AIMC Topic: Water Quality

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A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement.

Journal of environmental management
Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods result...

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak.

Journal of environmental management
Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the ...

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...

Groundwater quality index development using the ANN model of Delhi Metropolitan City, India.

Environmental science and pollution research international
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...

Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data.

Water research
Ensuring the safety and reliability of drinking water supply requires accurate prediction of water quality in water distribution networks (WDNs). However, existing hydraulic model-based approaches for system state prediction face challenges in model ...

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information.

Environmental research
Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable poli...

Forecasting water quality variable using deep learning and weighted averaging ensemble models.

Environmental science and pollution research international
Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a val...

Automated software for counting and measuring Hyalella genus using artificial intelligence.

Environmental science and pollution research international
Amphipods belonging to the Hyalella genus are macroinvertebrates that inhabit aquatic environments. They are of particular interest in areas such as limnology and ecotoxicology, where data on the number of Hyalella individuals and their allometric me...

An integrated modelling framework for multiple pollution source identification in surface water.

Journal of environmental management
Pollution source identification is vital in water safety management. An integrated simulation-optimization modelling framework comprising a process-based hydrodynamic water quality model, artificial neural network surrogate model and particle swarm o...

Modelling biochemical oxygen demand using improved neuro-fuzzy approach by marine predators algorithm.

Environmental science and pollution research international
Biochemical oxygen demand (BOD) is one of the most important parameters used for water quality assessment. Alternative methods are essential for accurately prediction of this parameter because the traditional method in predicting the BOD is time-cons...