a back propagation artificial neural network (bpann) is a well-known learning algorithmpredicated on a gradient descent method that minimizes the square error involving the networkoutput and the goal of output values. in this study, 261 gps/leveling and 8869 gravity intensityvalues of iran were selected, then the geoid with three methods “ellipsoidal stokes integral”,“bpann”, and “collocation” ...

We consider a univariate semimartingale model for (the logarithm of) an asset price, containing jumps having possibly infinite activity (IA). The nonparametric threshold estimator ˆ IV n of the integrated variance IV := ∫ T 0 σ sds proposed in [6] is constructed using observations on a discrete time grid, and precisely it sums up the squared increments of the process when they are under a thres...

in this study, the performance of aquacrop model to predict yield production of winter wheat (roshan and gods varieties), water and solute balance of soil profile was evaluated under water and salinity stresses. field experiments were conducted with three levels of saline irrigation water including: s1, s2 and s3corresponding to 1.4, 4.5 and 9.6 ds/m, respectively, and four levels of irrigation...

The optimum design of solar energy systems strongly depends on the accuracy of  solar radiation data. However, the availability of accurate solar radiation data is undermined by the high cost of measuring equipment or non-functional ones. This study developed a feed-forward backpropagation artificial neural network model for prediction of global solar radiation in Makurdi, Nigeria (7.7322  N lo...

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative fea...