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.

中文翻译：

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基于深度置信网络和最小二乘 SVR 的断面水质预测模型

最近，淡水资源的质量受到众多污染物的威胁。水质预测是控制和减少水污染的重要工具。通过利用深度学习卓越的大数据处理能力，可以提高预测的准确性。本文提出了一种基于深度信念网络（DBN）模型的水质预测方法。首先，利用粒子群优化（PSO）算法对深度置信网络的网络参数进行优化，即在多尺度下提取水质时间序列数据的特征向量。然后，结合最小二乘支持向量回归(LSSVR)机作为模型的顶层预测层，提出了一种新的水质预测模型PSO-DBN-LSSVR。开发的模型根据平均绝对误差 (MAE)、平均绝对百分比误差 (MAPE)、均方根误差 (RMSE) 和决定系数 (R 2 ) 进行估值。结果表明，与传统的反向传播（BP）神经网络、LSSVR、DBN神经网络和DBN-LSSVR组合模型相比，本文提出的模型能够准确预测水质参数，并具有更好的水质参数鲁棒性。