Environmental resilience through artificial intelligence: innovations in monitoring and management

被引：12

作者：

Wani, Atif Khurshid ^{[1
]}

Rahayu, Farida ^{[2
]}

Ben Amor, Ilham ^{[3
]}

Quadir, Munleef ^{[4
]}

Murianingrum, Mala ^{[5
]}

Parnidi, Parnidi ^{[5
]}

Ayub, Anjuman ^{[1
]}

Supriyadi, Supriyadi ^{[6
]}

Sakiroh, Sakiroh ^{[5
]}

Saefudin, Saefudin ^{[5
]}

Kumar, Abhinav ^{[7
]}

Latifah, Evy ^{[8
]}

机构：

[1] Lovely Profess Univ, Sch Bioengn & Biosci, Phagwara 144411, Punjab, India

[2] Natl Res & Innovat Agcy BRIN, Res Ctr Genet Engn, Bogor 16911, Indonesia

[3] Univ El Oued, Fac Technol, Dept Proc Engn & Petrochem, El Oued 39000, Algeria

[4] Jazan Univ, Coll Comp Sci & Informat Technol, Dept Comp Sci, Jazan, Saudi Arabia

[5] Natl Res & Innovat Agcy, Res Ctr Hort & Estate Crops, Bogor 16911, Indonesia

[6] Natl Res & Innovat Agcy, Res Ctr Behav & Circular Econ, Jakarta 12710, Indonesia

[7] Ural Fed Univ, Dept Nucl & Renewable Energy, Ekaterinburg 620002, Russia

[8] Natl Res & Innovat Agcy, Res Ctr Hort, Bogor 16911, Indonesia

来源：

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH | 2024年 / 31卷 / 13期

关键词：

Artificial intelligence; Environment; Remote sensing; Water pollution; Air quality; Sustainability; MUNICIPAL SOLID-WASTE; STATISTICAL EXPERIMENTAL-DESIGN; SUPPORT VECTOR MACHINE; EARLY-WARNING SYSTEM; NEURAL-NETWORK; WATER-TREATMENT; AQUEOUS-SOLUTION; DISSOLVED-OXYGEN; OBJECT DETECTION; GENERATION RATE;

D O I：

10.1007/s11356-024-32404-z

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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.

引用

页码：19381 / 19395

页数：15

共 100 条

[1] Results Uncertainty of Support Vector Machine and Hybrid of Wavelet Transform-Support Vector Machine Models for Solid Waste Generation Forecasting
Abbasi, M.
Abduli, M. A.
Omidvar, B.
Baghvand, A.
[J]. ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY, 2014, 33 (01) : 220 - 228
[2] Forecasting municipal solid waste generation using artificial intelligence modelling approaches
Abbasi, Maryam
El Hanandeh, Ali
[J]. WASTE MANAGEMENT, 2016, 56 : 13 - 22
[3] An artificial neural network approach for the estimation of the primary production of energy from municipal solid waste and its application to the Balkan countries
Adamovic, Vladimir M.
Antanasijevic, Davor Z.
Cosovic, Aleksandar R.
Ristic, Mirjana D.
Pocajt, Viktor V.
[J]. WASTE MANAGEMENT, 2018, 78 : 955 - 968
[4] Agarwala N., 2021, Maritime Technology and Research, V3, P120, DOI [10.33175/mtr.2021.248053, DOI 10.33175/MTR.2021.248053]
[5] Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey
Ali, Jarinah Mohd
Hussain, M. A.
Tade, Moses O.
Zhang, Jie
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (14) : 5915 - 5931
[6] Wastewater treatment aeration process optimization: A data mining approach
Asadi, Ali
Verma, Anoop
Yang, Kai
Mejabi, Ben
[J]. JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2017, 203 : 630 - 639
[7] Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye
Asfaram, A.
Ghaedi, M.
Azqhandi, M. H. Ahmadi
Goudarzi, A.
Dastkhoon, M.
[J]. RSC ADVANCES, 2016, 6 (46) : 40502 - 40516
[8] Ultrasound-assisted binary adsorption of dyes onto Mn@ CuS/ZnS-NC-AC as a novel adsorbent: Application of chemometrics for optimization and modeling
Asfaram, Arash
Ghaedi, Mehrorang
Azqhandi, Mohammad Hossein Ahmadi
Goudarzi, Alireza
Hajati, Shaaker
[J]. JOURNAL OF INDUSTRIAL AND ENGINEERING CHEMISTRY, 2017, 54 : 377 - 388
[9] Synthesis of magnetic γ-Fe2O3-based nanomaterial for ultrasonic assisted dyes adsorption: Modeling and optimization
Asfaram, Arash
Ghaedi, Mehrorang
Hajati, Shaaker
Goudarzi, Alireza
[J]. ULTRASONICS SONOCHEMISTRY, 2016, 32 : 418 - 431
[10] Bastanlar Y, 2014, METHODS MOL BIOL, V1107, P105, DOI 10.1007/978-1-62703-748-8_7

← 1 2 3 4 5 6 7 8 9 10 →