نتایج جستجو برای: runoff model using an ann approach
تعداد نتایج: 8719474 فیلتر نتایج به سال:
This work develops a rule curve-based conjunctive use management model for optimizing the operating rules for a lake–groundwater system with off-stream storage lakes. The proposed procedure is a simulation-optimization approach that embeds an Artificial Neural Network (ANN) instead of a groundwater numerical model into a genetic algorithm (GA). The direct physical exchange between lake water wi...
Dissolved inorganic P transport in runoff from agricultural soils is an environmental concern. Models are used to predict P transport but rarely simulate P in runoff from surface-applied manures. Using field-plot data, we tested a previously proposed model to predict manure P in runoff. We updated the model to include more data relating water to manure ratio to manure P released during water ex...
Artificial neural networks (ANN) have shown to be a powerful tool for system modeling in a wide range of applications. The focus of this study is on neural network applications to data analysis in egg production. An ANN model with two hidden layers, trained with a back propagation algorithm, successfully learned the relationship between the input (age of hen) and output (egg production) variabl...
this study aims to evaluate effects of two downscaling methods; change-factor and statistical downscaling on therunoff of the azam-harat river located at yazd province (with an arid climate) of iran, under the a2 emission scenariofor the period of 2010-2039. for this purpose, cgcm3-ar4 model; a rainfall-runoff conceptual model, ihacres; twodownscaling models, change factor and lars-wg were appl...
The use of infiltration trenches has proven to be useful to reduce runoff in urban surfaces. The design of these structures is based on the continuity equation taking into account inflow, outflow and detention water volumes. Basic procedures evaluate entering flow rates and relative volumes directly as a function of rain event characteristics, without taking into account rain-runoff processes o...
Due to the lack of necessary equipment for measuring and recording changes in watershed runoff and flood situation after the implementation of corrective actions, using hydrologic models is considered as an efficient tool to assess the undertaken actions and simulate the behavior of the watershed before and after the implementation of these measures. The present study aimed to simulate the effe...
Background: Multiple Sclerosis (MS) is one of the most debilitating disease among young adults. Understanding the disability score (Expanded Disability Status Scale (EDSS)) of these patients is helpful in choosing their treatment process. Calculating EDSS takes a lot of time for Neurologists, so having a way to estimate EDSS can be helpful. This study aimed to estimate the EDSS score of MS pati...
Investigation in to the sediment delivery of watersheds and its variation is an important element of ecosystem management. Since sediment load depends on runoff quantity, and runoff is considered as a unique indicator of sediment load, in this research the two modified versions of the MUSLE model were evaluated for 9 torrential events in two subwatersheds of the Kamal Saleh watershed in the Mar...
Hydrological models are used to represent the rainfallrunoff and pollutant transport mechanisms within watersheds. Accurate representation of these dynamic and complex natural processes within a watershed is an important step in managing and protecting a watershed Artificial neural network (ANN) models are often used in hydrologic modeling. Typical ANN models are trained to use lumped data. How...
background: municipal solid waste (msw) is the natural result of human activities. msw generation modeling is of prime importance in designing and programming municipal solid waste management system. this study tests the short-term prediction of waste generation by artificial neural network (ann) and principal component-regression analysis. methods: two forecasting techniques are presented in...
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