AIMC Topic: Drinking Water

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The insightful water quality analysis and predictive model establishment via machine learning in dual-source drinking water distribution system.

Environmental research Feb 16, 2024
Dual-source drinking water distribution systems (DWDS) over single-source water supply systems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydrau...
Models, Theoretical Water Pollutants, Chemical Machine Learning Drinking Water Water Supply Trihalomethanes Chlorine Water Quality
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Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

Science (New York, N.Y.) Jan 25, 2024
We assess which waters the Clean Water Act protects and how Supreme Court and White House rules change this regulation. We train a deep learning model using aerial imagery and geophysical data to predict 150,000 jurisdictional determinations from the...
Water Pollution Wetlands Water Quality Drinking Water Conservation of Natural Resources Machine Learning Rivers
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Groundwater quality index development using the ANN model of Delhi Metropolitan City, India.

Environmental science and pollution research international Dec 22, 2023
Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of...
India Cities Groundwater Neural Networks, Computer Drinking Water Environmental Monitoring Water Quality
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Deep learning approach for prediction and classification of potable water.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry Apr 8, 2023
Potable water, commonly known as drinking water, refers to water that is safe to drink and does not endanger human health. It must adhere to strict quality standards set by health organizations, be devoid of dangerous pollutants and chemicals, and me...
Deep Learning Water Quality Algorithms Ecosystem Drinking Water Humans
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Multiple Statistical Model Ensemble Predictions of Residual Chlorine in Drinking Water: Applications of Various Deep Learning and Machine Learning Algorithms.

Journal of environmental and public health Sep 28, 2022
Applicability of statistical models in predicting chlorine decay remains minimally explored. This study predicted residual chlorine using six deep learning and nine machine learning techniques. Suitability of multimodel ensembles (MMEs) including ari...
Deep Learning Models, Statistical Drinking Water Chlorine Machine Learning Algorithms
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Predicting flocculant dosage in the drinking water treatment process using Elman neural network.

Environmental science and pollution research international Aug 31, 2021
Predicting the flocculant dosage in the drinking water treatment process is essential for public health. However, due to the complexity of water quality and flocculation, many difficulties remain. The present study aimed to report on using artificial...
Drinking Water Flocculation Artificial Intelligence Water Quality Neural Networks, Computer
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Spectrophotometric Online Detection of Drinking Water Disinfectant: A Machine Learning Approach.

Sensors (Basel, Switzerland) Nov 21, 2020
The spectra fingerprint of drinking water from a water treatment plant (WTP) is characterised by a number of light-absorbing substances, including organic, nitrate, disinfectant, and particle or turbidity. Detection of disinfectant (monochloramine) c...
Drinking Water Water Purification Machine Learning Water Supply Disinfectants
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Application of a new HMW framework derived ANN model for optimization of aquatic dissolved organic matter removal by coagulation.

Chemosphere Jul 29, 2020
Removing dissolved organic matter (DOM) with polyaluminium chloride is one of the primary goals of drinking water treatment. In this study, a new HMW framework was proposed, which divided the factors affecting coagulation into three parts consisting ...
Water Purification Metals Hydrogen-Ion Concentration Aluminum Hydroxide Models, Theoretical Neural Networks, Computer Drinking Water
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Radial basis function artificial neural network able to accurately predict disinfection by-product levels in tap water: Taking haloacetic acids as a case study.

Chemosphere Jan 22, 2020
Control of risks caused by disinfection by-products (DBPs) requires pre-knowledge of their levels in drinking water. In this study, a radial basis function (RBF) artificial neural network (ANN) was proposed to predict the concentrations of haloacetic...
Trihalomethanes Neural Networks, Computer Disinfectants Drinking Water Water Purification Water Pollutants, Chemical China Environmental Monitoring Disinfection
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In vitro selection of DNA aptamers and their integration in a competitive voltammetric biosensor for azlocillin determination in waste water.

Analytica chimica acta Dec 16, 2019
The uncontrolled usage of veterinary antibiotics has led to their widespread pollution in waterways and milk products. Potential impact of antibiotic residues on the environment and human health such as increased antibiotic resistance of microorganis...
Drinking Water Azlocillin Base Sequence Water Pollutants, Chemical Wastewater Aptamers, Nucleotide Electrochemical Techniques DNA Anti-Bacterial Agents Limit of Detection Biosensing Techniques
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