Solar irradiation is the most critical parameter to consider when designing solar energy systems. The high cost and difficulty of measuring makes it impractical in every location. This study’s primary objective was develop an artificial neural network (ANN) model for global horizontal (GHI) prediction using satellite data inputs. Three types ANN, namely, feed forward (FFNN), cascaded (CFNN), El...

The purpose of this paper is to develop an appropriate artificial neural network (ANN) model of induction motor bearing (IMB) failure prediction. Acoustic emission (AE) represented the technique of collecting the data that was collected from the IMB and this data were measured in term of decibel (dB) and Distress level. The data was then used to develop the model using ANN for IMB failure predi...

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neura...

Minimizing dilution is essential in open stope mine design as excessive unplanned can compromise the operation's profitability. One of main challenges associated with empirical graph method used to stopes how determine boundary zones objectively. Hence, this paper explores implementation machine learning classifiers bridge gap conventional method. Stope performance data consisting (unplanned di...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

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 ...

Proper analysis of building energy performance requires selecting appropriate models for handling complicated calculations. Machine learning has recently emerged as a promising effective solution solving this problem. The present study proposes novel integrative machine model predicting two parameters residential buildings, namely annual thermal demand (DThE) and weighted average discomfort deg...

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially us...

this paper proposes a method for the prediction of pore size values in hydrocarbon reservoirs using 3d seismic data. to this end, an actual carbonate oil field in the south-western part ofiranwas selected. taking real geological conditions into account, different models of reservoir were constructed for a range of viable pore size values.  seismic surveying was performed next on these models. f...