Study on Prediction Model of Dissolved Oxygen about Water Quality Monitoring System Based on BP Neural Network

Yan, Jing Xin; Yu, Li; Mao, Wen Wu; Cao, Shou Qi

doi:10.4028/www.scientific.net/amr.912-914.1407

Cited by 11 publications

(4 citation statements)

References 3 publications

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“…Due to the limitation of unstable prediction performance, few studies have used such methods. In [10], the back-propagated (BP) neural network is used to predict the changing trend of DO using historical monitoring data. Liu et al [11] utilized the wavelet analysis method to de-noise and decompose the original DO sequences, and proposed the hybrid WA-CPSO-LSSVR for DO content forecasting.…”

Section: A Single 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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Section: A Single Parameter Predictionmentioning

confidence: 99%

“…In technical literature, DO prediction methods can be categorized into two types: single parameter prediction [10]- [13] and multi-parameter prediction [14]- [22]. In the former type, the prediction of DO just considers the change law of historical dissolved oxygen data.…”

Section: Introductionmentioning

confidence: 99%

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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show abstract

“…Ding applied BP for water quality forecast in Qiantang River, China, and the results showed that the forecast accuracy can be improved more effectively than the one-dimension water quality models [30]. Yan proposed an improved BP model for dissolved oxygen forecasting, the results showed that the forecast data could fit the real monitored data very well [31].…”

Section: The Back Propagation Neural Network Of Water Quality Forecastmentioning

confidence: 99%

“…According to nonlinear regression theory, the joint probability density function of the random variable x and the random variable y is defined as f(X,y), the observed values of x is denoted as X. Y is defined as the forecast output of variable y when the X is the input variable. The conditional mean of y can be calculated by Equation (31).…”

Section: The Theory Of Grnn Modelmentioning

confidence: 99%

Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network

Shao

Nong

Tan

et al. 2018

Water

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Water quality forecast is a critical part of water security management. Spatiotemporal and multifactorial variations make water quality very complex and changeable. In this article, a novel model, which was based on back propagation neural network that was optimized by the Cuckoo Search algorithm (hereafter CS-BP model), was applied to forecast daily water quality of the Middle Route of South-to-North Water Diversion Project of China. Nine water quality indicators, including conductivity, chlorophyll content, dissolved oxygen, dissolved organic matter, pH, permanganate index, turbidity, total nitrogen, and water temperature were the predictand. Seven external environmental factors, including air temperature, five particulate matter (PM2.5), rainfall, sunshine duration, water flow, wind velocity, and water vapor pressure were the default predictors. A data pre-processing method was applied to select pertinent predictors. The results show that the CS-BP model has the best forecast accuracy, with the Mean Absolute Percentage Errors (MAPE) of 0.004%–0.33%, and the lowest Root Mean Square Error (RMSE) of each water quality indicator in comparison with traditional Back Propagation (BP) model, General Regression Neural Network model and Particle Swarm Optimization-Back Propagation model under default data proportion, 150:38 (training data: testing data). When training data reduced from 150 to 140, and from 140 to 130, the CS-BP model still produced the best forecasts, with the MAPEs of 0.014%–0.057% and 0.004%–1.154%, respectively. The results show that the CS-BP model can be an effective tool in daily water quality forecast with limited observed data. The improvement of the Cuckoo Search algorithm such as calculation speed, the forecast errors reduction of the CS-BP model, and the large-scale impacts such as land management on different water quality indicators, will be the focus of future research.

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Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network

Cao

Liu

Wang³

et al. 2020

Aquacultural Engineering

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Study on Prediction Model of Dissolved Oxygen about Water Quality Monitoring System Based on BP Neural Network

Cited by 11 publications

References 3 publications

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

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

Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network

Prediction of dissolved oxygen in pond culture water based on K-means clustering and gated recurrent unit neural network

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