A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Yan, Jianzhuo; Gao, Ya; Yu, Yongchuan; Xu, Hongxia; Xu, Zongbao

doi:10.3390/w12071929

Cited by 35 publications

(24 citation statements)

References 32 publications

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“…Taken as a whole, by verifying on stations 2 and 3, we can conclude that the model proposed in this paper has the transferability when predicting the water quality data of different stations, which fully proves the generalization and stability of the model to predict time series water quality data. In the article by [33], the water quality data of the Sanhedong Bridge in the Hai River Basin was used as the research object, and the PSO-DBN-LSSVR model was built to predict total nitrogen (TN). To further illustrate that the 1-DRCNN-BiGRU model used in this paper has higher prediction accuracy for time series data, we compared the TN prediction results of stations 1-3 with those in the literature [33], as shown in Table 5.…”

Section: Resultsmentioning

confidence: 99%

“…In the article by [33], the water quality data of the Sanhedong Bridge in the Hai River Basin was used as the research object, and the PSO-DBN-LSSVR model was built to predict total nitrogen (TN). To further illustrate that the 1-DRCNN-BiGRU model used in this paper has higher prediction accuracy for time series data, we compared the TN prediction results of stations 1-3 with those in the literature [33], as shown in Table 5. The modeling method based on 1-DRCNN and BiGRU in this paper had the smallest prediction error (the mean absolute percentage error, MAPE) and a larger coefficient of determination (R 2 ) when predicting total nitrogen, which fully demonstrates that this method performs well in predicting water quality time series and has more practical significance.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the Selu activation function was selected and expressed by Equation (6). Among them, α and β are constant values; β = 1.05070098 and α = 1.67326324, respectively [33].…”

Section: Bi-directional Gated Recurrent Unitmentioning

confidence: 99%

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Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Yan

Liu

et al. 2021

Water

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The current global water environment has been seriously damaged. The prediction of water quality parameters can provide effective reference materials for future water conditions and water quality improvement. In order to further improve the accuracy of water quality prediction and the stability and generalization ability of the model, we propose a new comprehensive deep learning water quality prediction algorithm. Firstly, the water quality data are cleaned and pretreated by isolation forest, the Lagrange interpolation method, sliding window average, and principal component analysis (PCA). Then, one-dimensional residual convolutional neural networks (1-DRCNN) and bi-directional gated recurrent units (BiGRU) are used to extract the potential local features among water quality parameters and integrate information before and after time series. Finally, a full connection layer is used to obtain the final prediction results of total nitrogen (TN), total phosphorus (TP), and potassium permanganate index (COD-Mn). Our prediction experiment was carried out according to the actual water quality data of Daheiting Reservoir, Luanxian Bridge, and Jianggezhuang at the three control sections of the Luan River in Tangshan City, Hebei Province, from 5 July 2018 to 26 March 2019. The minimum mean absolute percentage error (MAPE) of this method was 2.4866, and the coefficient of determination (R2) was able to reach 0.9431. The experimental results showed that the model proposed in this paper has higher prediction accuracy and generalization than the existing LSTM, GRU, and BiGRU models.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Yan

Liu

et al. 2021

Water

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“…Several models based on different prediction methods have been developed for DO concentration forecasting in aquaculture ecosystems [11][12][13][14][15][16][17]. Xiao et al [11] applied back propagation (BP) NN method with the combination of purelin, logsig, and tansig activation functions to propose a prediction model for DO concentration in aquaculture.…”

Section: Related Literature Reviewmentioning

confidence: 99%

“…Liu et al [15] proposed a prediction model for water quality in smart mariculture with deep Bi-directional Stacked Simple Recurrent Unit (Bi-S-SRU) learning network. Yan et al [16] applied deep belief network and least squares support vector regression (LSSVR) machine to propose a forecasting model based on cross-section water quality. Furthermore, Liu et al [17] used support vector regression (SVR) machine to propose a hybrid forecasting approach with genetic algorithm optimization for aquaculture ponds DO content.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze¹,

Ajmal²

2020

Preprint

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Dissolved Oxygen (DO) concentration is a vital parameter that indicates water quality. DO short term forecasting using time series analysis on data collected from an aquaculture pond is presented here. This can provide data support for an early warning system for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD) based LSTM (Long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that are strongly correlated with the original sensor data and integrate both into inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. Performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, three statistical performance indices were adopted: MAE, MSE, and RMSE. Results presented for short term (12-hour period) and long term (1-month period) give a strong indication of suitability of this method for forecasting DO values.

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Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

Varadharajan

Appling

Arora

et al. 2022

Hydrological Processes

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The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub‐daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state‐of‐the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model‐data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge‐guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision‐relevant predictions of riverine water quality.

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A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Cited by 35 publications

References 32 publications

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Water Quality Prediction in the Luan River Based on 1-DRCNN and BiGRU Hybrid Neural Network Model

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

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