Analyses and investigations on river flow behavior are major issues in design, operation and studies related to water engineering. Thus, recently the application of chaos theory and new techniques, such as chaos theory, has been considered in hydrology and water resources due to relevant innovations and ability. This paper compares the performance of chaos theory with Anfis model and discusses ...

The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncatio...

In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend...

The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic ...

Two phase gas-liquid flow pattern in a horizontal pipeline is predicted very accurately using a newly-developed analytical relation. The pattern identification is based on one of the most widely used graphs, the Baker diagram, modified in a way that compensates for the unrealistic oversimplifications of recent works. The Kern's method of pressure drop calculation is used to obtain the frict...

this paper presents a hybrid approach to developing a short-term traffic flow prediction model. in thisapproach a primary model is synthesized based on neural networks and then the model structure is optimized throughgenetic algorithm. the proposed approach is applied to a rural highway, ghazvin-rasht road in iran. the obtainedresults are acceptable and indicate that the proposed approach can i...

background: pulmonary function tests (pfts) are used in assessing physiological to clinical status of the respiratory system, which is expressed as a percentage of predicted values. predicted pfts values are varies in different ethnics. predicted pfts values were studied in a sample of iranian children. materials and methods: prediction equations for pfts were derived from urban children in the...

A plasticity-fracture constitutive model is presented for prediction of the behavior of confined plain concrete. A three-parameter yield surface is used to define the elastic limit. Volumetric plastic strain is defined as hardening parameter, which together with a nonlinear plastic potential forms a non-associated flow rule. The use of non-associated flow rule improves the prediction of the dil...

Many program veriication, testing and performance prediction techniques rely on analysis of statically-identiied control-ow paths. However, some such paths may be semantically`dead' because they can never be followed at run time, and should therefore be excluded from analysis. It is shown how the formal semantics of those statements comprising a path provides a sound theoretical foundation for ...