Water quality ensemble prediction model for the urban water reservoir based on the hybrid long short-term memory (LSTM) network analysis DOI Creative Commons
Kai He, Yu Liu, Jinlong Yuan

et al.

AQUA - Water Infrastructure Ecosystems and Society, Journal Year: 2024, Volume and Issue: 73(8), P. 1621 - 1642

Published: July 15, 2024

ABSTRACT The water quality of drinking reservoirs directly impacts the supply safety for urban residents. This study focuses on Da Jing Shan Reservoir, a crucial source Zhuhai City and Macau Special Administrative Region. aim is to establish prediction model reservoirs, which can serve as vital reference plants when formulating their plans. In this research, after smoothing data using Hodrick-Prescott filter, we utilized long short-term memory (LSTM) network create Reservoir. Simulation calculations reveal that model's fitting degree consistently above 60%. Specifically, accuracy pH, dissolved oxygen (DO), biochemical demand (BOD) in aligns with actual results by more than 70%, effectively simulating reservoir's changes. Moreover, parameters such DO, BOD, total phosphorus, relative forecasting error LSTM less 10%, confirming validity. offer an essential predicting

Language: Английский

A review of machine learning and internet-of-things on the water quality assessment: methods, applications and future trends DOI Creative Commons
Gangani Dharmarathne,

A.M.S.R. Abekoon,

Madhusha Bogahawaththa

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105182 - 105182

Published: May 1, 2025

Language: Английский

Citations

0
A new method for predicting chlorophyll-a concentration in a reservoir: Coupling EFDC hydrodynamic and water quality model with ConvLSTM-MLP network DOI

Haobin Meng,

Jing Zhang, Yao‐Feng Chang

et al.

Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 133485 - 133485

Published: May 1, 2025

Language: Английский

Citations

0
DSE-NN: Discretized Spatial Encoding Neural Network for Ocean Temperature and Salinity Interpolation in the North Atlantic DOI Creative Commons
Shirong Liu,

Wentao Jia,

Weimin Zhang

et al.

Journal of Marine Science and Engineering, Journal Year: 2024, Volume and Issue: 12(6), P. 1013 - 1013

Published: June 18, 2024

The precise interpolation of oceanic temperature and salinity is crucial for comprehending the dynamics marine systems implications global climate change. Prior neural network-based methods face constraints related to their capacity delineate intricate spatio-temporal patterns that are intrinsic ocean data. This research presents an innovative approach, known as Discretized Spatial Encoding Neural Network (DSE-NN), comprising encoder–decoder model designed on basis deep supervision, network visualization, hyperparameter optimization. Through discretization input latitude longitude data into specialized vectors, DSE-NN adeptly captures temporal trends augments precision reconstruction, concurrently addressing complexity fragmentation characteristic sets. Employing North Atlantic a case study, this investigation shows enhanced accuracy in comparison with traditional network. outcomes demonstrate its quicker convergence lower loss function values, well ability reflect spatial distribution characteristics physical laws salinity. emphasizes potential providing robust tool three-dimensional reconstruction.

Language: Английский

Citations

2
DRSTF: A hybrid-approach framework for reservoir water temperature forecasting considering operation response DOI
Bowen Sun, Miao Yu,

Yuanning Zhang

et al.

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132081 - 132081

Published: Sept. 1, 2024

Language: Английский

Citations

2
Water quality ensemble prediction model for the urban water reservoir based on the hybrid long short-term memory (LSTM) network analysis DOI Creative Commons
Kai He, Yu Liu, Jinlong Yuan

et al.

AQUA - Water Infrastructure Ecosystems and Society, Journal Year: 2024, Volume and Issue: 73(8), P. 1621 - 1642

Published: July 15, 2024

ABSTRACT The water quality of drinking reservoirs directly impacts the supply safety for urban residents. This study focuses on Da Jing Shan Reservoir, a crucial source Zhuhai City and Macau Special Administrative Region. aim is to establish prediction model reservoirs, which can serve as vital reference plants when formulating their plans. In this research, after smoothing data using Hodrick-Prescott filter, we utilized long short-term memory (LSTM) network create Reservoir. Simulation calculations reveal that model's fitting degree consistently above 60%. Specifically, accuracy pH, dissolved oxygen (DO), biochemical demand (BOD) in aligns with actual results by more than 70%, effectively simulating reservoir's changes. Moreover, parameters such DO, BOD, total phosphorus, relative forecasting error LSTM less 10%, confirming validity. offer an essential predicting

Language: Английский

Citations

1