基于人工神经网络的贝尔湖水质预测模型

A Water Quality Prediction Model for Buir Lake Based on Artificial Neural Network

为提高贝尔湖水质预测精度,在避免人工蜂群算法局部最优和早熟的同时,提出了一种改进的人工蜂群算法ABC算法,对BP神经网络的贝尔湖水质预测模型进行优化。以2018-2019年贝尔湖水质监测数据为研究对象,采用改进的ABC算法对BP神经网络的权重和阈值进行优化,建立水质等级预测模型。研究结果表明,改进的ABC-BP算法与GA-BP、PSO-BP和ABC-BP算法进行比较,其预测精度最高,收敛速度更快,稳定性更强,鲁棒性更好。

In order to improve the accuracy of water quality prediction in Buir Lake,an improved artificial bee colony algorithm ABC algorithm was proposed to optimize the water quality prediction model of Buir Lake based on BP neural network while avoiding local optimization and premature maturation of the artificial bee colony algorithm.Taking the water quality monitoring data of Buir Lake from 2018 to 2019 as the research object,an improved ABC algorithm was used to optimize the weights and thresholds of the BP neural network,and a water quality grade prediction model was established.The research results show that compared with GA-BP,PSO-BP and ABC-BP algorithm,the improved ABC-BP algorithm has the highest prediction accuracy,faster convergence speed,stronger stability,and better robustness.