Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction

Liu, Yeqi; Zhang, Qian; Song, Lihua; Chen, Yingyi

doi:10.1016/j.compag.2019.104964

Cited by 113 publications

(49 citation statements)

References 16 publications

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“…Reducing the computation problems because the weights of the input and hidden layer need not be adjusted [31,[45][46][47][48] CCNN Start with input and output layer without a hidden layer…”

Section: Mlpsmentioning

confidence: 99%

“…A constructive neural network that aims to solve the problems of the determination of potential neurons which are not relevant to the output layer [49] MNNs A special feedforward network Choosing the neural network which have the maximum similarity between the inputs and centroids of the cluster Solving the problem of low prediction accuracy [30,50] RNNs The RNNs are developed with the development of deep learning Solving the problems of long-term dependence which are not captured by the feedforward network [12,31,38,51,52] LSTMs Its structure is similar to RNNs Memory cell state is added to hidden layer Addressing the well-known vanishing gradient problem of RNNs [15,26,45,53,54] TLRN Its structure is similar to MLPs It has the local recurrent connections in the hidden layer…”

Section: Mlpsmentioning

confidence: 99%

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A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

Self Cite

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Water quality prediction plays an important role in environmental monitoring, ecosystem sustainability, and aquaculture. Traditional prediction methods cannot capture the nonlinear and non-stationarity of water quality well. In recent years, the rapid development of artificial neural networks (ANNs) has made them a hotspot in water quality prediction. We have conducted extensive investigation and analysis on ANN-based water quality prediction from three aspects, namely feedforward, recurrent, and hybrid architectures. Based on 151 papers published from 2008 to 2019, 23 types of water quality variables were highlighted. The variables were primarily collected by the sensor, followed by specialist experimental equipment, such as a UV-visible photometer, as there is no mature sensor for measurement at present. Five different output strategies, namely Univariate-Input-Itself-Output, Univariate-Input-Other-Output, Multivariate-Input-Other(multi), Multivariate-Input-Itself-Other-Output, and Multivariate-Input-Itself-Other (multi)-Output, are summarized. From results of the review, it can be concluded that the ANN models are capable of dealing with different modeling problems in rivers, lakes, reservoirs, wastewater treatment plants (WWTPs), groundwater, ponds, and streams. The results of many of the review articles are useful to researchers in prediction and similar fields. Several new architectures presented in the study, such as recurrent and hybrid structures, are able to improve the modeling quality of future development.

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“…Reducing the computation problems because the weights of the input and hidden layer need not be adjusted [31,[45][46][47][48] CCNN Start with input and output layer without a hidden layer…”

Section: Mlpsmentioning

confidence: 99%

Section: Mlpsmentioning

confidence: 99%

A Review of the Artificial Neural Network Models for Water Quality Prediction

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…These parameters include water quality parameters and meteorological parameters. Many studies have been conducted at present [14][15][16][17][18][19][20][21][22][23][24].…”

Section: B Multi-parameter Predictionmentioning

confidence: 99%

“…Liu et al [14] systematically discussed and compared the application of the attention-based RNN method in dissolved oxygen prediction using two water quality parameters, two soil parameters and six meteorological parameters. Liu et al [15] proposed a prediction model based on support vector regression (SVR) to solve the aquaculture water quality prediction in modern intensive river crab aquaculture management problem.…”

Section: B Multi-parameter Predictionmentioning

confidence: 99%

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An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

Kuang¹,

Shi

Hua³

et al. 2020

IEEE Access

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Water quality monitoring using Wireless Sensor Networks (WSNs) is essential in aquaculture water quality management. In the field of water quality monitoring, dissolved oxygen (DO) is a key parameter, and its prediction can provide decision support for aquaculture production, thereby reducing farming risk. However, it is difficult to build a precise prediction model, and existing methods of DO prediction neglect the importance of analyzing DO content. To address this problem, this study proposes a hybrid DO prediction model, named KIG-ELM, which is composed of K-means, improved genetic algorithm (IGA), and extreme learning machine (ELM). This model is based on edge computing architecture, in which data acquisition, processing and dissolved oxygen prediction are distributed in sensing nodes, routing nodes and server respectively. Sensing technique and clustering operation are applied in the process of data acquisition and processing. Meanwhile, an optimized extreme learning machine is implemented for DO prediction. We evaluate the efficiency and accuracy of proposed prediction approach in a practical aquaculture on massive water quality data. Experimental results show that the hybrid model achieves significant prediction results and can meet the needs of practical production and management.

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A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

Xue

et al. 2022

Int J of Intelligent Sys

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In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly. Accurately and effectively predicting the trends of future research topics can help researchers discover future research hotspots.However, due to the increasingly close correlation between various research themes, there is a certain dependency relationship between a large number of research themes. Viewing a single research theme in isolation and using traditional sequence problem processing methods cannot effectively explore the spatial dependencies between these research themes. To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model. Our model combines a graph convolutional neural network (GCN) and Temporal Convolutional Network (TCN), specifically, GCNs are used to learn the spatial dependencies of research topics a and use space dependence to strengthen spatial characteristics. TCN is used to learn the dynamics of research topics' trends. Optimization is based on the calculation of weighted losses based on time distance. Compared with the current mainstream sequence prediction models and similar spatiotemporal models on the paper datasets, experiments show that, in research topic prediction tasks, our model can effectively capture spatiotemporal relationships and the predictions outperform state-of-art baselines.

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Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction

Cited by 113 publications

References 16 publications

A Review of the Artificial Neural Network Models for Water Quality Prediction

A Review of the Artificial Neural Network Models for Water Quality Prediction

An Enhanced Extreme Learning Machine for Dissolved Oxygen Prediction in Wireless Sensor Networks

A scientific research topic trend prediction model based on multi‐LSTM and graph convolutional network

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