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

Eco-Environment & Health, Journal Year: 2022, Volume and Issue: 1(2), P. 107 - 116

Published: June 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.

Language: Английский

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The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management

Results in Engineering, Journal Year: 2023, Volume and Issue: 20, P. 101566 - 101566

Published: Nov. 3, 2023

The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches resource (WRM) struggle with real-time data acquisition, analysis, intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) Big Data Analytics (BDA) at the forefront have potential revolutionize way managed. This paper reviews current applications AI BDA in WRM, highlighting their capacity overcome existing limitations. It includes investigation technologies, such as machine learning deep learning, diverse quality monitoring, allocation, demand forecasting. In addition, review explores role resources, elaborating on various sources that can be used, remote sensing, IoT devices, social media. conclusion, study synthesizes key insights outlines prospective directions for leveraging optimal allocation.

Language: Английский

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Exploring potential machine learning application based on big data for prediction of wastewater quality from different full-scale wastewater treatment plants

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 832, P. 154930 - 154930

Published: April 4, 2022

Language: Английский

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Biochar-enhanced bioremediation of eutrophic waters impacted by algal blooms

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 367, P. 122044 - 122044

Published: Aug. 3, 2024

Language: Английский

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Artificial Intelligence Technologies Revolutionizing Wastewater Treatment: Current Trends and Future Prospective

Water, Journal Year: 2024, Volume and Issue: 16(2), P. 314 - 314

Published: Jan. 17, 2024

Integration of the Internet Things (IoT) into fields wastewater treatment and water quality prediction has potential to revolutionize traditional approaches address urgent challenges, considering global demand for clean sustainable systems. This comprehensive article explores transformative applications smart IoT technologies, including artificial intelligence (AI) machine learning (ML) models, in these areas. A successful example is implementation an IoT-based automated monitoring system that utilizes cloud computing ML methods effectively above-mentioned issues. The been employed optimize, simulate, automate various aspects, such as managing natural systems, water-treatment processes, wastewater-treatment applications, water-related agricultural practices like hydroponics aquaponics. review presents a collection significant water-based which have combined with IoT, neural networks, or undergone critical peer-reviewed assessment. These encompass chlorination, adsorption, membrane filtration, indices, modeling parameters, river levels, automating/monitoring effluent aquaculture Additionally, this provides overview discusses future along examples how their algorithms utilized evaluate treated diverse aquatic environments.

Language: Английский

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Recent advances in algal bloom detection and prediction technology using machine learning

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 938, P. 173546 - 173546

Published: May 27, 2024

Language: Английский

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Predicting Chlorophyll-a Concentrations in the World’s Largest Lakes Using Kolmogorov-Arnold Networks

Environmental Science & Technology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 16, 2025

Accurate prediction of chlorophyll-a (Chl-a) concentrations, a key indicator eutrophication, is essential for the sustainable management lake ecosystems. This study evaluated performance Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) traditional machine learning tools (RF, SVR, GPR) predicting time-series Chl-a concentrations in large lakes. Monthly remote-sensed data derived from Aqua-MODIS spanning September 2002 to April 2024 were used. The based on their forecasting capabilities March August 2024. KAN consistently outperformed others both test forecast (unseen data) phases demonstrated superior accuracy capturing trends, dynamic fluctuations, peak concentrations. Statistical evaluation using ranking metrics critical difference diagrams confirmed KAN's robust across diverse sites, further emphasizing its predictive power. Our findings suggest that KAN, which leverages KA representation theorem, offers improved handling nonlinearity long-term dependencies data, outperforming grounded universal approximation theorem algorithms.

Language: Английский

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Predicting Water Quality with Artificial Intelligence: A Review of Methods and Applications

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(8), P. 4633 - 4652

Published: June 13, 2023

Language: Английский

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Improved prediction of chlorophyll-a concentrations in reservoirs by GRU neural network based on particle swarm algorithm optimized variational modal decomposition

Environmental Research, Journal Year: 2023, Volume and Issue: 221, P. 115259 - 115259

Published: Jan. 10, 2023

Language: Английский

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Tracking changes in chlorophyll-a concentration and turbidity in Nansi Lake using Sentinel-2 imagery: A novel machine learning approach

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102597 - 102597

Published: April 9, 2024

This study represents the first application of Sentinel-2 remote sensing imagery and model fusion techniques to assess chlorophyll-a (Chla) concentration turbidity in Nansi Lake, Shandong Province, China, from 2016 2022. First, we innovatively employed stacking method fuse eight fundamentally different Machine Learning (ML) models, each utilising 20 17 feature bands, resulting development a robust algorithm for estimating Chla Lake. The results demonstrate that Stacking Model has achieved outstanding theoretical generalisation capability. Second, sensitivity extreme value data sample was quantified, found compared with gradient boosting (XGBoost), optimal performance improved by 12%, some extent, it solved problem high-value underestimation low-value overestimation. SHapley Additive exPlanations (SHAP) revealed features such as Three Bands, Enhanced Three, Rrs492/Rrs560, Rrs705/Rrs665 play crucial role concentration. For estimation, Normalized Difference Turbidity Index (NDTI), Rrs705+Rrs560, Rrs865-Rrs740 made significant contributions. Finally, utilised create spatiotemporal maps Lake We analysed causes water quality changes explored driving factors. Compared previous studies, this paper provides new idea monitoring lake parameters using high resolution image precision technology, these can provide reference similar area research decision-making support environment-related departments.

Language: Английский

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