there is different methods for simulating river flow. some of thesemethods such as the process based hydrological models need multiple input data and high expertise about the hydrologic process. but some of the methods such as the regression based and artificial inteligens modelsare applicable even in data scarce conditions. this capability can improve efficiency of the hydrologic modeling in u...

A nonlinear prediction method, developed based on the ideas gained from deterministic chaos theory, is employed: (a) to predict monthly runoff; and (b) to detect the possible presence of chaos in runoff dynamics. The method first reconstructs the single-dimensional (or variable) runoff series in a multi-dimensional phase space to represent its dynamics, and then uses a local polynomial approach...

SWAT (Soil and Water Assessment Tool) is a watershed model that has been incorporated into USEPA’s modeling framework called BASINS used for total maximum daily load (TMDL) analysis. It is thus important that SWAT realistically simulate tile flow and pothole landscapes that are common in much of the Corn Belt and Great Lakes states. In this study, SWAT was modified to simulate water table dynam...

different approaches have been presented for estimating river environmental flow which hydrological approach has a lot addressed because of its need to access to limited data and moderately rapid assessment. this approach incorporates various methods therefore comparison between results of different methods is essential. the objective of this study is to investigate and compare the consequences...

a prediction of maximum monthly rainfall is indispensable for management of agriculture and for management of water resources. previous studies have demonstrated that see surface temperatures affect rainfall in their surrounding regions. in this study the relationship between sea surface temperatures of persian gulf and that of red sea, and maximum monthly rainfalls recorded at illam meteorolog...

This paper investigates the application of partially recurrent artificial neural networks (ANN) in the flow estimation for São Francisco River that feeds the hydroelectric power plant of Sobradinho. An Elman neural network was used suitably arranged to receive samples of the flow time series data available for São Francisco River shifted by one month. For that, the neural network input had a de...

comparison of linear and nonlinear (bilinear) time series models in reference crop evapotranspiration prediction in urmia synoptic stationreference crop evapotranspiration (eto) prediction is one of the important elements in optimizing agricultural water consumption. in this regard, one of the prediction approaches is to use the stochastic time series methods. in this research, ar (p) and arma(...

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude altitude of meteorological station, month, average temperatures, among others) areas Galicia (Spain). The were trained, validated queried using data three sta...