With the increasingly serious problem of surface water environmental safety, it is of great significance to study the changing trend of reservoir water quality, and it is necessary to establish a water quality prediction and early warning system for the management and maintenance of water resources. Aiming at the problem of water quality prediction in reservoirs, a CA-NARX algorithm is designed, which combines the improved dynamic clustering algorithm with the idea of machine learning and the forward dynamic regression neural network. The improved dynamic clustering algorithm is used to classify the eutrophication degree of waterbodies according to the total phosphorus and total nitrogen content. Considering four meteorological factors, air temperature, water temperature, water surface evaporation, and rainfall, synthetically for each water quality condition, the total phosphorus and total nitrogen in the waterbody are forecasted by an improved forward NARX dynamic regression neural network. Based on this, the CA-NARX prediction algorithm can realize short period water quality prediction. Compared with the traditional support vector regression machine model, improved GA-BP neural network, and exponential smoothing method, the CA-NARX model has the least prediction error.

随着地表水环境安全问题的日益严重，研究水库水质的变化趋势具有重要意义，建立水质预测预警系统对水资源的管理和维护具有重要意义。针对水库水质预测问题，设计了一种CA-NARX算法，将改进的动态聚类算法与机器学习思想和前向动态回归神经网络相结合。改进的动态聚类算法用于根据总磷和总氮含量对水体的富营养化程度进行分类。考虑到四个气象因素，气温，水温，水面蒸发和降雨，综合地，对于每种水质状况，通过改进的前向NARX动态回归神经网络预测水体中的总磷和总氮。基于此，CA-NARX预测算法可以实现短期水质预测。与传统的支持向量回归机模型，改进的GA-BP神经网络和指数平滑方法相比，CA-NARX模型的预测误差最小。