AIMC Topic: Water Pollution

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Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

Environmental science and pollution research international
The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecolog...

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

Science (New York, N.Y.)
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...

Reusable and Practical Biocomposite Based on sp. YF1 and Polyacrylonitrile-Based Carbon Fiber for the Efficient Bioremediation of Microcystin-LR-Contaminated Water.

Toxins
Microbial degradation is a cost-effective and environmentally friendly method for removing microcystin-LR (MC-LR). However, the application of free bacteria has limitations due to low operational stability and difficulties in recovery. In a previous ...

Identification of pollution source and prediction of water quality based on deep learning techniques.

Journal of contaminant hydrology
Semi-arid rivers are particularly vulnerable and responsive to the impacts of industrial contamination. Prompt identification and projection of pollutant dynamics are crucial in the accidental pollution incidents, therefore required the timely inform...

A fuzzy logic-based approach for groundwater vulnerability assessment.

Environmental science and pollution research international
Groundwater vulnerability assessment systems have been developed to protect groundwater resources. The DRASTIC model calculates the vulnerability index of the aquifer based on seven effective parameters. The application of expert opinion in rating an...

Air pollution, water pollution, and robots: Is technology the panacea.

Journal of environmental management
The degradation of the ecological environment caused by industrialization presents a major challenge for policymakers as they aim to develop sustainability. Is there a way to balance industrial growth and environmental sustainability? To answer this ...

IoT-based automated water pollution treatment using machine learning classifiers.

Environmental technology
Water is one of the most vital sources for the survival of life. In the globe, the accessibility of water in safe and healthy ways is a major concern. The consumption of unsafe water may lead to health risks. Therefore, it is necessary to classify an...

Development of new computational machine learning models for longitudinal dispersion coefficient determination: case study of natural streams, United States.

Environmental science and pollution research international
Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and ...

Identification the source of fecal contamination for geographically unassociated samples with a statistical classification model based on support vector machine.

Journal of hazardous materials
The bacterial diversity and corresponding biological significance revealed by high-throughput sequencing contribute massive information to source tracking of fecal contamination. The performances of classification models on predicting the fecal sourc...

Sequence-enabled community-based microbial source tracking in surface waters using machine learning classification: A review.

Journal of microbiological methods
The development of Microbial Source Tracking (MST) technologies was borne out of necessity. This was largely due to the: 1) inadequacies of the fecal indicator bacterial paradigm, 2) fact that many fecal bacteria can survive and often grow in the env...