This research intends to develop a method based on the Artificial Neural Network (ANN) to predict permanent earthquake-induced deformation of the earth dams and embankments. For this purpose, data sets of observations from 152 published case histories on the performance of the earth dams and embankments, during the past earthquakes, was used. In order to predict earthquake-induced deformation o...

In this paper, we propose an artificial neural network (ANN) as a design technique for multilayer circular microstrip antennas based on Levenberg Marquardt training algorithm for modelling, simulation and optimization. Levenberg – Marquart (LM) algorithm has been used to train the MultiLayer Perceptron Neural Networks (MLPNNs). In the design procedure, the feed forward network is defined as a s...

results multilayer perceptron (mlp) artificial neural network (ann) model with a 1-6-1 structure was chosen which explained 97% and 95% of kd and koc variances, respectively. the only input data was soil organic carbon content. conclusions based on this study, the ann method is a promising alternative for conventional methods in modeling and estimating sorption coefficients in relation to soil ...

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...

An Artificial Neural Network (ANN) is designed to investigate its application for data assimilation. This procedure provides an appropriated initial condition to the atmosphere to weather forecasting. Data assimilation is a method to insert observational information into a physicalmathematical model. The goal here is the process for assimilating meteorological observations. The numerical experi...

When designing artificial neural network (ANN) it is important to optimise the network architecture and the learning coefficients of the training algorithm, as well as the time the network training phase takes, since this is the more timeconsuming phase. In this paper an approach to cooperative co-evolutionary optimisation of multilayer perceptrons (MLP) is presented. The cooperative co-evoluti...

Abstract A class of recurrent neural networks is developed to solve nonlinear equations, which are approximated by a multilayer perceptron (MLP). The recurrent network includes a linear Hopfield network (LHN) and the MLP as building blocks. This network inverts the original MLP using constrained linear optimization and Newton’s method for nonlinear systems. The solution of a nonlinear equation ...

In this study, autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models were applied to predict the monthly flow time series of two stations, named Begova Chemcermo, for Khabour River. The analysis was performed using several criteria tests. autocorrelation function (ACF) partial (PACF) check accuracy ARIMA model. Akaike (AIC) Bayesian (BIC) equations determin...

Artificial Neural Networks (ANN) have been essentially used as regression models to predict the concentration of one or more pollutants usually requiring information collected from air quality stations. In this work we consider a Multilayer Perceptron (MLP) with one hidden layer as a classifier of the impact of air quality on human health, using only traffic and meteorological data as inputs. O...

An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydrologically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks ...