The need for accurate modeling of rainfall-runoff-sediment processes has grown rapidly in the past decades. This study investigates the efficiency of black-box models including Artificial Neural Network (ANN) and Autoregressive Integrated Moving Average with eXogenous input (ARIMAX) models for forecasting the rainfall-runoff-sediment process. According to the complex behavior of the rainfall-ru...

This study focuses on development, characterization and validation of an artificial neural network (ANN) model for prediction of advanced oxidation of organics in water matrix. The different ANNs, based on multilayer perceptron (MLP) and radial basis function (RBF) methodologies, have been applied for modeling of the behavior of complex system; zero-valent iron activated persulfate oxidation (F...

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...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

Objective(s): This paper investigates the validity of Artificial Neural Networks (ANN) model in the prediction of electrospun kefiran nanofibers diameter using 4 effective parameters involved in electrospinning process. Polymer concentration, applied voltage, flow rate and nozzle to collector distance were used as variable parameters to design various sets of electrospinning ex...

In this study the feasibility of using the artificial neural networks modeling in predicting the effect of MWCNT on amount of cement hydration products and improving the quality of cement hydration products microstructures of cement paste was investigated. To determine the amount of cement hydration products thermogravimetric analysis was used. Two critical parameters of TGA test are PHP(loss) ...

the veterinary drugs residues are also important pollutants found in milk, since veterinary drugs are commonly used in cattle management. considering the role of milk in human nutrition and its wide consumption throughout the world, it is very important to ensure the milk quality. a quantitative structure–retention relationship (qsrr) was developed using the partial least square (pls), kernel p...

Objective: L-asparaginase is an enzyme of industrial as well as therapeutic importance. The capabilities of bioprocess modeling of L-Asparaginase activity produced from Aspergillus niger by solid state fermentation (SSF) were explored here. Methods: Regression modeling (RM) and Artificial Neural Network (ANN) techniques were applied on input process parameter, which includes solid substrate, te...

The purpose of this study is to present a complementary modeling approach using data envelopment analysis (DEA) and artificial neural network (ANN) as an adaptive decision support tool in promoting best performance benchmarking and performance modeling. DEA and ANN are combined to take advantages of optimization and prediction capabilities inherent in each method. DEA is used as a preprocessor ...

There are many artificial intelligence approaches used in the development of Automatic Speech Recognition (ASR), hybrid approach is one of them. The common hybrid method in speech recognition is the combination of Artificial Neural Network (ANN) and Hidden Markov Model (HMM). The hybrid ANN/HMM is able to classify the phoneme model and to combine the strength of HMM in sequential modeling struc...