successful modeling of hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. on the other hand in training phase of any artificial intelligence (ai) based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole pop...

This paper proposes a hybrid intelligent model for runoff prediction. The proposed model is a combination of data preprocessing methods, genetic algorithms and levenberg–marquardt (LM) algorithm for learning feed forward neural networks. Actually it evolves neural network initial weights for tuning with LM algorithm by using genetic algorithm. We also use data pre-processing methods such as dat...

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theor...

Hydrological models are the simplified, conceptual and mathematical description of rainfall, runoff, oceans, rivers, groundwater, snow etc. Rainfall-runoff modeling in the broader sense a hydrological, mathematical model gives a detailed account of Rainfall-Runoff relationships of a catchment area or watershed. Conversion of rainfall into runoff takes place in the modelling. There are many meth...

introduction: snowmelt runoff plays an important role in providing water and agricultural resources, especially in mountainous areas. there are different methods to simulate the process of snowmelt. inter alia, degree-day model, based on temperature-index is more cited. snowmelt runoff model is a conceptual hydrological model to simulate and predict the daily flow of rivers in the mountainous b...

Artificial neural networks (ANNs) were successfully applied to data observations from a small watershed consisting of commonly measured indicator bacteria, weather conditions, and turbidity to distinguish between human sewage and animal-impacted runoff, fresh runoff from aged, and agricultural land-use-associated fresh runoff from that of suburban land-use-associated-fresh runoff. The ANNs were...

The application of artificial neural network (ANN) methodology for modelling daily flows during monsoon flood events for a large size catchment of the Narmada River in Madhya Pradesh (India) is presented. The spatial variation of rainfall is accounted for by subdividing the catchment and treating the average rainfall of each subcatchment as a parallel and separate lumped input to the model. A l...