drought affects on all aspects of human actirities seriously as a climatic phenomenon. however, the studies related to this phenomenon based on suitable method are very few. studying the drought features and its prediction can be effective on decreasing of resulted losses or damages. therefore, drought and evaluation of its prediction possibility are studied for some stations of ardabil provinc...

the ground improvement using plastic board drain (pbd) in soft soil was undertaken by sand mat formation, pbd installation, preloading surcharge, and removal of surcharge. during this procedure, the sand mat formation induced an initial settlement. however, it was very difficult to estimate that settlement due to pbd installation, which frequently destroyed the instruments installed in the grou...

in recent decades artificial neural networks (anns) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. this paper presents the application of artificial neural networks to predict drought in yazd meteorological station. in this research, different archite...

background and objectives: survival models are statistical technique to estimate or predict the overall time up to specific events. prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. the aim of this study is using parametric models to determine the factors influencing predicted sur...

The Artificial Neural Network (ANN) is a computer technique that uses mathematical model to represent simpler form of the biologic neural structure. It formed by many processing units and its intelligent behavior comes from iterations between these units. One application ANN for time series prediction algorithms, where network learns dependent data it able predict future values. In this work, a...

Extended Abstract. Forecasting is one of the most important purposes of time series analysis. For many years, classical methods were used for this aim. But these methods do not give good performance results for real time series due to non-linearity and non-stationarity of these data sets. On one hand, most of real world time series data display a time-varying second order structure. On th...