Evidence map›Paper›PMID 38358626›Full record

ReviewEnvironmental science and pollution research international2024

Environmental resilience through artificial intelligence: innovations in monitoring and management.

Atif Khurshid Wani, Farida Rahayu, Ilham Ben Amor, Munleef Quadir, Mala Murianingrum, Parnidi Parnidi, Anjuman Ayub, Supriyadi Supriyadi, Sakiroh Sakiroh, Saefudin Saefudin and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Environmental science and pollution research international, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Atif Khurshid WaniSchool of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, (144411), India. atifkhurshid61200216@gmail.com.ORCID http://orcid.org/0000-0002-8820-1231
Farida RahayuResearch Center for Genetic Engineering, National Research and Innovation Agency, Bogor, 16911, Indonesia.
Ilham Ben AmorDepartment of Process Engineering and Petrochemical, Faculty of Technology, University of El Oued, 39000, El Oued, Algeria.
Munleef QuadirDepartment of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan, Kingdom of Saudi Arabia.
Mala MurianingrumResearch Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia.
Parnidi ParnidiResearch Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia.
Anjuman AyubSchool of Bioengineering and Biosciences, Lovely Professional University, Jalandhar, Punjab, (144411), India.
Supriyadi SupriyadiResearch Center for Behavioral and Circular Economics, National Research and Innovation Agency, Gatot, Subroto, Jakarta, (12710), Indonesia.
Sakiroh SakirohResearch Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia.
Saefudin SaefudinResearch Center for Estate Crops, National Research and Innovation Agency, Bogor, (16911), Indonesia.
Abhinav KumarDepartment of Nuclear and Renewable Energy, Ural Federal University, Ekaterinburg, (620002), Russia.
Evy LatifahResearch Center for Horticulture, National Research and Innovation Agency, Bogor, (16911), Indonesia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceResilience, PsychologicalCatalysisEngineeringEnvironmental MonitoringAir qualityArtificial intelligenceEnvironmentRemote sensingSustainabilityWater pollution

Identifiers

PMID38358626

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.