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Environmental resilience through artificial intelligence: innovations in monitoring and management

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Abstract

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.

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Authors and Affiliations

  1. School of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, (144411), India

    Atif Khurshid Wani & Anjuman Ayub

  2. Research Center for Genetic Engineering, National Research and Innovation Agency, Bogor, 16911, Indonesia

    Farida Rahayu

  3. Department of Process Engineering and Petrochemical, Faculty of Technology, University of El Oued, 39000, El Oued, Algeria

    Ilham Ben Amor

  4. Department of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia

    Munleef Quadir

  5. Research Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia

    Mala Murianingrum, Parnidi Parnidi, Sakiroh Sakiroh & Saefudin Saefudin

  6. Research Center for Behavioral and Circular Economics, National Research and Innovation Agency, Gatot, Subroto, Jakarta, (12710), Indonesia

    Supriyadi Supriyadi

  7. Department of Nuclear and Renewable Energy, Ural Federal University, Ekaterinburg, (620002), Russia

    Abhinav Kumar

  8. Research Center for Horticulture, National Research and Innovation Agency, Bogor, (16911), Indonesia

    Evy Latifah

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  1. Atif Khurshid Wani
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Contributions

AKW: conceptualization, methodology, supervision, validation, writing—original draft preparation, writing—reviewing and editing. FR, IBA, MQ, and MM: methodology, resources, investigation, formal analysis, validation. PP, AA, SS, SS, SS, AK, and EL: writing—original draft preparation, visualization, writing and editing.

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Correspondence to Atif Khurshid Wani.

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Wani, A.K., Rahayu, F., Ben Amor, I. et al. Environmental resilience through artificial intelligence: innovations in monitoring and management. Environ Sci Pollut Res 31, 18379–18395 (2024). https://doi.org/10.1007/s11356-024-32404-z

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