Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications DOI

E Varun,

L. Rajesh,

M. Lokeshwari

et al.

Advances in IT standards and standardization research (AISSR) book series/Advances in IT standards and standardization research series, Journal Year: 2024, Volume and Issue: unknown, P. 79 - 110

Published: Dec. 18, 2024

This chapter points out machine learning-Ml-that is set to alter air and water quality monitoring technologies. Traditional systems usually work satisfactorily; however, there always exists deficiencies regarding data processing accuracy of response in real time. For this kind system, ML algorithms would become advantageous for such carry large-scale environmental analysis, thereby enhancing predictive capabilities realize early detection pollutants. The goes on explain techniques under supervised unsupervised learning, along with their applications sensor networks, remote sensing, fusion. Case studies the successful implementation ML-driven solution show improvement decision-making. Further, addresses challenge associated integration privacy, algorithmic bias, need robust training datasets.

Language: Английский

Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China DOI
Wei Qing, Ju Rui Yang, Fangbing Fu

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124121 - 124121

Published: Jan. 15, 2025

Language: Английский

Citations

1
Advanced Temporal Deep Learning Framework for Enhanced Predictive Modeling in Industrial Treatment Systems DOI Creative Commons

S Ramya,

S Srinath,

Pushpa Tuppad

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104158 - 104158

Published: Jan. 1, 2025

Language: Английский

Citations

1
Small hydropower impacts on water quality: a comparative analysis of different assessment methods DOI Creative Commons
Paweł Tomczyk,

Michał Tymcio,

Alban Kuriqi

et al.

Water Resources and Industry, Journal Year: 2025, Volume and Issue: unknown, P. 100282 - 100282

Published: Feb. 1, 2025

Language: Английский

Citations

0
Developing a real-time water quality simulation toolbox using machine learning and application programming interface DOI

Gi-Hun Bang,

Na-Hyeon Gwon,

Min‐Jeong Cho

et al.

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124719 - 124719

Published: Feb. 28, 2025

Language: Английский

Citations

0
The role of optimizers in developing data-driven model for predicting lake water quality incorporating advanced water quality model DOI
Md Galal Uddin, Apoorva Bamal, Mir Talas Mahammad Diganta

et al.

Alexandria Engineering Journal, Journal Year: 2025, Volume and Issue: 122, P. 411 - 435

Published: March 18, 2025

Language: Английский

Citations

0
Simulating the Deterioration Behavior of Tunnel Elements Using Amalgamation of Regression Trees and State-of-the-Art Metaheuristics DOI Creative Commons
Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf, Moaaz Elkabalawy

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(7), P. 1021 - 1021

Published: March 21, 2025

Tunnel infrastructures worldwide face escalating deterioration challenges due to aging materials, increasing load demands, and exposure harsh environmental conditions. Accurately predicting the onset progression of is paramount for ensuring structural safety, optimizing maintenance interventions, prolonging service life. However, complex interplay environmental, material, operational factors poses significant current predictive models. Additionally, they are constrained by small datasets a narrow range tunnel elements that limit their generalizability. This paper presents novel hybrid metaheuristic-based regression tree (REGT) model designed enhance accuracy robustness predictions. Leveraging metaheuristic algorithms’ strengths, developed method jointly optimizes critical hyperparameters identifies most relevant features prediction. A comprehensive dataset encompassing material properties, stressors, traffic loads, historical condition assessments was compiled development. Comparative analyses against conventional trees, artificial neural networks, support vector machines demonstrated consistently outperformed baseline techniques regarding While trees classic machine learning models, no single variant dominated all elements. Furthermore, optimization framework mitigated overfitting provided interpretable insights into primary driving deterioration. Finally, findings this research highlight potential models as powerful tools infrastructure management, offering actionable predictions enable proactive strategies resource optimization. study contributes advancing field modeling in civil engineering, with implications sustainable management infrastructure.

Language: Английский

Citations

0
An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction DOI Creative Commons
Fei Ding, Shilong Hao,

Mingcen Jiang

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103126 - 103126

Published: April 1, 2025

Language: Английский

Citations

0
A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality DOI Creative Commons
Mir Talas Mahammad Diganta, Md Galal Uddin, Tomasz Dabrowski

et al.

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 957, P. 177180 - 177180

Published: Nov. 23, 2024

Language: Английский

Citations

3
Decontamination of fish aquarium wastewater by ozonation catalyzed by multi-metal loaded activated carbons for sustainable aquaculture DOI
Amir Ikhlaq,

Mamoona Kanwal,

Osama Shaheen Rizvi

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 1, 2024

Language: Английский

Citations

0
Machine Learning-Enhanced Water and Air Quality Monitoring Technologies and Applications DOI

E Varun,

L. Rajesh,

M. Lokeshwari

et al.

Advances in IT standards and standardization research (AISSR) book series/Advances in IT standards and standardization research series, Journal Year: 2024, Volume and Issue: unknown, P. 79 - 110

Published: Dec. 18, 2024

This chapter points out machine learning-Ml-that is set to alter air and water quality monitoring technologies. Traditional systems usually work satisfactorily; however, there always exists deficiencies regarding data processing accuracy of response in real time. For this kind system, ML algorithms would become advantageous for such carry large-scale environmental analysis, thereby enhancing predictive capabilities realize early detection pollutants. The goes on explain techniques under supervised unsupervised learning, along with their applications sensor networks, remote sensing, fusion. Case studies the successful implementation ML-driven solution show improvement decision-making. Further, addresses challenge associated integration privacy, algorithmic bias, need robust training datasets.

Language: Английский

Citations

0