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Showing 3 results for Flow Forecasting

Mohammad T. Dastorani, Nigel G. Wright,
Volume 2, Issue 3 (9-2004)
Abstract

In this study, an artificial neural networks (ANN) was used to optimise the results obtained from a hydrodynamic model of river flow prediction. The study area is Reynolds Creek Experimental Watershed in southwest Idaho, USA. First a hydrodynamic model was constructed to predict flow at the outlet using time series data from upstream gauging sites as boundary conditions. The model, then was replaced with an ANN model using the same inputs. Finally a hybrid model was employed in which the error of the hydrodynamic model is predicted using an ANN model to optimise the outputs. Simulations were carried out for two different conditions (with and without data from a recently suspended gauging site) to evaluate the effect of this suspension in hydrodynamic, ANN and the hybrid model. Using ANN in this way, the error produced by the hydrodynamic model was predicted and thereby, the results of the model were improved.
M. T. Banki, B. Esmaeili,
Volume 7, Issue 4 (12-2009)
Abstract

Cash flow forecasting is an indispensable tool for construction companies, and is essential for the survival

of any contractor at all stages of the work. The time available for a detailed pre-tender cash flow forecast is often

limited. Therefore, contractors require simpler and quicker techniques which would enable them to forecast cash flow

with reasonable accuracy. Forecasting S-curves in construction in developing countries like Iran in compare with

developed countries has many difficulties. It is because of uncertainty and unknown situation in nature of construction

industry of these countries. Based on knowledge of authors there is a little attempt for cash flow forecasting in

construction industry of Iran. As a result authors produced An S-curve equation for construction project from historical

data which has reasonable accuracy. A sample of 20 completed projects was collected and classified in to the three

different groups. In order to model S-curves for each group, a simple and reliable method of S curve fitting has been

used. S-curves were fitted into each group by using different techniques. Errors incurred when fitting these curves were

measured and compared with those associates in fitting individual projects. At the end, accuracy of each model has

been calculated and an equation has been proposed to forecast S-curves.


L. Zhang,
Volume 12, Issue 3 (9-2014)
Abstract

Short-term traffic flow forecasting plays a significant role in the Intelligent Transportation Systems (ITS), especially for the traffic signal control and the transportation planning research. Two mainly problems restrict the forecasting of urban freeway traffic parameters. One is the freeway traffic changes non-regularly under the heterogeneous traffic conditions, and the other is the successful predictability decreases sharply in multiple-steps-ahead prediction. In this paper, we present a novel pattern-based short-term traffic forecasting approach based on the integration of multi-phase traffic flow theory and time series analysis methods. For the purpose of prediction, the historical traffic data are classified by the dynamic flow-density relation into three traffic patterns (free flow, synchronized and congested pattern), and then different predict models are built respectively according to the classified traffic patterns. With the current traffic data, the future traffic state can be online predicted by means of pattern matching to identify traffic patterns. Finally, a comparative study in a section of the Third-Loop Freeway, LIULIQIAO, Beijing city, shows that the proposed approach represents more accurately the anticipated traffic flow when compared to the classical time series models that without integration with the traffic flow theory.

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