Enhanced Monthly Precipitation Forecasting Using Artificial Neural Network and Singular Spectrum Analysis Conjunction Models

Abstract

Precipitation is a complex atmospheric process with both temporal and spatial variations which make it difficult to forecast. Several models have been developed to forecast precipitation. This study investigates the accuracy of artificial neural network (ANN) and singular spectrum analysis (SSA) conjunction model in monthly precipitation forecasting. Here, SSA is used to preprocess raw input signals to provide data of high quality to ANN. The conjunction model is obtained by combining two methods, ANN and SSA, and compared with the single/regular ANN. In SSA, the window length and the optimum number of components must be determined. For this purpose, several tentative models, one for each of the available component numbers, are developed, and the related component number of the model with best performance is considered as the optimum number of components. If the selected optimum number is less or equal to the chosen window length which in turn indicates the adequacy of the chosen window length. Monthly precipitation data from one rain gauge station, Ponel station, in northern Iran, is used in the study. The root mean square error (RMSE), correlation coefficient (R) and coefficient of efficiency (CE) statistics are used as the comparing criteria. The comparison of the results reveals that the conjunction models could increase the forecast accuracy of the ANN model in monthly precipitation forecasting. It is found that the conjunction model with $$RMSE = 52.257$$ , $$R = 0.858$$ and $$CE = 0.731$$ in the validation/testing period is superior in forecasting monthly precipitations than the most accurate ANN model with $$RMSE = 91.096$$ , $$R = 0.444$$ and $$CE = 0.183$$ , respectively.