AIMC Topic: Water Quality

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Improved prediction of chlorophyll-a concentrations using advancing graph neural network variants.

The Science of the total environment
Accurate estimation of harmful algal blooms is essential for protecting surface water. Chlorophyll-a (Chl-a), commonly used as a proxy for estimating algal concentration, is influenced by a broad range of weather and physicochemical factors that oper...

Comparing neural network architectures for simulating pollutant loads and first flush events in urban watersheds: Balancing specialization and generalization.

Chemosphere
This study investigates the effectiveness of artificial neural networks (ANNs) models in predicting urban water quality, specifically focusing on first flush (FF) event classification and pollutant event mean load (EML) predictions for total suspende...

The integrated fuzzy AHP and fuzzy logic techniques for mapping and prioritizing groundwater potential zone based on water quality.

Environmental monitoring and assessment
Groundwater, which is utilized to supply water demand in various sectors such as domestic water consumption, agriculture, and industry, could be achieved by delineating a groundwater potential zone. Although mapping groundwater potential zones has be...

Application of artificial intelligence for nutrient estimation in surface water bodies of basins with intensive agriculture.

Integrated environmental assessment and management
Eutrophication is one of the most relevant concerns due to the risk to water supply and food security. Nitrogen and phosphorus chemical species concentrations determined the risk and magnitude of eutrophication. These analyses are even more relevant ...

Comparative analysis of supervised learning models for effluent quality prediction in wastewater treatment plants.

PloS one
Effluent quality prediction is critical for optimizing Wastewater Treatment Plant (WWTP) operations, ensuring regulatory compliance, and promoting environmental sustainability. This study evaluates the performance of five supervised learning models-A...

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Water environment research : a research publication of the Water Environment Federation
Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed mo...

A water quality prediction model based on signal decomposition and ensemble deep learning techniques.

Water science and technology : a journal of the International Association on Water Pollution Research
Accurate water quality predictions are critical for water resource protection, and dissolved oxygen (DO) reflects overall river water quality and ecosystem health. This study proposes a hybrid model based on the fusion of signal decomposition and dee...

Deep learning based an effective hybrid model for water quality assessment.

Water environment research : a research publication of the Water Environment Federation
Water, which is very important for life and civilizations on Earth, has been a source of life for all living things. However, freshwater resources gradually decrease due to climate change, pollution, and population growth. Water pollution is the qual...

Adopting improved Adam optimizer to train dendritic neuron model for water quality prediction.

Mathematical biosciences and engineering : MBE
As one of continuous concern all over the world, the problem of water quality may cause diseases and poisoning and even endanger people's lives. Therefore, the prediction of water quality is of great significance to the efficient management of water ...

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

Water science and technology : a journal of the International Association on Water Pollution Research
There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to en...