## 1 Introduction  
In recent years, a variety of extreme weather phenomena have occurred on a global scale, causing overhead power transmission line icing disasters to happen frequently, accompanied by power outages and huge economic losses due to the destruction of a large number of fixed assets. Since the first power transmission line icing accident recorded in 1932, several serious icing disasters have occurred one after another throughout the world [1], such as Canada's freezing rain disaster in 1998, which led to direct economic losses of 1 billion dollars and indirect losses of 30 billion dollars for the power system. The power system failure caused by freezing rain was also a tremendous shock to the production and life of the region. In China, the earliest recorded icing disaster occurred in 1954. After that year, several large areas of freezing rain and snow disasters have successively occurred in China, especially Southern China's freezing weather in January 2008 [2]. That disaster caused tremendous damage to China's power system, directly resulting in 8709 tower collapses, more than 27,000 line breakages, and 1497 substation outages of 110 kV and above lines. The direct property loss for the State Grid Corporation amounted to 10.45 billion RMB, and the investment in post-disaster electricity reconstruction and transformation was 39 billion RMB. According to the lessons learned from previous grid freezing accidents, and given the current development situation of China's grid and the conditions of global climate change, the grid in China will again be tested by the future large-scale icing disasters. It is always preferable to take preventative action instead of reacting to events after an accident hasalready happened. Therefore, icing forecasting research with regard to overhead power transmission lines have important practical application value. At present, there are many studies on icing forecasting for power transmission lines. According to the hydrodynamic movement law and heat transfer mechanisms, domestic and foreign scholars have established a variety of transmission line icing forecasting models that consider meteorological factors, environmental factors, and various line parameters. Generally these models can be divided into three categories: mathematical and physical models, statistical models and intelligent forecasting models. Mathematical and physical models involve simulation modeling and forecasting of the icing growth process by observing the physical process and mathematical equations of icing formation, notably including the Goodwin model [3], the Makkonen model [4] and so on. These models are established based on the experimental data, but there are differences between the experimental data and the practical data, so the forecasting results of these models are not ideal. Statistical models process historical data based on statistical theory and traditional statistical methods and do not consider the physical process of icing formation, e.g., the multiple linear regression icing model [5]. However, transmission line icing is affected by a variety of factors, and a multiple linear regression model cannot take all factors into account, meaning the icing forecasting accuracy is greatly reduced. Intelligent forecasting models combine modern computer technology with mathematical sciences, and are able to handle high-dimensional nonlinear problems through their powerful learning and processing capabilities, which can improve the prediction accuracy. The common intelligent forecasting models are support vector machines (SVM) and Back Propagation(BP) neural network. BP neural network has a strong nonlinear fitting ability to create the non-linear relationship between the output and a variety of impact factors. With its strong learning ability and forecasting capability, the non-linear output can arbitrarily approximate the actual value.