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

и другие.

AQUA - Water Infrastructure Ecosystems and Society, Год журнала: 2024, Номер 73(8), С. 1621 - 1642

Опубликована: Июль 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

Язык: 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

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105182 - 105182

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133485 - 133485

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

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

и другие.

Journal of Marine Science and Engineering, Год журнала: 2024, Номер 12(6), С. 1013 - 1013

Опубликована: Июнь 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.

Язык: Английский

Процитировано

2

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

Yuanning Zhang

и другие.

Journal of Hydrology, Год журнала: 2024, Номер unknown, С. 132081 - 132081

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

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

и другие.

AQUA - Water Infrastructure Ecosystems and Society, Год журнала: 2024, Номер 73(8), С. 1621 - 1642

Опубликована: Июль 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

Язык: Английский

Процитировано

1