基于灰狼群智能最优化的神经网络PM2.5浓度预测

针对目前PM_2.5浓度测量成本高和测量过程繁杂等问题，建立了基于灰狼群智能最优化算法的神经网络预测模型。从非机理模型的角度，结合气象因素和空气污染物对上海市的PM_2.5浓度进行预测，并使用平均影响值分析了影响PM_2.5浓度的重要因素。使用灰狼群智能算法优化神经网络的过程中，引入了检验样本实时监控训练过程以避免发生"过训练"现象，确保建立的神经网络模型具有较好的泛化能力。实验结果表明：PM₁₀对PM_2.5的影响最为显著，其次是CO和前一天PM_2.5。选取2016年11月1日-12日的数据进行验证，其平均相对误差为13.46%，平均绝对误差为8μg/m³，与粒子群算法优化的神经网络、BP神经网络模型及支持向量回归（SVR）模型的误差相比，平均相对误差分别下降了约3个百分点、5个百分点和1个百分点。因此，使用灰狼算法优化的神经网络更适合上海市PM_2.5浓度的预测和空气质量的预报。

Neural network model for PM2.5 concentration prediction by grey wolf optimizer algorithm

Focusing on high cost and complicated process of the fine particulate matter (PM_2.5) measurement system, a neural network model based on grey wolf optimizer algorithm was established. From the perspective of non-mechanism model, the daily PM_2.5 concentration in Shanghai was forecasted with meteorological factors and air pollutants, and the important factors were analyzed by mean impact value. To avoid the "over training" and ensure the generalization ability, the validation datasets were used to monitor the training process. The experimental results show that the most significant factors that affecting the PM_2.5 concentration are PM₁₀, and then are the CO and the previous day's PM_2.5. Based on the datasets obtained from November 1, 2016 to November 12, the relative average error of the proposed model is 13.46%, the absolute average error is 8μg/m³; the relative average error of it is decreased by about 3 percentage points, 5 percentage points and 1 percentage points compared with the prediction models based on Particle Swarm Optimization (PSO), BP neural network and Support Vector Regression (SVR). The neural network model based on the grey wolf optimizer algorithm is more suitable for forecasting PM_2.5concentration and air quality in Shanghai.