with the increase of the volume of information and the progress in technology, the deficiency of traditional algorithms for fast information retrieval becomes more clear. when large volumes of data are to be handled, the use of neural network as an artificial intelligent technique is a suitable method to increase the information retrieval speed. neural networks present a suitable representation...

Abstract Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute slope stability. Artificial neural networks (ANN) have been shown improve prediction accuracy but largely uninterpretable. Here we introduce an additive ANN optimization framework assess landslide susceptibility, as well dataset division outcome interpretation techniques...

in this paper an intelligent method through general regression neural networks (grnn) is presented to estimate the depth of salt domes from gravity data. neural networks are as a good tool for automatic interpretation of geophysical data especially for depth estimation of gravity anomalies. the gravity signal is a nonlinear function of depth and density and the geometrical parameters of the bur...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...

in this paper, the artificial neural network (ann) approach is applied for forecasting groundwater level fluctuation in aghili plain,southwest iran. an optimal design is completed for the two hidden layers with four different algorithms: gradient descent withmomentum (gdm), levenberg marquardt (lm), resilient back propagation (rp), and scaled conjugate gradient (scg). rain,evaporation, relative...