Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automated Assessment of Municipal Solid Wastes Using a Hybrid Sine Cosine Algorithm-Based Neural Network
Municipal solid waste management has emerged in the recent few years in contemporary built environments due to the rapid increase in population and urbanization. Hence, this research aims at developing a hybrid sine cosine algorithm-based feed-forward artificial neural network model for forecasting waste quantities in Poland. In addition, the developed hybrid model is compared against the classical feed-forward artificial neural network model. The performance evaluation analysis is explored using the indicators of mean bias error, root-mean-squared error, Pearson correlation coefficient, Willmott’s index of agreement, and coefficient of efficiency. Test results illustrated that the developed hybrid feed-forward artificial neural network model trained using sine cosine algorithm significantly outperformed the classical neural network model. It can be argued that the developed model could assist decision-makers in the proper management of growing quantities of municipal solid wastes.
Automated Assessment of Municipal Solid Wastes Using a Hybrid Sine Cosine Algorithm-Based Neural Network
Municipal solid waste management has emerged in the recent few years in contemporary built environments due to the rapid increase in population and urbanization. Hence, this research aims at developing a hybrid sine cosine algorithm-based feed-forward artificial neural network model for forecasting waste quantities in Poland. In addition, the developed hybrid model is compared against the classical feed-forward artificial neural network model. The performance evaluation analysis is explored using the indicators of mean bias error, root-mean-squared error, Pearson correlation coefficient, Willmott’s index of agreement, and coefficient of efficiency. Test results illustrated that the developed hybrid feed-forward artificial neural network model trained using sine cosine algorithm significantly outperformed the classical neural network model. It can be argued that the developed model could assist decision-makers in the proper management of growing quantities of municipal solid wastes.
Automated Assessment of Municipal Solid Wastes Using a Hybrid Sine Cosine Algorithm-Based Neural Network
Lecture Notes in Civil Engineering
Gupta, Rishi (Herausgeber:in) / Sun, Min (Herausgeber:in) / Brzev, Svetlana (Herausgeber:in) / Alam, M. Shahria (Herausgeber:in) / Ng, Kelvin Tsun Wai (Herausgeber:in) / Li, Jianbing (Herausgeber:in) / El Damatty, Ashraf (Herausgeber:in) / Lim, Clark (Herausgeber:in) / Elshaboury, Nehal (Autor:in) / Al-Sakkaf, Abobakr (Autor:in)
Canadian Society of Civil Engineering Annual Conference ; 2022 ; Whistler, BC, BC, Canada
Proceedings of the Canadian Society of Civil Engineering Annual Conference 2022 ; Kapitel: 12 ; 141-153
13.01.2024
13 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Municipal solid wastes , Wastes quantities , Feed-forward neural network , Sine cosine algorithm , Hybrid machine learning , Performance evaluation Engineering , Building Construction and Design , Geoengineering, Foundations, Hydraulics , Transportation Technology and Traffic Engineering , Environment, general
Cement Transport Vehicle Routing with a Hybrid Sine Cosine Optimization Algorithm
| DOAJ | 2023
A Sine Cosine Algorithm-Based Fractional MPPT for Thermoelectric Generation System
| DOAJ | 2021
Recycling municipal solid wastes
| British Library Conference Proceedings | 2001