This paper presents a new short-term traffic flow prediction system based on an advanced Time Delay Neural Network (TDNN) model, the structure of which is synthesized using a Genetic Algorithm (GA). The model predicts flow and occupancy values at a given freeway section based on contributions from their recent temporal profile (over a few minutes) as well the spatial profile (including inputs f...

Sediment Bypass Tunnels (SBTs) are deviant channels that convey the current containing sediments from the upstream of the reservoir to the downstream of the dam. In this research, by applying submerged plates on the entrance of a 90-degree diversion channel for sediment transport, the effect of hydraulic parameters of flow and changes in the angle of plates on sediment transport and deviated fl...

physiographic characteristics and climatic conditions are factors which contributing to river flow regime and understanding of relations between these factors and river flow in a basin result in its application for the ungauged sub-basins river flow prediction. in this research the relation between physiographic and climatic parameters of golestan province and rivers flow were examined by appli...

gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. gas hydrate formation rate is one of the most important topics related to the kinetics of the process of gas hydrate crystallization. the main purpose of this study is investigating phenomenon of gas hyd...

Traffic flow forecasting, the core element of intelligent transportation system, plays an important role in traffic information services and traffic guidance. Since neural network prediction needs plenty of training samples, it cannot guarantee the real-timeness of traffic flow forecasting. In this paper, a GMDH network was constructed by self-organization, and the network was applied to traffi...

The water erosion prediction project (WEPP) model is a physically-based hydrology and erosion model. In recent years, the hydrology prediction within the model has been improved for forest watershed modeling by incorporating shallow lateral flow into watershed runoff prediction. This has greatly improved WEPP’s hydrologic performance on small watersheds with seasonal flows, but the current vers...

This paper explores the potential for seasonal prediction of hydrological variables that are potentially useful for reservoir operation of the Three Gorges Dam, China. The seasonal flow of the primary inflow season and the peak annual flow are investigated at Yichang hydrological station, a proxy for inflows to the Three Gorges Dam. Building on literature and diagnostic results, a prediction mo...

Short-term traffic flow is difficult to predict because of high uncertainty. This paper proposes a short-term traffic forecast algorithm based on cloud similarity. By taking advantage of quantitative and qualitative cloud model mutual conversion function and traffic flow predictability, the historical traffic data can be processed with cloud transformation. Set the current traffic cloud as a st...

[1] In the recent past the nonlinear prediction (NLP) method, initially developed in the context of nonlinear time series analysis, has been successfully applied to river flow deterministic forecasting. In this work a probabilistic approach to the NLP method is proposed, which allows one to estimate the probability distribution of the predicted discharge values and to quantify the total uncerta...

Ensemble prediction systems are used operationally to make probabilistic streamflow forecasts for seasonal time scales. However, hydrological models used for ensemble streamflow prediction often have simulation biases that degrade forecast quality and limit the operational usefulness of the forecasts. This study evaluates three biascorrection methods for ensemble streamflow volume forecasts. Al...