AIMC Topic: Water Pollution, Chemical

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Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory.

Chemosphere
Accurate in silico predictions of chemical substance ecotoxicity has become an important issue in recent years. Most conventional methods, such as the Ecological Structure-Activity Relationship (ECOSAR) model, cluster chemical substances empirically ...

Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill.

Environmental science and pollution research international
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environ...

Modeling of an activated sludge process for effluent prediction-a comparative study using ANFIS and GLM regression.

Environmental monitoring and assessment
In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive mod...

Use of ultraviolet-visible spectrophotometry associated with artificial neural networks as an alternative for determining the water quality index.

Environmental monitoring and assessment
The water quality index (WQI) is an important tool for water resource management and planning. However, it has major disadvantages: the generation of chemical waste, is costly, and time-consuming. In order to overcome these drawbacks, we propose to s...

Suspect screening of large numbers of emerging contaminants in environmental waters using artificial neural networks for chromatographic retention time prediction and high resolution mass spectrometry data analysis.

The Science of the total environment
The recent development of broad-scope high resolution mass spectrometry (HRMS) screening methods has resulted in a much improved capability for new compound identification in environmental samples. However, positive identifications at the ng/L concen...

Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.

The Science of the total environment
Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vecto...