Large-scale water quality prediction using federated sensing and learning: A case study with real-world sensing big-data
Green tide, which is a serious water pollution problem, is caused by the complex
relationships of various factors, such as flow rate, several water quality indicators, and …
relationships of various factors, such as flow rate, several water quality indicators, and …
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 …
Long range multi-step water quality forecasting using iterative ensembling
Real-life water quality monitoring applications such as aquaculture domains and water
resource management need long range multi-step prediction for disaster control. However …
resource management need long range multi-step prediction for disaster control. However …
Hierarchical attention-based context-aware network for red tide forecasting
X He, S Shi, X Geng, L Xu - Applied Soft Computing, 2022 - Elsevier
Chlorophyll forecasting is helpful for understanding characteristics of red tides, thus
enabling early warning. In practice, it is formulated as a time series forecasting problem …
enabling early warning. In practice, it is formulated as a time series forecasting problem …
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 …
Federated learning for water consumption forecasting in smart cities
Water consumption remains a major concern among the world's future challenges. For
applications like load monitoring and demand response, deep learning models are trained …
applications like load monitoring and demand response, deep learning models are trained …
An integrated deep neural network approach for large-scale water quality time series prediction
The prediction of water quality has great significance for the management of water
environment and the protection of water resources. Traditional water quality prediction …
environment and the protection of water resources. Traditional water quality prediction …
A water quality prediction method based on the deep LSTM network considering correlation in smart mariculture
Z Hu, Y Zhang, Y Zhao, M Xie, J Zhong, Z Tu, J Liu - Sensors, 2019 - mdpi.com
An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture.
Since the mariculturing environment is always open to its surroundings, the changes in …
Since the mariculturing environment is always open to its surroundings, the changes in …
Predicting urban water quality with ubiquitous data-a data-driven approach
Urban water quality is of great importance to our daily lives. Prediction of urban water quality
help control water pollution and protect human health. However, predicting the urban water …
help control water pollution and protect human health. However, predicting the urban water …
A multi-site tide level prediction model based on graph convolutional recurrent networks
Predicting regional tide levels is vital for engineering and catastrophe avoidance along the
shore. Data-driven method is capable of fast prediction of tide levels. However, current data …
shore. Data-driven method is capable of fast prediction of tide levels. However, current data …