Applications of deep learning in water quality management: A state-of-the-art review
Excellent water quality (WQ) is an indispensable element in ensuring sustainable water
resource development. It is highly associated with the 3rd (good health and well-being), the …
resource development. It is highly associated with the 3rd (good health and well-being), the …
Large-scale water quality prediction with integrated deep neural network
Water environment time series prediction is important to efficient water resource
management. Traditional water quality prediction is mainly based on linear models …
management. Traditional water quality prediction is mainly based on linear models …
[HTML][HTML] Modeling bitcoin prices using signal processing methods, bayesian optimization, and deep neural networks
B Tripathi, RK Sharma - Computational Economics, 2023 - Springer
Bitcoin is a volatile financial asset that runs on a decentralized peer-to-peer Blockchain
network. Investors need accurate price forecasts to minimize losses and maximize profits …
network. Investors need accurate price forecasts to minimize losses and maximize profits …
Accurate water quality prediction with attention-based bidirectional LSTM and encoder–decoder
Accurate prediction of water quality indicators can effectively predict sudden water pollution
events and reveal them to water users for reducing the impact of water quality pollution …
events and reveal them to water users for reducing the impact of water quality pollution …
Combining knowledge graph with deep adversarial network for water quality prediction
J Yan, Q Gao, Y Yu, L Chen, Z Xu, J Chen - Environmental Science and …, 2023 - Springer
Water quality prediction is an important research focus in smart water and can provide the
support to control and reduce water pollution. However, existing water quality prediction …
support to control and reduce water pollution. However, existing water quality prediction …
Prediction of dissolved oxygen concentration in aquaculture based on attention mechanism and combined neural network
W Yang, W Liu, Q Gao - Mathematical biosciences and …, 2023 - pubmed.ncbi.nlm.nih.gov
As an essential water quality parameter in aquaculture ponds, dissolved oxygen (DO) affects
the growth and development of aquatic animals and their feeding and absorption. However …
the growth and development of aquatic animals and their feeding and absorption. However …
Hybrid water quality prediction with graph attention and spatio-temporal fusion
Spatio-temporal prediction has a wide range of applications in many fields, eg, air pollution,
weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio …
weather forecasting, and traffic forecasting. Water quality prediction is also one of spatio …
Decomposed intrinsic mode functions and deep learning algorithms for water quality index forecasting
The water quality index (WQI) serves as a global representation of river water quality (WQ).
Existing studies related to the WQI have mainly focused on two aspects:(i) a WQI point …
Existing studies related to the WQI have mainly focused on two aspects:(i) a WQI point …
Hybrid prediction for water quality with bidirectional LSTM and temporal attention
Accurate prediction of water quality indicators can effectively prevent sudden water pollution
events, and control pollution diffusion. Neural networks, eg, long short-term memory (LSTM) …
events, and control pollution diffusion. Neural networks, eg, long short-term memory (LSTM) …
Multi-indicator water time series imputation with autoregressive generative adversarial networks
The water quality data has missing values and lacks integrity because water environment
monitoring equipments are easily damaged by environmental influences, thereby affecting …
monitoring equipments are easily damaged by environmental influences, thereby affecting …