Abstract
Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.
Highlights
Rapid population growth, industrialization, and the use of fertilizers and pesticides for agricultural purposes have made the water environment problem increasingly serious [1,2]
We establish a double hidden layer deep belief network (DBN) network structure composed of RBM1 and RBM2, it is found that the structural parameter of 8-12-4-1 is the best after many iteration experiments, where the number of visible layer neurons is 8, the number of RBM1 hidden layer neurons is 12, the number of RBM2 hidden layer neurons is 4, and the least squares support vector regression (LSSVR) output layer is 1
It is found that the optimal parameter combination of LSSVR is C = 96 and δ = 1.5, utilized the optimal prediction model to predict the total nitrogen (TN) content
Summary
Industrialization, and the use of fertilizers and pesticides for agricultural purposes have made the water environment problem increasingly serious [1,2]. Mudan River using hydrodynamic and water quality model based on EFDC [8] These water quality simulation models need to consider the influence of physical, chemical, biological, and other external environments on the water body, so the modeling is complicated. In 1971, a mathematical model was established, taking advantage of mathematical statistics to predict river water quality, focusing on the “black box” method without involving chemical, biological, and physical relationships [10]. Applied the deep belief network model in 2015 to analyze and predict the chemical eigenvalues of water, especially DO and pH, their research indicates that deep learning techniques can provide more accurate results than supervised learning-based techniques. We proposed a new deep learning method that combines DBN neural network optimized by PSO with least squares support vector regression (LSSVR) to predict water quality parameters. Materials and Methodology method proposed in this paper has a higher accuracy of prediction than the other four methods
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