Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm

Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality dat...

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Published in:	Scientific reports Vol. 15; no. 1; pp. 30790 - 18
Main Authors:	Shi, Pei, Tang, Mingjie, Wang, Quan, Ma, Xiaofei
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 21.08.2025 Nature Publishing Group Nature Portfolio
Subjects:	639/705/1042 639/705/117 Accuracy Algorithms Aquatic ecosystems Artificial intelligence Combinatorial models Datasets Deep learning Dissolved oxygen Dissolved oxygen prediction Feature selection Genetic algorithms Humanities and Social Sciences Long short-term memory Machine learning multidisciplinary Neural networks Optimization algorithms Oxygen Prediction models Research methodology Science Science (multidisciplinary) Sparrow optimization algorithm Temporal convolutional networks Time series Trends Water quality Wavelet transforms Sparrow optimization algorithm Combinatorial models Temporal convolutional networks Dissolved oxygen prediction
ISSN:	2045-2322, 2045-2322
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Abstract	Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality data and insufficient feature extraction. To address these challenges, this paper proposes a dissolved oxygen prediction method based on an improved sparrow search algorithm optimized TCN- BiLSTM (SMI-TCN BiLSTM). Initially, the Savitzky-Golay (SG) filter is employed to denoise the water quality data, producing smoother and more consistent datasets. Next, the Maximum Information Coefficient (MIC) is applied to quantify the correlation between input features, enabling the identification and selection of key influencing factors. In addition, the traditional Temporal Convolutional Network (TCN) often fails to capture the dynamic fluctuations present in DO data, resulting in suboptimal prediction performance. To overcome this limitation, a Bi-directional Long Short-Term Memory (BiLSTM) network is integrated into the TCN framework, forming a TCN-BiLSTM prediction module. This module effectively captures both forward and backward temporal dependencies, improving the model’s ability to track the dynamic trends in the data and enhancing its prediction accuracy. Finally, to address the stochastic nature of hyperparameter optimization in the TCN-BiLSTM module, we introduce an improved Sparrow Search Algorithm (ISSA). The ISSA is applied to optimize the hyperparameters of the TCN-BiLSTM model, thereby improving the overall prediction performance. To validate the proposed model, experiments are conducted on real datasets and compared with other water quality prediction models. The experimental results demonstrate that our method achieves the best prediction results.
AbstractList	Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality data and insufficient feature extraction. To address these challenges, this paper proposes a dissolved oxygen prediction method based on an improved sparrow search algorithm optimized TCN- BiLSTM (SMI-TCN BiLSTM). Initially, the Savitzky-Golay (SG) filter is employed to denoise the water quality data, producing smoother and more consistent datasets. Next, the Maximum Information Coefficient (MIC) is applied to quantify the correlation between input features, enabling the identification and selection of key influencing factors. In addition, the traditional Temporal Convolutional Network (TCN) often fails to capture the dynamic fluctuations present in DO data, resulting in suboptimal prediction performance. To overcome this limitation, a Bi-directional Long Short-Term Memory (BiLSTM) network is integrated into the TCN framework, forming a TCN-BiLSTM prediction module. This module effectively captures both forward and backward temporal dependencies, improving the model's ability to track the dynamic trends in the data and enhancing its prediction accuracy. Finally, to address the stochastic nature of hyperparameter optimization in the TCN-BiLSTM module, we introduce an improved Sparrow Search Algorithm (ISSA). The ISSA is applied to optimize the hyperparameters of the TCN-BiLSTM model, thereby improving the overall prediction performance. To validate the proposed model, experiments are conducted on real datasets and compared with other water quality prediction models. The experimental results demonstrate that our method achieves the best prediction results. Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality data and insufficient feature extraction. To address these challenges, this paper proposes a dissolved oxygen prediction method based on an improved sparrow search algorithm optimized TCN- BiLSTM (SMI-TCN BiLSTM). Initially, the Savitzky-Golay (SG) filter is employed to denoise the water quality data, producing smoother and more consistent datasets. Next, the Maximum Information Coefficient (MIC) is applied to quantify the correlation between input features, enabling the identification and selection of key influencing factors. In addition, the traditional Temporal Convolutional Network (TCN) often fails to capture the dynamic fluctuations present in DO data, resulting in suboptimal prediction performance. To overcome this limitation, a Bi-directional Long Short-Term Memory (BiLSTM) network is integrated into the TCN framework, forming a TCN-BiLSTM prediction module. This module effectively captures both forward and backward temporal dependencies, improving the model's ability to track the dynamic trends in the data and enhancing its prediction accuracy. Finally, to address the stochastic nature of hyperparameter optimization in the TCN-BiLSTM module, we introduce an improved Sparrow Search Algorithm (ISSA). The ISSA is applied to optimize the hyperparameters of the TCN-BiLSTM model, thereby improving the overall prediction performance. To validate the proposed model, experiments are conducted on real datasets and compared with other water quality prediction models. The experimental results demonstrate that our method achieves the best prediction results.Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality data and insufficient feature extraction. To address these challenges, this paper proposes a dissolved oxygen prediction method based on an improved sparrow search algorithm optimized TCN- BiLSTM (SMI-TCN BiLSTM). Initially, the Savitzky-Golay (SG) filter is employed to denoise the water quality data, producing smoother and more consistent datasets. Next, the Maximum Information Coefficient (MIC) is applied to quantify the correlation between input features, enabling the identification and selection of key influencing factors. In addition, the traditional Temporal Convolutional Network (TCN) often fails to capture the dynamic fluctuations present in DO data, resulting in suboptimal prediction performance. To overcome this limitation, a Bi-directional Long Short-Term Memory (BiLSTM) network is integrated into the TCN framework, forming a TCN-BiLSTM prediction module. This module effectively captures both forward and backward temporal dependencies, improving the model's ability to track the dynamic trends in the data and enhancing its prediction accuracy. Finally, to address the stochastic nature of hyperparameter optimization in the TCN-BiLSTM module, we introduce an improved Sparrow Search Algorithm (ISSA). The ISSA is applied to optimize the hyperparameters of the TCN-BiLSTM model, thereby improving the overall prediction performance. To validate the proposed model, experiments are conducted on real datasets and compared with other water quality prediction models. The experimental results demonstrate that our method achieves the best prediction results. Abstract Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and sustainable utilization of these ecosystems. However, current DO prediction models often struggle with issues such as noise in the water quality data and insufficient feature extraction. To address these challenges, this paper proposes a dissolved oxygen prediction method based on an improved sparrow search algorithm optimized TCN- BiLSTM (SMI-TCN BiLSTM). Initially, the Savitzky-Golay (SG) filter is employed to denoise the water quality data, producing smoother and more consistent datasets. Next, the Maximum Information Coefficient (MIC) is applied to quantify the correlation between input features, enabling the identification and selection of key influencing factors. In addition, the traditional Temporal Convolutional Network (TCN) often fails to capture the dynamic fluctuations present in DO data, resulting in suboptimal prediction performance. To overcome this limitation, a Bi-directional Long Short-Term Memory (BiLSTM) network is integrated into the TCN framework, forming a TCN-BiLSTM prediction module. This module effectively captures both forward and backward temporal dependencies, improving the model’s ability to track the dynamic trends in the data and enhancing its prediction accuracy. Finally, to address the stochastic nature of hyperparameter optimization in the TCN-BiLSTM module, we introduce an improved Sparrow Search Algorithm (ISSA). The ISSA is applied to optimize the hyperparameters of the TCN-BiLSTM model, thereby improving the overall prediction performance. To validate the proposed model, experiments are conducted on real datasets and compared with other water quality prediction models. The experimental results demonstrate that our method achieves the best prediction results.
ArticleNumber	30790
Author	Ma, Xiaofei Tang, Mingjie Wang, Quan Shi, Pei
Author_xml	– sequence: 1 givenname: Pei surname: Shi fullname: Shi, Pei organization: School of Internet of Things Engineering, Wuxi University, School of Automation, Nanjing University of Information Science and Technology – sequence: 2 givenname: Mingjie surname: Tang fullname: Tang, Mingjie organization: School of Automation, Nanjing University of Information Science and Technology – sequence: 3 givenname: Quan surname: Wang fullname: Wang, Quan email: wangquan@cwxu.edu.cn organization: School of Internet of Things Engineering, Wuxi University, School of Automation, Nanjing University of Information Science and Technology – sequence: 4 givenname: Xiaofei surname: Ma fullname: Ma, Xiaofei organization: Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences
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Keywords	Sparrow optimization algorithm Combinatorial models Temporal convolutional networks Dissolved oxygen prediction
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Snippet	Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection and... Abstract Dissolved oxygen (DO) is a crucial indicator of water quality in river ecosystems, and its accurate prediction plays a vital role in the protection...
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StartPage	30790
SubjectTerms	639/705/1042 639/705/117 Accuracy Algorithms Aquatic ecosystems Artificial intelligence Combinatorial models Datasets Deep learning Dissolved oxygen Dissolved oxygen prediction Feature selection Genetic algorithms Humanities and Social Sciences Long short-term memory Machine learning multidisciplinary Neural networks Optimization algorithms Oxygen Prediction models Research methodology Science Science (multidisciplinary) Sparrow optimization algorithm Temporal convolutional networks Time series Trends Water quality Wavelet transforms
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Title	Optimization of TCN-BiLSTM for dissolved oxygen prediction based on improved sparrow search algorithm
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