UK precipitation data display and forecasting - Han, Haoliang (2016)
As the current agriculture technology is highly developed, weather conditions are more and more likely to be the crucial factor and can barely be large scale changed because of the huge cost of artificial weather modification. Because of this, rainfall forecasting can help to give an intuitive trend of the precipitation in the next period of time, thus reducing the loss of drought by taking measures in advance. This report’s data is based on the Standardized Precipitation Index(SPI) which is a kind of probabilistic data, it is mainly to define the drought condition of a specific region and can be calculated from a period of accumulation time with an explicit interval ranging from -3 to 3 where to denote extremely dry(negative) to extremely wet(positive). The program of this dissertation applies Backpropagation neural network to predict the future SPI values of different times period. The forecast results will use the main methods of error analysis to verify the reliability of prediction. Predicted value will be used to color the British Map according to the pre-determined classifications which are actually the different intervals of SPI. The forecasting results show an encouraging performance that artificial neural network(ANN) can produce a reliable forecasting output with reasonable lead time.