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Misaghi F., Mohammadi K., Mousavizadeh M.h.,
Volume 1, Issue 1 (9-2003)

In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network.
M. Karamouz, M. Fallahi, S. Nazif, M. Rahimi Farahani,
Volume 10, Issue 4 (12-2012)

Runoff simulation is a vital issue in water resource planning and management. Various models with different levels of accuracy

and precision are developed for this purpose considering various prediction time scales. In this paper, two models of IHACRES

(Identification of unit Hydrographs And Component flows from Rainfall, Evaporation and Streamflow data) and ANN (Artificial

Neural Network) models are developed and compared for long term runoff simulation in the south eastern part of Iran. These

models have been utilized to simulate5-month runoff in the wet period of December-April. In IHACRES application, first the

rainfall is predicted using climatic signals and then transformed to runoff. For this purpose, the daily precipitation is downscaled

by two models of SDSM (Statistical Downscaling Model) and LARS-WG (Long Ashton Research Station-Weather Generator). The

best results of these models are selected as IHACRES model input for simulating of runoff. In application of the ANN model,

effective large scale signals of SLP(Sea Level Pressure), SST(Sea Surface Temperature), DSLP and runoff are considered as model

inputs for the study region. The performances of the considered models in real time planning of water resources is evaluated by

comparing simulated runoff with observed data and through SWSI(Surface Water Scarcity Index) drought index calculation.

According to the results, the IHACRES model outperformed ANN in simulating runoff in the study area, and its results are more

likely to be comparable with the observed values and therefore, could be employed with more certainty.

D. Galan, M. Marchamalo, R. Martinez-Marin, J. A. Sanchez-Sobrino,
Volume 11, Issue 2 (6-2013)

New advances in geomatics and communications technologies are enabling the development of Automated Auscultation System for structure monitoring. In particular, Differential GPS (DGPS) technique allows real-time monitoring of structures with millimetre accuracy after an appropriate mathematical treatment. The results of real-time DGPS monitoring of a pilot dam over 15 months are presented and compared with the results of pendulums and angular collimation. DGPS monitoring was established to control two points at the top of the dam with reference to an external and stable station. Communications were critical, evolving from initial GPRS connections to more reliable ASDL line in the last months. Real-time DGPS positions were filtered to reach millimetric accuracy through Kalman filter. Two configurations of the filter were tested, one more adapted to predictable and uniform velocity deformations (low frequency) and another more suitable for sudden and large movements (high frequency). Root mean square errors were calculated taking pendulums as a reference. Results show that both DGPS and angular collimation allow monitoring with millimetric accuracy. In the last period, where communications with processing server were stable, a global accuracy of 1.44 and 1.86 mm was reached for real-time DGPS monitoring. RINEX post-processing yielded millimetric results, validating real-time observations. We can affirm that the DGPS system is very useful for dam auscultation and safety as it detects adequately absolute deformations, being a complement to existing methods which should be considered in new safety plans.
Mr Rakesh Bahera, Mr Anil Kumar, Dr. Lelitha Vanajakshi,
Volume 15, Issue 8 (12-2017)

In recent times, Bus Arrival Time Prediction (BATP) systems are gaining more popularity in the field of Advanced Public transportation systems (APTS), a major functional area under Intelligent Transportation Systems (ITS). BATP systems aim to predict bus arrival times at various bus stops and provide the same to passenger’s pre-trip or while waiting at bus stops. A BATP system, which is accurate, is expected to attract more commuters to public transport, thus helping to reduce congestion. However, such accurate prediction of bus arrival still remains a challenge, especially under heterogeneous and lane-less traffic conditions such as the one existing in India. The uncertainty associated with such traffic is very high and hence the usual approach of prediction based on average speed will not be enough for accurate prediction. In order to make accurate predictions under such conditions, there is a need to identify correct inputs and suitable prediction methodology that can capture the variations in travel time. To accomplish the above goal, a robust framework relying on data analytics is proposed in this study. The spatial and temporal patterns in travel times were identified in real time by performing cluster analysis and the significant inputs thus identified were used for the prediction. The prediction algorithm used the Adaptive Kalman Filter approach, in order to take into account of the high variability in travel time. The proposed schemes were corroborated using real-world GPS data and the results obtained are very promising.

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