Air pollution may cause pernicious effects on human health, and is a widespread problem in the world. Air quality management systems have became an important research issue with strong implications for inhabitants’ health. Monitoring and forecasting of air quality indicators plays an important role in the management systems. Artificial intelligent techniques are successfully used in modelling of highly complex and nonlinear phenomena. In this paper, a model, which is radial basis function (RBF) neural network, is established to estimate the impact of meteorological indicators on SO2. The proposed model achieves 9.91% in mean absolute percentage error (MAPE) compared to real observation data sequence. For air quality, it could be a promising candidate for forecasting the air quality indicators data sequence.

Recently, manual observation sequence has been gradually replaced by automatic observation sequence. The difference between manual observation sequence and automatic observation sequence is somewhat inevitable. This challenges the the homogeneity and the continuity of historical weather data, and influences atmospheric researches and applications. Therefore, based on the understanding of the influence caused by the two observation sequences, how to modify the data sequence of manual observation to automatic observation sequence has become a problem. In this paper, a model, which is a neural network based on the particle swarm optimization technique (PSONN), is established to modify the wind speed data sequence from manual observation to automatic observation. The proposed model achieves 15.6% in mean absolute percentage error (MAPE) compared to manual observation data sequence. For wind speed, it could be a promising candidate for modifying manual observing data sequence to automatic observing data sequence.

Nowadays, wind power has occupied the position that cannot be ignored in the electric power development. However, the accuracy of the wind speed forecast is very important for wind power generation. For proper and efficient forecast of wind speed, a novel model named WRF-SSA-MLR, which was combined by Weather Reseacher Forecast (WRF), Singular Spectrum Analysis (SSA) and Multiple Linear Regression (MLR), was proposed to predict the wind speed. The proposed model achieves 0.1318 to 7.9170 in the root mean square error (RMSE) compared to real wind speed values. For wind speed forecast, it could be a promising candidate for improving the prediction accuracy.

This paper presents a new strategy in wind speed prediction based on AR model and wavelet transform.The model uses the adjacent data for short-term wind speed forecasting and the data of the same moment in earlier days for long-term wind speed prediction at that moment,taking the similarity of wind speed at the same moment every day into account.Using the new model to analyze the wind speed of An-xi,China in April,2010,this paper concludes that the model is effective for that the correlation coefficient between the predicted value and the original data is larger than 0.8 when the prediction is less than 48 hours;while the prediction time is long ahead (48-120h),the error is acceptable (within 40%),which demonstrates that the new method is a novel and good idea for prediction on wind speed.