基于主成分分析的GA-BP模型在城市需水预测中的应用研究

针对城市需水预测模型中需水量影响因子多、影响因子之间普遍存在多重共线问题,以及BP神经网络收敛速度慢、易陷入局部最优等缺点,提出一种由主成分分析、遗传算法及BP神经网络三者相结合的改进预测模型。以泰州市为实例,建立以主成分分析筛选需水量主要影响因子,遗传算法优化BP网络连接权值和阈值的需水预测模型,预测结果与BP神经网络预测模型预测结果做对比。结果表明:改进预测模型对泰州市2003-2014年需水量预测的平均相对误差为0.564%,最大相对误差为1.681%,精度优于BP神经网络预测模型;改进预测模型预测值与实际泰州市需水量吻合良好且训练速度更快、预测精度更高,可作为需水预测的一种有效方法。

Application of GA-BP model based on principal component analysis to urban water demand prediction

There are too many impact factors of water demand in the urban water demand prediction model and most of the factors are multicollinear. Besides, the BP neural network has slow convergence rate and easily gets into a local optimum. To tackle these problems, we proposed an improved prediction model by combining the principal component analysis (PCA), genetic algorithm (GA), and back propagation neural network (BPNN). Taizhou city was taken as a case for study. We established a water demand prediction model that selects the main impact factors of water demand by principal component analysis and optimizes the connection weights and thresholds of the BP neural network by genetic algorithm. The BP neural network prediction model was set up as the contrast model. The results showed that the average relative error and the maximum relative error of water demand prediction by the improved model in 2003-2014 in Taizhou city were 0.564% and 1.681% respectively. The precision was superior to that of the BP neural network prediction model. The results predicted by the GA-BP model matched with the actual water demand data of Taizhou city, and the model had faster calculation speed and higher precision. It can be used as an effective method for water demand prediction.