Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)
Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations. 1. Introduction Estimation and forecasting of precipitation and its runoff have played effective and critical role in the watershed management and proper utilization of watershed, dams, and reservoirs and finally minimizing the damage caused by floods and drought. Therefore, this subject is the hydrologist’s interest. Predicting any event forms the basis of crisis management, and when this goal can be achieved, the predicting model could be accessed. Several methods are used for predicting hydrological events such as precipitation. Using each of these methods is always with some error in results. Accurate prediction of hydrological signals such as precipitation can provide useful information to predict amount of precipitation for water resources and soil management in a basin. In addition, correct prediction of hydrological signals plays an important role in reducing the effects of drought on water resources systems. Hydrological systems are affected by many factors such as climate, land cover, soil infiltration rates, evapotranspiration which is dependent on stochastic components, multitemporal scales, and above-mentioned nonlinear characteristics. Despite nonlinear relationships, uncertainty, and high lack of precision and variables temporal and spatial characteristics in water circulation system, none of the statistical and conceptual models which are proposed for accurate precipitation and runoff modeling were
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