Implementation of Neural Networks and Big Data to Optimising Waste Disposal Processes and Reducing Emissions

Abstract

The article discusses the implementation of neural network technologies and big data analysis methods to optimize waste disposal processes and reduce emissions. The relevance of the study is justified by growing environmental problems and the need for effective waste management. The paper presents the main approaches to using neural networks to predict waste volumes, optimize their transportation routes, and improve the efficiency of material sorting. It also analyzes examples of successful cases of using these technologies in various industries. The results of the study show that the integration of neural networks and big data not only helps to reduce the ecological footprint, but also save resources, improving the overall efficiency of waste disposal systems. The article highlights the importance of an interdisciplinary approach and cooperation between experts in the field of ecology, information technology and waste management to achieve sustainable development.