基于BP神经网络的潇河流域水质预测

针对传统水质预测方法存在预测精度不理想以及对实测数据要求较高的问题,建立基于BP神经网络的水质预测模型,以掌握研究流域未来一定时段的水环境质量情况.模型以潇河流域6个水质监测断面2017年1月-2020年5月的重铬酸盐指数和高锰酸盐指数的浓度作为训练集,以2020年6月-2020年8月的水质数据作为验证集进行模拟与预测...

Water quality prediction of the Xiaohe River Basin based on BP neural network model

Conventional water quality prediction methods have the disadvantages of poor prediction accuracy and high requirements for monitored data, in order to have a better grasp of the future water environment quality of the Xiaohe River Basin over a certain period of time, a BP neural network model was established to predict the concentration of water quality indices. The indices of COD_Cr and COD_Mnof six water quality monitoring sections in the basin from January 2017 to May 2020 were selected as the training set and the water quality data from June to August 2020 as the validation set for the model. The simulation results show that the average relative error of the water quality index of each section simulated by the trained BP neural network model was less than 7%, and all the correlation coefficients exceeded 0.98. The average relative errors of the water quality indices in the verification set were all less than 18%. The prediction accuracy of the proposed model is high and satisfactory, so it can be used for the water quality prediction of Xiaohe River Basin.