Developing reliable estimates of streamflow prediction are crucial for water resources management and flood forecasting purposes. The objectives of this study are to investigate the potential of support vector machines (SVM) model for streamflow forecasting at ungaged sites, and to compare its performance with other statistical method of multiple linear regression (MLR). Three quantitative stan...

We study the problem of short term wind speed prediction, which is a critical factor for effective wind power generation. This is a challenging task due to the complex and stochastic behavior of the wind environment. Observing various periods in the wind speed time series present different patterns, we suggest a nonlinear adaptive framework to model various hidden dynamic processes. The model i...

In this paper, we propose some new modeling techniques that provide a more synergistic approach to multistage time-domain speech compression. In particular, we propose a new error criterion for determining all-pole filters, and a unique method for jointly coding the pulse information in excitation vectors. The new error criterion for determining all-pole filters is based upon minimizing the sum...

The temporal prediction of the solar radiation is very important for the operation of any solar energy system technology and completing data set. Based on meteorological parameters, the artificial neural network (ANN) can bring a technical solution for the prediction problems. In this paper, we developed an ANN for the south Tunisian climate to predict global solar radiation. Five years of reco...

This paper presents an artificial neural network (ANN) approach to predicting and classifying distribution transformer specific iron losses, i.e., losses per weight unit. The ANN is trained to learn the relationship of several parameters affecting iron losses. For this reason, the ANN learning and testing sets are formed using actual industrial measurements, obtained from previous completed tra...

Prediction of highly non linear behavior of suspended sediment flow in rivers has prime importance in the field of water resources engineering. In this study the predictive performance of two Artificial Neural Networks (ANNs) namely, the Radial Basis Function (RBF) Network and the Multi Layer Feed Forward (MLFF) Network have been compared. Time series data of daily suspended sediment discharge ...

Research on vehicle longitudinal control with a stop and go system is presently one of the most important topics in the field of intelligent transportation systems. The purpose of stop and go systems is to assist drivers for repeatedly accelerate and stop their vehicles in traffic jams. This system can improve the driving comfort, safety and reduce the danger of collisions and fuel consumption....

Matrix factorization is the most popular approach to solving prediction problems. However, in the recent years multiple relationships amongst the entities have been exploited in order to improvise the state-of-the-art systems leading to a multi relationalmatrix factorization (MRMF)model.MRMFdealswith factorization of multiple relationships existing between the main entities of the target relati...

Decision trees based on features derived from text analysis have previously been used to predict the input parameters of models of F0 contour for text-to-speech synthesis. Yet it is not known which features contribute most to the success of the prediction. This paper quantifies the dependence of the predicted F0 contour on each of several input features derived from the text. Parameters for the...