Role of big data analytics and hyperspectral imaging in waste management for circular economy

Abstract

Abstract The accumulation of waste has a profound impact on the environment, prompting a crucial discussion about effective waste management strategies aligned with Oman Vision 2040’s sustainability goals. The consequences of municipal solid waste generation have multifaceted impacts on the environment, public health, and overall well-being of communities. Addressing these consequences requires a holistic approach that includes the integration of sustainable waste management technologies to foster a circular economy. This paper emphasizes the necessity for a paradigm shift in waste management methodologies, emphasizing the importance of Hyperspectral Imaging and Big Data Analytics into municipal solid waste management processes. This paper explores the synergistic relationship between hyperspectral imaging which is capable of precise material identification, and big data analytics to facilitate comprehensive data analysis. The integration aims to optimize waste segregation, resource recovery, and recycling processes. The utilization of data-driven insights enables predictive modeling and the identification of trends thereby facilitating more efficient and sustainable waste management practices. The harnessing of big data analytics empowers stakeholders to make informed decisions in waste management to achieve long-term environmental, and economic sustainability.

Keywords

• Hyperspectral Imaging
• Big Data
• Waste Management
• Municipal Solid Waste
• Data Analytics
• Circular Economy