the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

considering the importance of cd and u as pollutants of the environment, this study aims to predict the concentrations of these elements in a stream sediment from the eshtehard region in iran by means of a developed artificial neural network (ann) model. the forward selection (fs) method is used to select the input variables and develop hybrid models by ann. from 45 input candidates, 13 and 14 ...

the present investigation entails a procedure by which the surface tension and viscosity of liquids could be redicted.to this end, capillary experiments were performed for porous media by utilizing fifteen different liquids and powders. the time of capillary rise to a certain known height of each liquid in a particular powder was recorded. two artificial neural networks (anns) were designed and...

Evaporation is one of the most important components of hydrologic cycle.Accurate estimation of this parameter is used for studies such as water balance,irrigation system design, and water resource management. In order to estimate theevaporation, direct measurement methods or physical and empirical models can beused. Using direct methods require installing meteorological stations andinstruments ...

prediction of the heavy metals in the groundwater is important in developing any appropriate remediation strategy. this paper attempts to predict heavy metals (pb, zn and cu) in the groundwater from arak city, using artificial neural network (ann) algorithm by taking major elements (hco3, so4) in the groundwater from arak city. for this purpose, contamination sources in the groundwater were rec...

objective(s): this study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ann) and correlation using experimental data. methods: two-layer perceptron feedforward artificial neural network and backpropagation levenberg-marquardt (bp-lm) training algorithm are used to predict the thermal cond...

the spatial distribution of petrophysical properties within the reservoirs is one of the most importantfactors in reservoir characterization. flow units are the continuous body over a specific reservoirvolume within which the geological and petrophysical properties are the same. accordingly, anaccurate prediction of flow units is a major task to achieve a reliable petrophysical description of a...

This research deals with the development and comparison of two data-driven models, i.e., Artificial Neural Network (ANN) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) models for estimation of 28-day compressive strength of concrete for 160 different mix designs. These various mix designs are constructed based on seven different parameters, i.e., 3/4 mm sand, 3/8 mm sand, cement conten...