In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...

solar radiation data play an important role in solar energy relevant researches. these data are not available for some locations due to the absence of the meteorological stations. therefore, solar radiation data have to be predicted by using solar radiation estimation models. this study presents an integrated artificial neural network (ann) approach for estimating solar radiation potential over...

modeling of stream flow–suspended sediment relationship is one of the most studied topics in hydrology due to itsessential application to water resources management. recently, artificial intelligence has gained much popularity owing toits application in calibrating the nonlinear relationships inherent in the stream flow–suspended sediment relationship. thisstudy made us of adaptive neuro-fuzzy ...

soil hydraulic properties such as saturated and unsaturated hydraulic conductivity play an important role in environmental research. since direct measurement of these soil hydraulic properties is time-consuming and costly, indirect methods such as pedotransfer functions and artificial neural networks (ann) were developed based on readily available parameters. in this study, the use of ann to pr...

Grey-box’ models take advantage of physical insight about the dynamical system in question. This reduces uncertainty and facilitates the remaining system identification process. Here a grey-box system identification (GBSI) method for nonlinear dynamical systems is presented, which first establishes a linear model (the ‘white box’) using a priori knowledge and conventional tools, and then adds a...

In recent years, deep learning based on artificial neural network (ANN) has achieved great success in pattern recognition. However, there is no clear understanding of such neural computational models [1], for instance, for a trained ANN-classifier, we have no idea of why some classes are easy to be predicted correctly, but some are difficult to be predicted correctly. In this paper, we try to u...

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...

A variety of analytical and experimental methods have been suggested so far for industrial system modelling. However, the need for optimized models for different objectives and applications is still a strong motivation for researchers to continue to work in this field. Artificial Neural Network (ANN) as a black-box approach has been playing a significant role in system identification and modell...