## 1 Introduction  
Human activities are considered to cause major pollution of water quality, which calls for urgent action around the world. Some of the main emission sources that can significantly affect surface water quality are discharge of hazardous substances from industrial processes and urban waste water, accidental hazardous substances pollution, and diffuse pollution originating from agricultural areas [1].  
Numerous water quality parameters are being measured to indicate lake water status and guide decision makers towards implementing optimal and sustainable measures. Dissolved oxygen (DO) content is one of the most important water quality parameters as it directly indicates the status of aquatic ecosystem and its ability to sustain aquatic life. DO is considered to be the most badly affected among all water quality parameters [1]. So far, many methods have been used to predict water quality: [2] use the grey relational method to predict the water quality and perform water quality evaluation of rivers in china; [3] developed a Bayesian approach to river WQM combined with inverse methods to support the practical adaptive water quality management under uncertainty of Hun-Taiz River in northeastern China; [4] used genetic algorithm (GA) and geographic information system (GIS) methods to calculate BOD and DO from the WQMM model; Approximately 85-90% of water quality prediction work has been completed using neural networks (NNs) [5]. For example, The ANN modeling technique's application for dynamic seawater quality prediction variation [6] was presented in combination with high-efficiency learning methods (general regression neural networks (GRNN) and multilayer perceptron (MLP)); [5] established a water quality prediction system combined principal component analysis (PCA), genetic algorithm (GA) and back propagation neural network (BPNN), a hybrid intelligent algorithm is designed for river water quality; [7] presented a flexible structure Radial Basis Function neural network (FS-RBFNN) for water quality prediction, and it can change its structure dynamically to maintain prediction accuracy. Also, [8] verified that the developed ANFIS model performs quite well for each data combination for BOD prediction with adequate performance. The learning abilities of an ANN and argumental abilities of fuzzy logic are combined to increase ANFIS prediction abilities [9]. Because of its strong self-learning ability, an artificial neural network can be widely used in the field of time series prediction. Nevertheless, ANN application carries some disadvantages, including the slow rate of learning and getting trapped in local minima [10; 11; 12]; therefore, many researchers are committed to the optimization of neural network parameters to solve these problems. Some researchers consider combining particle swarm optimization (PSO) with genetic algorithms to optimize neural networks and this has been applied in the field of nonlinear prediction successfully, such as in industrial manufacture [13], energy demand [14; 15], and biological engineering [16].  
In this work, we practically predict water quality parameters using the methods of BPNN, PSO-BPNN, GA-BPNN, and PSO-GA-BPNN, and analyze the algorithm theoretically. Based on the observed value, the performance of the models in prediction of lake water quality was evaluated to prove that PSO-GA-BPNN has a better prediction accuracy than others.