Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)

Li, Wuyan; Wu, Hao; Zhu, Nanyang; Jiang, Yongnian; Tan, Jinglu; Guo, Ya

doi:10.1016/j.inpa.2020.02.002

Cited by 96 publications

(45 citation statements)

References 28 publications

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“…GRU is lightweight in terms of computational power and training time compared with LSTM [ 52 ]. There are two gates—namely, ab update gate and a reset gate—in the GRU architecture.…”

Section: Methodology Of Proposed Nsga-iii Optimized Rnn-gru-lstm Modelmentioning

confidence: 99%

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

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Road traffic accidents have been listed in the top 10 global causes of death for many decades. Traditional measures such as education and legislation have contributed to limited improvements in terms of reducing accidents due to people driving in undesirable statuses, such as when suffering from stress or drowsiness. Attention is drawn to predicting drivers’ future status so that precautions can be taken in advance as effective preventative measures. Common prediction algorithms include recurrent neural networks (RNNs), gated recurrent units (GRUs), and long short-term memory (LSTM) networks. To benefit from the advantages of each algorithm, nondominated sorting genetic algorithm-III (NSGA-III) can be applied to merge the three algorithms. This is named NSGA-III-optimized RNN-GRU-LSTM. An analysis can be made to compare the proposed prediction algorithm with the individual RNN, GRU, and LSTM algorithms. Our proposed model improves the overall accuracy by 11.2–13.6% and 10.2–12.2% in driver stress prediction and driver drowsiness prediction, respectively. Likewise, it improves the overall accuracy by 6.9–12.7% and 6.9–8.9%, respectively, compared with boosting learning with multiple RNNs, multiple GRUs, and multiple LSTMs algorithms. Compared with existing works, this proposal offers to enhance performance by taking some key factors into account—namely, using a real-world driving dataset, a greater sample size, hybrid algorithms, and cross-validation. Future research directions have been suggested for further exploration and performance enhancement.

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“…GRU is lightweight in terms of computational power and training time compared with LSTM [ 52 ]. There are two gates—namely, ab update gate and a reset gate—in the GRU architecture.…”

Section: Methodology Of Proposed Nsga-iii Optimized Rnn-gru-lstm Modelmentioning

confidence: 99%

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Chui¹,

Gupta

Liu

et al. 2021

Sensors

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show abstract

“…A structure is introduced using the real-time water quality indicators and operational information, where impact on survival rate, biomass, and production failure of aquaculture species are evaluated [9]. A prediction model using one feature of water called DO is presented for the aquatic creature [10]. A hardware is made for monitoring water quality factors including pH, temperature, and dissolved oxygen [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Fish survival prediction in an aquatic environment using random forest model

Islam

Kashem

Uddin

2021

IJ-AI

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In the real world, it is very difficult for fish farmers to select the perfect fish species for aquaculture in a specific aquatic environment. The main goal of this research is to build a machine learning that can predict the perfect fish species in an aquatic environment. In this paper, we have utilized a model using random forest (RF). To validate the model, we have used a dataset of aquatic environment for 11 different fishes. To predict the fish species, we utilized the different characteristics of aquatic environment including pH, temperature, and turbidity. As a performance metrics, we measured accuracy, true positive (TP) rate, and kappa statistics. Experimental results demonstrate that the proposed RF-based prediction model shows accuracy 88.48%, kappa statistic 87.11% and TP rate 88.5% for the tested dataset. In addition, we compare the proposed model with the state-of-art models J48, RF, k-nearest neighbor (k-NN), and classification and regression trees (CART). The proposed model outperforms than the existing models by exhibiting the higher accuracy score, TP rate and kappa statistics.

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“…Second, the forget gate controls the information lost in the cells. Lastly, the output gate performs the function of determining the final output value based on the input and memory of the cell 44,45 .…”

Section: Modelingmentioning

confidence: 99%

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Preprint

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The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

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Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU)

Cited by 96 publications

References 28 publications

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Extended-Range Prediction Model Using NSGA-III Optimized RNN-GRU-LSTM for Driver Stress and Drowsiness

Fish survival prediction in an aquatic environment using random forest model

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

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