The artificial neural networks (ANNs) have been applied to various hydrologic problems recently. This research demonstrates static neural approach by applying Modular feedforward neural network to rainfall-runoff modeling for the upper area of Wardha River in India. The model is developed by processing online data over time using static modular neural network modeling. Methodologies and techniq...

Simulation modeling is widely used to assess water contamination risk associated with pesticide use and for evaluating effectiveness of agricultural conservation practices. Currently the Agricultural Policy/Environmental eXtender (APEX) model is being used for this purpose in the USDA Conservation Effects Assessment Project (CEAP) Cropland National Assessment. In support of the CEAP modeling ef...

rainfall-runoff modeling is most important component in the water resource management of river basins. the successful application of a conceptual rainfall-runoff model depends on how well it is calibrated. the degree of difficulty in solving the global optimization method is generally dependent on the dimensionality of the model and certain of the characteristics of object function. the purpose...

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...

Enrichment of surface waters with excess nutrients is associated with increased algal blooms, euthrophication and hypoxic zones, as reported in the northern Gulf of Mexico. A source of nutrients to surface waters results from fertilizer runoff. Management strategies used to maintain turf on golf courses and recreational fields often include aerification and application of fertilizer. Although r...

The Soil and Water Assessment Tool (SWAT) was used to assess the impact of climate change on sediment, nitrate, phosphorus and pesticide (diazinon and chlorpyrifos) runoff in the San Joaquin watershed in California. This study used modeling techniques that include variations of CO(2), temperature, and precipitation to quantify these responses. Precipitation had a greater impact on agricultural ...

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...

Model selection is an extremely important aspect of many hydrologic modeling studies because of the complexity, variability, and uncertainty that surrounds the current understanding of watershed-scale systems. However, development and implementation of a complete precipitation-runoff modeling framework, from model selection to calibration and uncertainty analysis, are rarely confronted. This pa...