Hierarchical Network-Based Multi-Task Learning Method for Fishway Water Level Prediction

SU Xin; QIN Zijian; LÜ Jia; QIN Mingyu

doi:10.11999/JEIT241003

Volume 47 Issue 6

Jun. 2025

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SU Xin, QIN Zijian, LÜ Jia, QIN Mingyu. Hierarchical Network-Based Multi-Task Learning Method for Fishway Water Level Prediction[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1950-1965. doi: 10.11999/JEIT241003

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SU Xin, QIN Zijian, LÜ Jia, QIN Mingyu. Hierarchical Network-Based Multi-Task Learning Method for Fishway Water Level Prediction[J]. Journal of Electronics & Information Technology, 2025, 47(6): 1950-1965. doi: 10.11999/JEIT241003

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Hierarchical Network-Based Multi-Task Learning Method for Fishway Water Level Prediction

doi: 10.11999/JEIT241003 cstr: 32379.14.JEIT241003

SU Xin¹,
QIN Zijian¹,
LÜ Jia^{2
,
,},
QIN Mingyu¹

1.
College of Information Science and Engineering, Hohai University, Changzhou 213022, China
2.
College of Artificial Intelligence and Automation, Hohai University, Changzhou 213022, China

Funds: The National Natural Science Foundation of China (62371181), Changzhou Science and Technology International Cooperation Program (CZ20230029)

Received Date: 2024-11-11
Rev Recd Date: 2025-04-02

Available Online: 2025-04-21

Publish Date: 2025-06-10

Abstract

Abstract

Objective The construction of dams and other large-scale water infrastructure projects has significant ecological consequences, particularly affecting fish migration patterns. These environmental changes pose substantial challenges to biodiversity conservation and resource management. One of the key challenges is the accurate and real-time prediction of water levels in fish passages, which is essential for mitigating the negative effects of dams on fish migration, maintaining ecological balance, and ensuring the sustainability of aquatic species. Traditional water level monitoring systems often face limitations, such as insufficient coverage, lack of real-time predictive capabilities, and an inability to capture complex temporal dependencies in water level fluctuations, leading to inaccurate or delayed predictions. Furthermore, the processing of long-term, high-dimensional water level data in dynamic environments remains a critical gap in existing systems. To address these issues, this study proposes a Hierarchical Network-based Fish Passage Monitoring System (HNFMS) and a novel Multi-Task (MT) learning model, Adaptive Sequence Self-Organizing Map Transformation based on Variational Mode Decomposition (AS-SOMVT). The HNFMS aims to enhance both the efficiency and coverage of water level monitoring by providing comprehensive and timely data. The AS-SOMVT model employs auxiliary sequences to improve prediction accuracy and manage dynamic, multi-dimensional water level data in real time. Through these innovations, this study aims to enhance fish passage monitoring, mitigate the ecological impact of dam construction on fish migration, and provide a robust tool for ecological conservation and resource management. Methods The HNFMS integrates a hierarchical network structure to improve both the efficiency and coverage of water level monitoring. To address the complex temporal dependencies inherent in water level fluctuations, this study introduces the AS-SOMVT MT learning model. This model leverages auxiliary sequences to enhance the ability to capture complex temporal relationships, ensuring accurate water level predictions. The approach enables real-time processing of multi-dimensional water level data, effectively managing the complexity of fluctuating water levels across varying conditions. Additionally, the study incorporates an Auxiliary Sequence Self-Organizing Map (AS-SOM) algorithm to optimize prediction efficiency for long sequences, further enhancing the model’s capacity to process high-dimensional, multi-variate water level data. The model also integrates a Variational Mode Decomposition (VMD) technique, which decomposes complex water level time series into different frequency components. This approach extracts key feature patterns with higher predictive value while filtering out noise and redundant information, improving data quality and enhancing the model’s predictive performance. To increase the robustness of the system, the study incorporates an ensemble of diverse machine learning techniques, including both deep learning models and traditional statistical methods. This ensemble is designed to adapt to varying environmental conditions and ensure robust performance across different situations. Results and Discussions The AS-SOMVT model significantly outperforms traditional models in water level prediction accuracy. The integration of auxiliary sequences allows the model to capture complex temporal dependencies more effectively, resulting in more reliable real-time predictions (Fig. 4). Furthermore, the incorporation of VMD improves the model’s ability to remove noise and extract crucial features, enhancing its adaptability to dynamic water level changes in real-world environments. Ablation experiments demonstrate that removing key components, such as feature Relationship modeling (Rel), Attention Pooling (AP), or MT Learning, leads to a substantial decline in model performance. This highlights the essential role these components play in improving predictive accuracy and managing complex patterns. Specifically, the removal of any of these components results in a marked decrease in precision and stability, highlighting the collaborative contribution of these elements within the MT learning framework. In multi-dimensional water level prediction tasks, the AS-SOMVT model performs exceptionally well, especially in dynamic environments. Additionally, the hierarchical structure of the HNFMS substantially enhances monitoring efficiency and coverage, providing more accurate and comprehensive water level data through real-time model adjustments (Fig. 8). In comparative experiments, the AS-SOMVT model consistently outperforms traditional models, particularly in forecasting multi-dimensional water levels, establishing it as a powerful tool for large-scale, real-time monitoring applications (Table 4). Conclusions The proposed HNFMS, combined with the AS-SOMVT MT learning model, offers an effective solution for real-time, accurate water level prediction in fish passages. This innovative approach not only enhances the efficiency and coverage of water level monitoring systems but also provides a valuable tool for mitigating the ecological impacts of dam constructions on fish migration. The integration of auxiliary sequences into the MT learning model has proven to be a critical factor in improving predictive performance, opening new opportunities for ecological conservation. As concerns about the ecological impacts of water infrastructure projects grow, the development of more accurate and efficient water level monitoring systems becomes increasingly vital for informing policy decisions, designing fish-friendly structures, and enhancing aquatic ecosystem management. This study presents a scientifically significant and practically necessary solution for promoting sustainable environmental practices. The integration of advanced machine learning techniques, such as MT learning and VMD, ensures the system can handle both short-term and long-term water level prediction tasks, addressing the complexities of environmental dynamics in real time. This research, therefore, makes a significant contribution to the field of environmental monitoring and provides essential insights for the future development of eco-friendly infrastructure.
- Fish passage monitoring,
- Hierarchical network,
- Time series analysis,
- Multi-Task (MT) learning,
- Water level prediction

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References(28)

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