Environmental resilience through artificial intelligence: innovations in monitoring and management

• Published in: Environmental science and pollution research international 2024-03, Vol.31 (12), p.18379-18395
• Main Authors: Wani, Atif Khurshid, Rahayu, Farida, Ben Amor, Ilham, Quadir, Munleef, Murianingrum, Mala, Parnidi, Parnidi, Ayub, Anjuman, Supriyadi, Supriyadi, Sakiroh, Sakiroh, Saefudin, Saefudin, Kumar, Abhinav, Latifah, Evy
• Format: Article
• Language: English
• Subjects: Adaptability; Aquatic Pollution; Artificial Intelligence; Atmospheric Protection/Air Quality Control/Air Pollution; Catalysis; Decision making; domain; Early warning systems; Earth and Environmental Science; Ecotoxicology; Engineering; Environment; Environmental Chemistry; Environmental control; Environmental Health; Environmental impact; Environmental Monitoring; finance; health services; Object recognition; Remote sensing; Resilience; Resilience, Psychological; Review Article; Reviews; Solid waste management; Solid wastes; Telemedicine; texture; Threat evaluation; Warning systems; Waste management; Waste Water Technology; Wastewater treatment; Water Management; Water Pollution Control
• ISSN: 1614-7499; 0944-1344; 1614-7499
• DOI: 10.1007/s11356-024-32404-z

The rapid rise of artificial intelligence (AI) technology has revolutionized numerous fields, with its applications spanning finance, engineering, healthcare, and more. In recent years, AI’s potential in addressing environmental concerns has garnered significant attention. This review paper provides a comprehensive exploration of the impact that AI has on addressing and mitigating critical environmental concerns. In the backdrop of AI’s remarkable advancement across diverse disciplines, this study is dedicated to uncovering its transformative potential in the realm of environmental monitoring. The paper initiates by tracing the evolutionary trajectory of AI technologies and delving into the underlying design principles that have catalysed its rapid progression. Subsequently, it delves deeply into the nuanced realm of AI applications in the analysis of remote sensing imagery. This includes an intricate breakdown of challenges and solutions in per-pixel analysis, object detection, shape interpretation, texture evaluation, and semantic understanding. The crux of the review revolves around AI’s pivotal role in environmental control, examining its specific implementations in wastewater treatment and solid waste management. Moreover, the study accentuates the significance of AI-driven early-warning systems, empowering proactive responses to environmental threats. Through a meticulous analysis, the review underscores AI’s unparalleled capacity to enhance accuracy, adaptability, and real-time decision-making, effectively positioning it as a cornerstone in shaping a sustainable and resilient future for environmental monitoring and preservation.