prediction of precipitation is very important. regarding to the non- linear relationships and uncertainty of models, there is no superior and persuasive model among various proposed models to simulate precise precipitation and its amount. wavelet is one of the novel and very effective methods in time series and signals analyzing, that has been considered in the field of hydrology in recent year...

Several ANN models were developed to prediction of monthly precipitation data in Mashhad synoptic station. From the total 636 monthly precipitation data (from 1958 to 2008), 580 data has been used for training networks and the rest selected randomly has been used for validation of the models. To extract the precipitation dynamic of this station by ANN, a new approach of three-layer feed-forward...

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model....

Low-frequency (interannual or longer period) climatic variability is of interest, because of its significance for the understanding and prediction of protracted climatic anomalies. Since precipitation is one of the key variables driving various hydrologic processes, it is useful to examine precipitation records to better understand long-term climate dynamics. Here, we use the multi-taper method...

This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of intere...

abstract obligatory modelling of precipitations in various periods, have a lot of problems and weakness because of their casual nature in time and space. moreover, their uncertainty in predictions, reduce credibility of estimations which have done via these models. wavelet is one of the novel and very effective methods in analyzing of time series and signals considered in the hydrology in recen...

prediction the river flow discharge values are important in the surface water resources management. find an appropriate model to accurately predictionof this parameter is one of the most important ways to simulation and prediction. in this study three anfis, svm and gp models were evaluated and compared to modeling the monthly flow discharge of nazloochi river in tapik hydrometric station that ...