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...

Two important applications of rainfall-runoff models are forecasting and simulation. At present, rainfall-runoff models based on artificial intelligence methods are built basically for short-term forecasting purposes and these models are not very effective for simulation purposes. This study explores the applicability and effectiveness of adaptive neuro-fuzzy-system-based rainfall-runoff models...

The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-ru...

Each year, extreme floods, which appear to be occurring more frequently in recent years (owing to climate change), lead to enormous economic damage and human suffering around the world. It is therefore imperative to be able to accurately predict both the occurrence time and magnitude of peak discharge in advance of an impending flood event. The use of meta-heuristic techniques in rainfall-runof...

Design flood estimation is often required in hydrologic practice such as design of hydraulic structures and flood plain management. In flood estimation, use of rainfall runoff models is frequently adopted which convert selected rainfall events into the corresponding streamflow events. In Australia, runoff routing model is frequently adopted as the preferred method of rainfall runoff modeling. T...

rainfall-runoff is one of complex hydrological processes that is affected by a variety of physical and hydrological factors. in this study statistical method armax model, neural network, neuro-fuzzy (anfis subtractive clustering and grid partition) and two hybrid models of this methods were used to simulate rainfall-runoff and prediction of streamflow. in each method optimum structure was deter...

investigation of inter-storm variability of runoff and soil loss characteristics helps experts and decision makes better understanding of hydrological response. however, this important has been less considered in the world, especially developing countries. the present study was therefore planned to evaluate the effect of different rainfall intensities and slopes on inter-storm variability of co...