The present study aims to utilize an Artificial Neural Network (ANN) to modeling the rainfallrunoff relationship in a catchment area located in a semiarid region of Iran. The paper illustrates the applications of the feed forward back propagation for the rainfall forecasting with various algorithms with performance of multi-layer perceptions. The monthly stream of Jarahi Watershed was analyzed ...

This work addresses the issue of better considering the heterogeneity of precipitation fields within lumped rainfall-runoff models where only areal mean precipitation is usually used as an input. A method using a Kohonen neural network is proposed for the clustering of precipitation fields. The evaluation and improvement of the performance of a lumped rainfall-runoff model for one-day ahead pre...

This paper presents a data-based mechanistic modelling (DBM) approach to rainfallrunoff modelling based on the direct identification and estimation of continuous-time models from discrete-time series. It is argued that many mechanistic model parameters are more naturally defined in the context of continuous-time, differential equation models. As a results, there are advantages if such models ar...

The successful application of a conceptual rainfall-runoff (CRR) model depends on how well it is calibrated. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not. i.•possible, to obtain unique optimal values for their parameters using automatic calibration methods. Unless the best set of parameters.associated witha given calibration dat...

Problems associated with the application of monthly rainfallrunoff models to arid and semiarid areas for water resource estimation purposes are discussed with respect to the representation of hydrological processes and the model rainfall input. The problems are illustrated using a specific monthly model applied to catchments within the semiarid to arid parts of southern Africa. Some model impro...

runoff estimation is one of the main challenges encountered in water and watershed management. spatial and temporal changes of factors which influence runoff due to het-erogeneity of the basins explain the complicacy of relations. artificial neural network (ann) is one of the intelligence techniques which is flexible and doesn’t call for any much physically complex processes. these networks can...

The Shuffled Complex Evolution (SCE-UA) method has been widely applied for calibration of rainfall-runoff models and has been shown to be robust and efficient search algorithm. In spite of its superiority, since many commonly used rainfallrunoff models have large simulation times, the use of model calibrating algorithms may become impractical due to the high computational time involved. This wo...

Precise knowledge ofthe amount of global solar radiation plays an important role in designing solar energy systems. In this study, by using 22-year meteorologicaldata, 19 empirical models were tested for prediction of the monthly mean daily global solar radiation in Tabriz. In addition, various Artificial Neural Network (ANN) models were designed for comparison with empirical models. For this p...

This paper presents a method for appropriate coupling of deterministic and statistical models. In the decision-support system for the Elbe river, a conceptual rainfall-runoff model is used to obtain the discharge statistics and corresponding average number of flood days, which is a key input variable for a rule-based model for floodplain vegetation. The required quality of the discharge time se...