Revolutionizing municipal solid waste management (MSWM) with machine learning as a clean resource: Opportunities, challenges and solutions
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[1] Dan Liu,et al. Multi-agent quantum-inspired deep reinforcement learning for real-time distributed generation control of 100% renewable energy systems , 2023, Eng. Appl. Artif. Intell..
[2] Fan Wu,et al. Exploring Key Components of Municipal Solid Waste in Prediction of Moisture Content in Different Functional Areas Using Artificial Neural Network , 2022, Sustainability.
[3] Tonni Agustiono Kurniawan,et al. Strengthening waste recycling industry in Malang (Indonesia): Lessons from waste management in the era of Industry 4.0 , 2022, Journal of Cleaner Production.
[4] L. M. Queiroz,et al. Improving circularity in municipal solid waste management through machine learning in Latin America and the Caribbean , 2022, Sustainable Chemistry and Pharmacy.
[5] Ufuk Derecia,et al. The applications of multiple route optimization heuristics and meta-heuristic algorithms to solid waste transportation: A case study in Turkey , 2022, Decision Analytics Journal.
[6] Jia-Hong Kuo,et al. Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai. , 2022, Chemosphere.
[7] R. Kahhat,et al. Reviewing the influence of sociocultural, environmental and economic variables to forecast municipal solid waste (MSW) generation , 2022, Sustainable Production and Consumption.
[8] Zheng‐Hong Luo,et al. Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors , 2022, Industrial & Engineering Chemistry Research.
[9] Mueen Uddin,et al. Waste Classification for Sustainable Development Using Image Recognition with Deep Learning Neural Network Models , 2022, Sustainability.
[10] Huijuan Dong,et al. Machine learning based prediction for China's municipal solid waste under the shared socioeconomic pathways. , 2022, Journal of environmental management.
[11] Qinqin Fan,et al. Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method , 2022, Mathematics.
[12] M. Pires,et al. Analysis of solid waste management scenarios using the WARM model: Case study , 2022, Journal of Cleaner Production.
[13] Youcai Zhao,et al. Toward smarter management and recovery of municipal solid waste: A critical review on deep learning approaches , 2022, Journal of Cleaner Production.
[14] Kai Li,et al. Impact of information timeliness and richness on public engagement on social media during COVID-19 pandemic: An empirical investigation based on NLP and machine learning , 2022, Decision Support Systems.
[15] M. Taki,et al. Machine learning models for prediction the higher heating value (HHV) of municipal solid waste (MSW) for waste-to-energy evaluation , 2022, Case Studies in Thermal Engineering.
[16] R. Suman,et al. Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability , 2022, Sustainable Operations and Computers.
[17] A. Olabi,et al. Optimal operating parameter determination based on fuzzy logic modeling and marine predators algorithm approaches to improve the methane production via biomass gasification , 2022, Energy.
[18] L. Joshi,et al. Internet of things and machine learning‐based approaches in the urban solid waste management: Trends, challenges, and future directions , 2021, Expert Syst. J. Knowl. Eng..
[19] Mauro Coccoli,et al. A cloud-based cognitive computing solution with interoperable applications to counteract illegal dumping in smart cities , 2021, Multim. Tools Appl..
[20] M. W. Anjum,et al. Anaerobic digestion of sewage sludge for biogas & biohydrogen production: State-of-the-art trends and prospects , 2022, Fuel.
[21] Machine Learning Tools, Algorithms, and Techniques in Retail Business Operations: Consumer Perceptions, Expectations, and Habits , 2022, Journal of Self-Governance and Management Economics.
[22] Siming You,et al. Machine learning methods for modelling the gasification and pyrolysis of biomass and waste , 2021, Renewable and Sustainable Energy Reviews.
[23] Guanyi Chen,et al. Fast identification and characterization of residual wastes via laser-induced breakdown spectroscopy and machine learning , 2021 .
[24] Raheel Anjum,et al. Technologies for municipal solid waste management: Current status, challenges, and future perspectives. , 2021, Chemosphere.
[25] Reza Tavakkoli-Moghaddam,et al. Sustainable vehicle routing problem for coordinated solid waste management , 2021 .
[26] Akhilesh Kumar Srivastava,et al. Machine Learning and IoT-Based Waste Management Model , 2021, Comput. Intell. Neurosci..
[27] Xiangwen Wang,et al. Recyclable waste image recognition based on deep learning , 2021 .
[28] R. Zhao,et al. Application of machine learning algorithms in municipal solid waste management: A mini review , 2021, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.
[29] Ahmad Mohaddespour,et al. Municipal solid waste-to-energy processing for a circular economy in New Zealand , 2021, Renewable and Sustainable Energy Reviews.
[30] Gopalakrishnan Kumar,et al. Development of machine learning - based models to forecast solid waste generation in residential areas: A case study from Vietnam , 2021 .
[31] Eylem Asmatulu,et al. Applying machine learning approach in recycling , 2021, Journal of Material Cycles and Waste Management.
[32] Marco F. Huber,et al. A Survey on the Explainability of Supervised Machine Learning , 2020, J. Artif. Intell. Res..
[33] Mustafa Cakir,et al. The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system , 2020, Comput. Ind. Eng..
[34] Aman Kumar,et al. Artificial Intelligence Models for Forecasting of Municipal Solid Waste Generation , 2021, Soft Computing Techniques in Solid Waste and Wastewater Management.
[35] Hailong Li,et al. Machine learning aided bio-oil production with high energy recovery and low nitrogen content from hydrothermal liquefaction of biomass with experiment verification , 2021 .
[36] M. Shahbaz,et al. Agro-industrial residue gasification feasibility in captive power plants: A South-Asian case study , 2021 .
[37] Jie Li,et al. Multi-task prediction and optimization of hydrochar properties from high-moisture municipal solid waste: Application of machine learning on waste-to-resource , 2021 .
[38] S. Yusup,et al. Integrated adsorption steam gasification for enhanced hydrogen production from palm waste at bench scale plant , 2020 .
[39] Yuzo Iano,et al. An Overview of the Machine Learning Applied in Smart Cities , 2020, Smart Cities: A Data Analytics Perspective.
[40] Tareq Al-Ansari,et al. Air catalytic biomass (PKS) gasification in a fixed-bed downdraft gasifier using waste bottom ash as catalyst with NARX neural network modelling , 2020, Comput. Chem. Eng..
[41] R. Shegog,et al. Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine , 2020, JAMA network open.
[42] Hassan Basri,et al. Waste collection route optimisation model for linking cost saving and emission reduction to achieve sustainable development goals , 2020 .
[43] Samir B. Patel,et al. Heart Disease Prediction using Machine Learning Techniques , 2020, SN Computer Science.
[44] Qasim Zeeshan,et al. Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 , 2020, Sustainability.
[45] Hao-nan Guo,et al. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review. , 2020, Bioresource technology.
[46] Murad A. Rassam,et al. A Financial Fraud Detection Model Based on LSTM Deep Learning Technique , 2020 .
[47] Maximiliano Cubillos,et al. Multi-site household waste generation forecasting using a deep learning approach. , 2020, Waste management.
[48] B. Dubey,et al. Challenges, opportunities, and innovations for effective solid waste management during and post COVID-19 pandemic , 2020, Resources, Conservation and Recycling.
[49] Chuanbin Zhou,et al. Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method. , 2020, Environmental science & technology.
[50] Adam Glowacz,et al. IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System for Smart Cities , 2020 .
[51] Teresa Pamuła,et al. Application of deep learning object classifier to improve e-waste collection planning. , 2020, Waste management.
[52] Shabir Ahmad,et al. Optimal Route Recommendation for Waste Carrier Vehicles for Efficient Waste Collection: A Step Forward Towards Sustainable Cities , 2020, IEEE Access.
[53] A. R. Abdul Rajak,et al. Automatic waste detection by deep learning and disposal system design , 2020 .
[54] Arash Bazyar,et al. An integrated approach to the selection of municipal solid waste landfills through GIS, K-Means and multi-criteria decision analysis. , 2020, Environmental research.
[55] C. Mukherjee,et al. A review on municipal solid waste-to-energy trends in the USA , 2020, Renewable and Sustainable Energy Reviews.
[56] Fang He,et al. Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach , 2020, Transportation Research Part C: Emerging Technologies.
[57] Corey J. Nolet,et al. Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence , 2020, Inf..
[58] Sirkka-Liisa Jämsä-Jounela,et al. Industry 4.0 based process data analytics platform: A waste-to-energy plant case study , 2020, International Journal of Electrical Power & Energy Systems.
[59] Shehzad Ashraf Chaudhry,et al. Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends , 2020, Symmetry.
[60] Denis Kleyko,et al. An Automated Machine Learning Approach for Smart Waste Management Systems , 2020, IEEE Transactions on Industrial Informatics.
[61] Hatem Rmili,et al. An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model , 2020, IEEE Access.
[62] Manoj Kumar Tiwari,et al. A review of leakage detection strategies for pressurised pipeline in steady-state , 2020 .
[63] Amit Kumar Tyagi,et al. Artificial Intelligence and Machine Learning Algorithms , 2020 .
[64] O. Olatunji,et al. Property-based biomass feedstock grading using k-Nearest Neighbour technique , 2020 .
[65] Yang Liu,et al. Barriers to smart waste management for a circular economy in China , 2019, Journal of Cleaner Production.
[66] Daniel K Hartline,et al. t-Distributed Stochastic Neighbor Embedding (t-SNE): A tool for eco-physiological transcriptomic analysis. , 2019, Marine genomics.
[67] Jesper E. van Engelen,et al. A survey on semi-supervised learning , 2019, Machine Learning.
[68] S. S. Lee,et al. Solid waste management: Scope and the challenge of sustainability , 2019, Journal of Cleaner Production.
[69] C. Srinilta,et al. Municipal Solid Waste Segregation with CNN , 2019, 2019 5th International Conference on Engineering, Applied Sciences and Technology (ICEAST).
[70] M. Olazar,et al. Selecting Monitoring Variables in the Manual Composting of Municipal Solid Waste Based on Principal Component Analysis , 2019 .
[71] R Sarc,et al. Digitalisation and intelligent robotics in value chain of circular economy oriented waste management - A review. , 2019, Waste management.
[72] K. Ngiam,et al. Big data and machine learning algorithms for health-care delivery. , 2019, The Lancet. Oncology.
[73] Sotiris Karabetsos,et al. A Review of Machine Learning and IoT in Smart Transportation , 2019, Future Internet.
[74] Hoang Lan Vu,et al. Assessment of waste characteristics and their impact on GIS vehicle collection route optimization using ANN waste forecasts. , 2019, Waste management.
[75] Santiago Grijalva,et al. A Review of Reinforcement Learning for Autonomous Building Energy Management , 2019, Comput. Electr. Eng..
[76] Tamás Bányai,et al. Optimization of Municipal Waste Collection Routing: Impact of Industry 4.0 Technologies on Environmental Awareness and Sustainability , 2019, International journal of environmental research and public health.
[77] Germán G. Creamer,et al. Machine Learning in Energy Economics and Finance: A Review , 2018, Energy Economics.
[78] Dhiya Al-Jumeily,et al. A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science , 2019, Unsupervised and Semi-Supervised Learning.
[79] Vidhya Shree,et al. The Use of Modern Technology in Smart Waste Management and Recycling: Artificial Intelligence and Machine Learning , 2019, Recent Advances in Computational Intelligence.
[80] Ammar Ismael Kadhim. Survey on supervised machine learning techniques for automatic text classification , 2019, Artificial Intelligence Review.
[81] Krist V. Gernaey,et al. Resource recovery from organic solid waste using hydrothermal processing: Opportunities and challenges , 2018, Renewable and Sustainable Energy Reviews.
[82] Constantine E. Kontokosta,et al. Using machine learning and small area estimation to predict building-level municipal solid waste generation in cities , 2018, Comput. Environ. Urban Syst..
[83] Mingjiang Ni,et al. Life cycle assessment of pyrolysis, gasification and incineration waste-to-energy technologies: Theoretical analysis and case study of commercial plants. , 2018, The Science of the total environment.
[84] Biswajeet Pradhan,et al. Developing robust arsenic awareness prediction models using machine learning algorithms. , 2018, Journal of environmental management.
[85] Farid Bensebaa,et al. Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. , 2017, Waste management.
[86] J. Gardy,et al. Waste to Energy: A Case Study of Madinah City , 2017 .
[87] Y. Uemura,et al. Catalytic Pyrolysis Of Botryococcus Braunii (microalgae) Over Layered and Delaminated Zeolites For Aromatic Hydrocarbon Production , 2017 .
[88] M. Goddard. The EU General Data Protection Regulation (GDPR): European Regulation that has a Global Impact , 2017 .
[89] Charlotte Scheutz,et al. Statistical analysis of solid waste composition data: Arithmetic mean, standard deviation and correlation coefficients. , 2017, Waste management.
[90] Jianhua Yan,et al. Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. , 2017, Waste management.
[91] Osisanwo F.Y,et al. Supervised Machine Learning Algorithms: Classification and Comparison , 2017 .
[92] S. Yusup,et al. Catalytic consequences of micropore topology on biomass pyrolysis vapors over shape selective zeolites , 2017 .
[93] Burcu Oralhan,et al. Smart City Application: Internet of Things (IoT) Technologies Based Smart Waste Collection Using Data Mining Approach and Ant Colony Optimization , 2017 .
[94] M. Jeguirim,et al. Thermochemical conversion of waste tyres—a review , 2017, Environmental Science and Pollution Research.
[95] E. Dahlquist,et al. Waste Biomass Gasification Based off-grid Electricity Generation: A Case Study in Pakistan , 2016 .
[96] Maryam Abbasi,et al. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. , 2016, Waste management.
[97] N. J. Raju,et al. Assessment of pollution potential of leachate from the municipal solid waste disposal site and its impact on groundwater quality, Varanasi environs, India , 2016, Arabian Journal of Geosciences.
[98] Sama Azadi,et al. Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran. , 2016, Waste management.
[99] M D Bovea,et al. Attitude towards the incorporation of the selective collection of biowaste in a municipal solid waste management system. A case study. , 2014, Waste management.
[100] Stefan Salhofer,et al. Municipal solid waste recycling and the significance of informal sector in urban China , 2014, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.
[101] Babak Omidvar,et al. Results uncertainty of support vector machine and hybrid of wavelet transform‐support vector machine models for solid waste generation forecasting , 2014 .
[102] Maryam Mokhtari,et al. Prediction of the compression ratio for municipal solid waste using decision tree , 2014, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.
[103] Erik Dahlquist,et al. System analysis of dry black liquor gasification based synthesis gas production comparing oxygen and air blown gasification systems , 2013 .
[104] E. Dahlquist,et al. Energy conversion performance of black liquor gasification to hydrogen production using direct causticization with CO(2) capture. , 2012, Bioresource technology.
[105] Sunil Kumar,et al. Assessment of the status of municipal solid waste management in metro cities, state capitals, class I cities, and class II towns in India: an insight. , 2009, Waste management.
[106] Paolo Viotti,et al. Genetic algorithms as a promising tool for optimisation of the MSW collection routes , 2003, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.
[107] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[108] A. Parasuraman,et al. Technology Readiness Index (Tri) , 2000 .
[109] Ian Barclay,et al. New Product Development From Past Research to Future Applications , 1998 .