The monitoring of soil salinity levels is necessary for the prevention and mitigation of land degradation in arid environments. To assess the potential of remote sensing in estimating and mapping soil salinity in the El-Tina Plain, Sinai, Egypt, two predictive models were constructed based on the measured soil electrical conductivity (ECe) and laboratory soil reflectance spectra resampled to La...

Credit scoring has become a very important task as the credit industry has been experiencing severe competition during the past few years. The artificial neural network is becoming a very popular alternative in credit scoring models due to its associated memory characteristic and generalization capability. However, the relative importance of potential input variables, long training process and ...

In this study, an inventory of storm-triggered debris flows performed in the area San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck November 2009 as result simultaneous action low-pressure system 96E Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) employed model relationships between se...

In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propaga...

This study uses data mining techniques to examine the effect of various demographic, cognitive and psychographic factors on Egyptian citizens’ use of e-government services. Multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), classification and regression trees (CART), and multivariate adaptive regression splines (MARS) are compared to a standard statistical method (...