Dynamic and explainable fish mortality prediction under low-concentration ammonia nitrogen stress

Wu, Yao; Wang, Xiaochan; Wang, Lin; Zhang, Xiaolei; Shi, Yinyan; Jiang, Ye

doi:10.1016/j.biosystemseng.2023.03.003

Cited by 10 publications

(3 citation statements)

References 38 publications

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“…This accuracy rate is comparable to those reported in the literature. For example, in a study in China in which water quality was taken into account, the root mean square error of prediction was only 0.3 and the accuracy was 81% (Wu et al, 2023). Dilmi and Ladjal (2021), who integrated LSTM with linear discriminant analysis, found that LSTM combined with independent component analysis gave the best performance, with 99.72% accuracy for water quality classification.…”

Section: Model Validationmentioning

confidence: 99%

“…Runoff and turbulence caused by waterfalls and storms can lead to immediate changes in turbidity and the concentrations of nutrients such as NH 3 -N (Terada, 2022). In a study of an artificial fish farm, fish mortality increased to 40% when the NH 3 -N concentration was sustained at 23 mg/L for 96 h (Wu et al, 2023). Chen et al (2022) found that an NH 3 -N concentration exceeding 12 mg/L inhibited the metabolic pathway of cuttlefish (Sepia pharaonis).…”

Section: Combined Impacts Of Meteorological Conditions and Water Qual...mentioning

confidence: 99%

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Forecasting fish mortality from water and air quality data using deep learning models

Ting,

Chen

2024

J of Env Quality

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High rate of aquatic mortality incidents recorded in Taiwan and worldwide is creating an urgent demand for more accurate fish mortality prediction. Present study innovatively integrated air and water quality data to measure water quality degradation, and utilized deep learning methods to predict accidental fish mortality from the data. Keras library was used to build multilayer perceptron and long short‐term memory models for training purposes, and the models’ accuracies in fish mortality prediction were compared with that of the naïve Bayesian classifier. Environmental data from the 5 days before a fish mortality event proved to be the most important data for effective model training. Multilayer perceptron model reached an accuracy of 93.4%, with a loss function of 0.01, when meteorological and water quality data were jointly considered. It was found that meteorological conditions were not the sole contributors to fish mortality. Predicted fish mortality rate of 4.7% closely corresponded to the true number of fish mortality events during the study period, that is, four. A significant surge in fish mortality, from 20% to 50%, was noted when the river pollution index increased from 5.36 to 6.5. Moreover, the probability of fish mortality increased when the concentration of dissolved oxygen dropped below 2 mg/L. To mitigate fish mortality, ammonia nitrogen concentrations should be capped at 5 mg/L. Dissolved oxygen concentration was found to be the paramount factor influencing fish mortality, followed by the river pollution index and meteorological data. Results of the present study are expected to aid progress toward achieving the Sustainable Development Goals and to increase the profitability of water resources.

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Section: Model Validationmentioning

confidence: 99%

Section: Combined Impacts Of Meteorological Conditions and Water Qual...mentioning

confidence: 99%

Forecasting fish mortality from water and air quality data using deep learning models

Ting,

Chen

2024

J of Env Quality

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“…Previous studies have shown that one-dimensional CNN has strong feature extraction capabilities in temporal data processing [42]. Depending on the monitored historical changes of WBIA signals, gated recurrent units (GRUs) allow each recurrent unit to be adaptively captured independently at different time scales to better evaluate fish health status [43]. GRUs also combine the input gate and forgetting gate of long short-term memory (LSTM) into an update gate, and change the output gate into a reset gate, which makes it have a simpler structure.…”

Section: Deep Learning Modelsmentioning

confidence: 99%

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions

Zhang,

Chen,

Feng

et al. 2023

Sensors

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(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.

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Dietary inulin benefits on growth, digestive ability and intestinal microbiota in yellow catfish exposed to ammonia

Zhang,

Wang,

Qin

et al. 2023

Aquaculture Reports

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Dynamic and explainable fish mortality prediction under low-concentration ammonia nitrogen stress

Cited by 10 publications

References 38 publications

Forecasting fish mortality from water and air quality data using deep learning models

Forecasting fish mortality from water and air quality data using deep learning models

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions

Dietary inulin benefits on growth, digestive ability and intestinal microbiota in yellow catfish exposed to ammonia

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