Prediction of dissolved oxygen concentration in aquatic systems based on transfer learning

Zhu, Nanyang; Ji, Xiang Feng; Tan, Jinglu; Jiang, Yongnian; Guo, Ya

doi:10.1016/j.compag.2020.105888

Cited by 33 publications

(10 citation statements)

References 25 publications

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“…Transfer learning (TL) improved model performance in a measurable way. Te TL technique provides accurate DO forecast of lake Y while managing more than a year of data collection time [25]. Deep learning (DL) technology has quickly gained popularity and is now being successfully applied in a number of industries, including aquaculture.…”

Section: State-of-art Reviewmentioning

confidence: 99%

Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications

Kaur

Adhikari

Krishnapriya

et al. 2023

Journal of Food Quality

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The growth of the fish is influenced by a variety of scientific factors. So, profit can be easily achieved by using some clever techniques, for example, maintaining the correct pH level along with the dissolved oxygen (DO) level and temperature, as well as turbidity for good growth of fish. Fully grown fish are generally sold at a good price because price of fish in the market is governed by weight as well as size of nurtured fish. Artificial intelligence (AI)-based systems may be created to regulate key water quality factors including salinity, dissolved oxygen, pH, and temperature. The software programme operates on an application server and is connected to multiparameter water quality meters in this system. This study examines smart fish farming methods that show how complicated science and technology may be simplified for use in seafood production. This research focuses on the use of artificial intelligence in fish culture in this setting. The technical specifics of DL approaches used in smart fish farming which includes data and algorithms as well as performance was also examined. In a nutshell, our goal is to provide academics and practitioners with a better understanding of the current state of the art in DL implementation in aquaculture, which will help them deploy smart fish farming applications as well their benefits.

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Section: State-of-art Reviewmentioning

confidence: 99%

Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications

Kaur

Adhikari

Krishnapriya

et al. 2023

Journal of Food Quality

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“…Recent studies have employed attention-LSTM in water quality modeling. Zhu et al. (2021) used a fusion model incorporating ResNet, BiLSTM, and multihead attention to predict DO concentrations in Lake Taihu.…”

Section: Lstm With Attention Mechanismmentioning

confidence: 99%

Long short-term memory models of water quality in inland water environments

Pyo,

Pachepsky,

Kim

et al. 2023

Water Research X

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“…The findings suggested that more targeted data collection is necessary to improve predictions. Zhu et al (2021) explored the use of deep learning and transfer learning to predict dissolved oxygen (DO) concentrations in aquatic systems. They developed a pre-trained model using a large dataset from one aquatic system and applied it to another system with less data.…”

Section: Introductionmentioning

confidence: 99%

Sequential GP-UCB Bayesian Optimization for Deep Neural Network Fine-Tuning in Dissolved Oxygen Prediction

Garabaghi,

Benzer,

Benzer

2024

Preprint

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Dissolved Oxygen (DO) is a key indicator of water quality, essential for sustaining aquatic ecosystems and human uses. Machine learning, particularly deep learning, is recognized as an effective approach for predicting DO levels by learning from data rather than requiring explicit human knowledge input. The effectiveness of deep learning models improves with fine-tuning of hyperparameters. Amongst hyperparameter tuning methods, Bayesian methods have gained particular interest for optimization. This study focuses on predicting DO levels in riverine environments using a Deep Neural Network model. The research employs a Gaussian Process Upper Confidence Bound (GP-UCB) Bayesian optimization technique to fine-tune hyperparameters, aiming for an optimal configuration. Comparative analysis is conducted between the optimized model and baseline model with default settings. Results indicate that the Bayesian-optimized model outperforms the baseline, particularly evident with moderately sized datasets. The findings underscore the pivotal role of Bayesian optimization in elevating model performance, exhibiting robust generalization capabilities while significantly reducing the need for manual parameter tuning. This successful application underscores a substantial methodological advancement in environmental management, particularly in predictive modelling for indicators of aquatic ecosystem health.

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Prediction of dissolved oxygen concentration in aquatic systems based on transfer learning

Cited by 33 publications

References 25 publications

Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications

Recent Advancements in Deep Learning Frameworks for Precision Fish Farming Opportunities, Challenges, and Applications

Long short-term memory models of water quality in inland water environments

Sequential GP-UCB Bayesian Optimization for Deep Neural Network Fine-Tuning in Dissolved Oxygen Prediction

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