A review of the application of machine learning in water quality evaluation

Eco-Environment & Health, Год журнала: 2022, Номер 1(2), С. 107 - 116

Опубликована: Июнь 1, 2022

With the rapid increase in volume of data on aquatic environment, machine learning has become an important tool for analysis, classification, and prediction. Unlike traditional models used water-related research, data-driven based can efficiently solve more complex nonlinear problems. In water environment conclusions derived from have been applied to construction, monitoring, simulation, evaluation, optimization various treatment management systems. Additionally, provide solutions pollution control, quality improvement, watershed ecosystem security management. this review, we describe cases which algorithms evaluate different environments, such as surface water, groundwater, drinking sewage, seawater. Furthermore, propose possible future applications approaches environments.

Язык: Английский

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Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Bioresource Technology, Год журнала: 2022, Номер 369, С. 128486 - 128486

Опубликована: Дек. 14, 2022

Язык: Английский

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Machine learning methods for modeling conventional and hydrothermal gasification of waste biomass: A review

Bioresource Technology Reports, Год журнала: 2022, Номер 17, С. 100976 - 100976

Опубликована: Фев. 1, 2022

Язык: Английский

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Surface Modification of Biochar for Dye Removal from Wastewater

Catalysts, Год журнала: 2022, Номер 12(8), С. 817 - 817

Опубликована: Июль 26, 2022

Nowadays, biochar is being studied to a great degree because of its potential for carbon sequestration, soil improvement, climate change mitigation, catalysis, wastewater treatment, energy storage, and waste management. The present review emphasizes on the utilization biochar-based nanocomposites play key role in decontaminating dyes from wastewater. Numerous trials are underway synthesize functionalized, surface engineered that can sufficiently remove dye-contaminated removal via natural modified follows numerous mechanisms such as precipitation, complexation, ion exchange, cation–π interactions, electrostatic attraction. Further, production modification promote good adsorption capacity dye owing properties tailored stage linked with specific hydrophobic interactions. Meanwhile, framework artificial neural networking machine learning model efficiency proposed even though studies still their infancy stage. article recommends smart technologies modelling forecasting should be included proper applications.

Язык: Английский

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A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management

Applied Sciences, Год журнала: 2023, Номер 13(22), С. 12147 - 12147

Опубликована: Ноя. 8, 2023

This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain water resource management. Environmental issues, such as climate change ecosystem destruction, pose significant threats to humanity planet. Addressing these challenges necessitates sustainable management increased efficiency. Artificial intelligence (AI) ML technologies present promising solutions this regard. By harnessing AI ML, we can collect analyze vast amounts data from diverse sources, remote sensing, smart sensors, social media. enables real-time monitoring decision making applications, including irrigation optimization, quality monitoring, flood forecasting, demand enhance agricultural practices, distribution models, desalination plants. Furthermore, facilitates integration, supports decision-making processes, enhances overall sustainability. However, wider adoption faces challenges, heterogeneity, stakeholder education, high costs. To provide an management, research focuses on core fundamentals, major (prediction, clustering, reinforcement learning), ongoing issues offer new insights. More specifically, after in-depth illustration algorithmic taxonomy, comparative mapping all specific tasks. At same time, include tabulation works along with some concrete, yet compact, descriptions objectives at hand. leveraging tools, develop plans address world’s supply concerns effectively.

Язык: Английский

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Recent advances and future prospects of thermochemical biofuel conversion processes with machine learning

Chemical Engineering Journal, Год журнала: 2023, Номер 471, С. 144503 - 144503

Опубликована: Июль 1, 2023

Язык: Английский

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Photocatalytic degradation of drugs and dyes using a maching learning approach

RSC Advances, Год журнала: 2024, Номер 14(13), С. 9003 - 9019

Опубликована: Янв. 1, 2024

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior organic pollutants during catalytic degradation.

Язык: Английский

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Nanocomposite ceramic membranes as novel tools for remediation of textile dye waste water – A review of current applications, machine learning based modeling and future perspectives

Journal of environmental chemical engineering, Год журнала: 2024, Номер 12(2), С. 112353 - 112353

Опубликована: Фев. 27, 2024

Язык: Английский

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Artificial intelligence interventions in 2D MXenes-based photocatalytic applications

Coordination Chemistry Reviews, Год журнала: 2025, Номер 529, С. 216460 - 216460

Опубликована: Янв. 24, 2025

Язык: Английский

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Comprehensive review on machine learning methodologies for modeling dye removal processes in wastewater

Journal of Cleaner Production, Год журнала: 2022, Номер 385, С. 135522 - 135522

Опубликована: Дек. 15, 2022

Язык: Английский

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