11.039.
  • Hoang, M. G., T. Fujiwara, S. T. Pham Phu, and K. T. Nguyen Thi. 2017. Predicting waste generation using Bayesian model averaging. Global Journal of Environmental Science and Management 3 (4):385–402. doi:10.22034/gjesm.2017.03.04.005.
  • Hochreiter, S., and J. Schmidhuber. 1997. Long short-term memory. Neural Computation 9 (8):1735–80. doi:10.1162/NECO.1997.9.8.1735.
  • Hockett, D., D. J. Lober, and K. Pilgrim. 1995. Determinants of per capita municipal solid waste generation in the Southeastern United States. The Journal of Environmental Management 45 (3):205–17. doi:10.1006/jema.1995.0069.
  • Izquierdo-Horna, L., R. Kahhat, and I. Vázquez-Rowe. 2022. Reviewing the influence of sociocultural, environmental and economic variables to forecast municipal solid waste (MSW) generation. In Sustainable production and consumption, vol. 33, 809–19. Elsevier B.V. doi:10.1016/j.spc.2022.08.008.
  • Johnson, N. E., O. Ianiuk, D. Cazap, L. Liu, D. Starobin, G. Dobler, and M. Ghandehari. 2017. Patterns of waste generation: A gradient boosting model for short-term waste prediction in New York City. Waste Management 62:3–11. doi:10.1016/J.WASMAN.2017.01.037.
  • Kumar, N. M., M. A. Mohammed, K. H. Abdulkareem, R. Damasevicius, S. A. Mostafa, M. S. Maashi, and S. S. Chopra. 2021. Artificial intelligence-based solution for sorting COVID related medical waste streams and supporting data-driven decisions for smart circular economy practice. Process Safety and Environmental Protection 152:482–94. doi:10.1016/J.PSEP.2021.06.026.
  • Lai, G., W. C. Chang, Y. Yang, and H. Liu. 2018. Modeling long- and short-term temporal patterns with deep neural networks. 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, 95–104. doi:10.1145/3209978.3210006.
  • Lin, K., Y. Zhao, L. Tian, C. Zhao, M. Zhang, and T. Zhou. 2021. Estimation of municipal solid waste amount based on one-dimension convolutional neural network and long short-term memory with attention mechanism model: A case study of Shanghai. Science of the Total Environment 791:148088. doi:10.1016/J.SCITOTENV.2021.148088.
  • Lu, W., W. Huo, H. Gulina, and C. Pan. 2022. Development of machine learning multi-city model for municipal solid waste generation prediction. Frontiers of Environmental Science & Engineering 16 (9):1–10. doi:10.1007/s11783-022-1551-6.
  • Mohammed, M. A., M. J. Abdulhasan, N. M. Kumar, K. H. Abdulkareem, S. A. Mostafa, M. S. Maashi, L. S. Khalid, H. S. Abdulaali, and S. S. Chopra. 2023. Automated waste-sorting and recycling classification using artificial neural network and features fusion: A digital-enabled circular economy vision for smart cities. Multimedia Tools & Applications 82 (25):39617–32. doi:10.1007/s11042-021-11537-0.
  • Niu, D., F. Wu, S. Dai, S. He, and B. Wu. 2021. Detection of long-term effect in forecasting municipal solid waste using a long short-term memory neural network. Journal of Cleaner Production 290:125187. doi:10.1016/j.jclepro.2020.125187.
  • Rahman, A. U., M. Saeed, M. A. Mohammed, K. H. Abdulkareem, J. Nedoma, and R. Martinek. 2023. Fppsv-NHSS: Fuzzy parameterized possibility single valued neutrosophic hypersoft set to site selection for solid waste management. Applied Soft Computing 140:110273. doi:10.1016/J.ASOC.2023.110273.
  • Shahabi, H., S. Khezri, B. Ahmad, and H. Zabihi. 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):336–43.
  • Sun, Q., and Z. Ge. 2021. Deep learning for industrial KPI prediction: When ensemble learning meets semi-supervised data. IEEE Transactions on Industrial Informatics 17 (1):260–69. doi:10.1109/TII.2020.2969709.
  • Tseng, F. M., H. C. Yu, and G. H. Tzeng. 2002. Combining neural network model with seasonal time series ARIMA model. Technological Forecasting & Social Change 69 (1):71–87. doi:10.1016/S0040-1625(00)00113-X.
  • Vu, H. L., K. T. W. Ng, and D. Bolingbroke. 2019. Time-lagged effects of weekly climatic and socio-economic factors on ANN municipal yard waste prediction models. Waste Management 84:129–40. doi:10.1016/J.WASMAN.2018.11.038.
  • Wang, D., Y. A. Yuan, Y. Ben, H. Luo, and H. Guo. 2022. Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai, China. Environmental Science and Pollution Research 29 (46):69472–90. doi:10.1007/s11356-022-20438-0.
  • Wang, H., W. Fu, C. Li, B. Li, C. Cheng, Z. Gong, Y. Hu, and H. Dinçer. 2023. Short-term wind and solar power prediction based on feature selection and improved long- and short-term time-series networks. Mathematical Problems in Engineering 1–7. doi:10.1155/2023/7745650.
  • Xu, A., H. Chang, Y. Xu, R. Li, X. Li, and Y. Zhao. 2021. Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. In Waste management, vol. 124, 385–402. Elsevier Ltd. doi:10.1016/j.wasman.2021.02.029.