AIMC Topic: Water Quality

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Deep-Learning-Based Automated Tracking and Counting of Living Plankton in Natural Aquatic Environments.

Environmental science & technology
Plankton are widely distributed in the aquatic environment and serve as an indicator of water quality. Monitoring the spatiotemporal variation in plankton is an efficient approach to forewarning environmental risks. However, conventional microscopy c...

Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China.

Environmental science and pollution research international
In the past few decades, with the country's rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, w...

Deep learning approach for prediction and classification of potable water.

Analytical sciences : the international journal of the Japan Society for Analytical Chemistry
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...

A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction.

Environmental research
Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series predicti...

Operational parameter prediction of electrocoagulation system in a rural decentralized water treatment plant by interpretable machine learning model.

Journal of environmental management
Electrocoagulation (EC) is a promising alternative for decentralized drinking water treatment in rural areas as a chemical-free technology. However, seasonal fluctuations of water quality in influent remain a significant challenge for rural decentral...

Efficacy of GIS-based AHP and data-driven intelligent machine learning algorithms for irrigation water quality prediction in an agricultural-mine district within the Lower Benue Trough, Nigeria.

Environmental science and pollution research international
Agricultural productivity can be impaired by poor irrigation water quality. Therefore, adequate vulnerability assessment and identification of the most influential water quality parameters for accurate prediction becomes crucial for enhanced water re...

Large-scale prediction of stream water quality using an interpretable deep learning approach.

Journal of environmental management
Deep learning methods, which have strong capabilities for mapping highly nonlinear relationships with acceptable calculation speed, have been increasingly applied for water quality prediction in recent studies. However, it is argued that the practica...

Evaluation and prediction of irrigation water quality of an agricultural district, SE Nigeria: an integrated heuristic GIS-based and machine learning approach.

Environmental science and pollution research international
Poor irrigation water quality can mar agricultural productivity. Traditional assessment of irrigation water quality usually requires the computation of various conventional quality parameters, which is often time-consuming and associated with errors ...

Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

Bulletin of environmental contamination and toxicology
In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodie...

Pattern recognition describing spatio-temporal drivers of catchment classification for water quality.

The Science of the total environment
Classification using spatial data is foundational for hydrological modelling, particularly for ungauged areas. However, models developed from classified land use drivers deliver inconsistent water quality results for the same land uses and hinder dec...