Shiri J.,University of Tabriz |
Makarynskyy O.,URS Australia |
Kisi O.,Erciyes University |
Dierickx W.,Geraardsbergsesteenweg 18 |
Fard A.F.,University of Tabriz
Journal of Waterway, Port, Coastal and Ocean Engineering | Year: 2011
Sea level estimates are important in many coastal applications and port activities. This paper investigates the ability of a neuro-fuzzy (NF) model to predict sea level variations at a tide gauge site in the Hillarys Boat Harbour, Western Australia. In the first part of the study, previously recorded sea levels were used as input to estimate current sea levels. The results showed an acceptable level of NF model accuracy. In the second part of the study, NF models were implemented to forecast sea levels averaged over 12- and 24-h time periods, three time steps ahead. The NF forecasts were compared with those of artificial neural networks (ANNs) for the same data set. The results show that the NF approach performed better than the ANN in half-daily 12-, 24-, and 36-h sea level predictions. The traditional linear regression and autoregressive models were also tested for comparison, and they demonstrated their inferiority to the results of other techniques. © 2011 American Society of Civil Engineers.
Khatibi R.,Consultant Mathematical Modeller |
Ghorbani M.A.,University of Tabriz |
Aalami M.T.,University of Tabriz |
Kocak K.,Technical University of Istanbul |
And 3 more authors.
Ocean Dynamics | Year: 2011
Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed: low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another, the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual insights provided by each model. © 2011 Springer-Verlag.
Wang L.,Queensland University of Technology |
Jayaratne R.,Queensland University of Technology |
Heuff D.,URS Australia |
Morawska L.,Queensland University of Technology
Atmospheric Environment | Year: 2010
A composite line source emission (CLSE) model was developed to specifically quantify exposure levels and describe the spatial variability of vehicle emissions in traffic interrupted microenvironments. This model took into account the complexity of vehicle movements in the queue, as well as different emission rates relevant to various driving conditions (cruise, decelerate, idle and accelerate), and it utilised multi-representative segments to capture the accurate emission distribution for real vehicle flow. Hence, this model was able to quickly quantify the time spent in each segment within the considered zone, as well as the composition and position of the requisite segments based on the vehicle fleet information, which not only helped to quantify the enhanced emissions at critical locations, but it also helped to define the emission source distribution of the disrupted steady flow for further dispersion modelling. The model then was applied to estimate particle number emissions at a bi-directional bus station used by diesel and compressed natural gas fuelled buses. It was found that the acceleration distance was of critical importance when estimating particle number emission, since the highest emissions occurred in sections where most of the buses were accelerating and no significant increases were observed at locations where they idled. It was also shown that emissions at the front end of the platform were 43 times greater than at the rear of the platform. Although the CLSE model is intended to be applied in traffic management and transport analysis systems for the evaluation of exposure, as well as the simulation of vehicle emissions in traffic interrupted microenvironments, the bus station model can also be used for the input of initial source definitions in future dispersion models. © 2010 Elsevier Ltd.
Richards A.E.,CSIRO |
Andersen A.N.,CSIRO |
Schatz J.,CSIRO |
Eager R.,CSIRO |
And 4 more authors.
Austral Ecology | Year: 2012
Savanna burning for greenhouse gas abatement presents an opportunity for remote Aboriginal communities of northern Australia to engage with the mainstream economy while fulfilling cultural obligations for land stewardship. The recently established Tiwi Carbon Study aims to identify the biophysical and economic potential of fire management for greenhouse gas abatement on the Tiwi Islands north of Darwin, as a basis for possible livelihood opportunities for the Aboriginal Tiwi people. Recent (2001-2010) fire history for the Tiwi Islands based on AVHRR satellite imagery shows that on average 35% (187700ha) of its savanna woodlands and open forests are burned every year, with 72% of burning occurring late in the dry season (August to November). Non-CO 2 greenhouse gas emissions from Tiwi fires average 68000tCO 2-eyear -1 and we discuss scenarios for greenhouse gas abatement through management of these fires by Tiwi people, consistent with the savanna burning methodology approved under the Federal Government's Carbon Farming Initiative. Changed fire management scenarios produced emissions abatement of up to 46000tCO 2-eyear -1, with highest savings under a change in both fire frequency and intensity. In addition to abatement of non-CO 2 emissions, fire management has the potential to alter rates of carbon sequestered in soil and vegetation. Current ecosystem C stocks (excluding roots) on the Tiwi Islands range from 60 to 160tCha -1. The Tiwi Carbon Study features a long-term, landscape-scale fire experiment for informing full carbon accounting in relation to different fire management options, and for understanding their implications for biodiversity. We discuss potential co-benefits and trade-offs of fire management for greenhouse gas emissions abatement in relation to biodiversity and Tiwi cultural requirements and livelihood aspirations. © 2012 Ecological Society of Australia.
Wang L.,Queensland University of Technology |
Morawska L.,Queensland University of Technology |
Jayaratne E.R.,Queensland University of Technology |
Mengersen K.,Queensland University of Technology |
Heuff D.,URS Australia
Atmospheric Environment | Year: 2011
Measurements of airborne particle number size distributions, particle number and PM2.5 concentrations were conducted at two bus stations of different designs: open station and canyon station, operated according to the same timetables and fleet compositions, as well as at a reference point in Brisbane, Australia. Simultaneous traffic and meteorological parameters were also monitored, aiming to quantify particle characteristics and investigate the impact of station design and meteorological conditions on particle emissions at the two bus stations. It was found that there was no significant difference in average particle number concentrations in the size range 7-3000nm (PN7-3000) between the two stations (fine days: p=0.90 and rainy days: p=0.80), and that PN50-120 contributed to the largest proportion of particle number concentrations. PN20-30 were observed to increase at the open station during all time periods, except 0:00-7:00, which is likely to be attributed to the lower average daily temperature at the open station (around 7°C lower than at the canyon station). During precipitation, it was found that particle number concentration in the size range 25-250nm decreased greatly, and the average daily reduction in PM2.5 concentration on rainy days compared to fine days was 44.2% and 22.6% at the open and canyon station, respectively. The effect of ambient wind speeds on particle number concentration was also examined, and no relationship was found between particle number concentration and wind speed for the entire measurement period. In addition, 33 pairs of average half-hourly PN7-3000 concentrations were calculated and identified at the two stations, during the same time of a day, and with the same ambient wind speeds and precipitation conditions. The results of a paired t-test showed that the average half-hourly PN7-3000 concentrations at the two stations were not significantly different at the 5% confidence level (t=0.06, p=0.96), which indicates that the different station designs were not a crucial factor for influencing PN7-3000 concentrations. This finding implies that the timescale of dispersion at the bus stations was comparatively long, and that the source contribution was more important compared to the atmospheric dispersions associated with different station designs. © 2010 Elsevier Ltd.
Makarynskyy O.,METOcean Dynamic Solutions |
Makarynska D.,URS Australia |
Rayson M.,APASA |
Applied Soft Computing Journal | Year: 2015
Abstract Estimates of suspended sediment concentrations and transport are an important part of any marine environment assessment study because these factors have a direct impact on the life cycle and survival of marine ecosystems. This paper proposes to implement a combined methodology to tackle these estimates. The first component of the methodology comprised two numerical current and wave models, while the second component was based on the artificial intelligence technique of neural networks (ANNs) used to reproduce values of sediment concentrations observed at two sites. The ANNs were fed with modelled currents and waves and trained to produce area-specific concentration estimates. The trained ANNs were then applied to predict sediment concentrations over an independent period of observations. The use of a data set that merged together observations from both the mentioned sites provided the best ANN testing results in terms of both the normalised root mean square error (0.13) and the mean relative error (0.02). © 2015 Elsevier B.V.
Karimi S.,University of Tabriz |
Kisi O.,Canik Basari University |
Shiri J.,University of Tabriz |
Makarynskyy O.,URS Australia
Computers and Geosciences | Year: 2013
Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1. h, 24. h, 48. h and 72. h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals. © 2012 Elsevier Ltd.
Chen K.,URS Australia |
Chapman G.A.,Golder Assoc.
Australian Geomechanics Journal | Year: 2014
This paper presents a case history of a preliminary liquefaction assessment for bridge pile foundations for the Breakwater Road Realignment Bridge in Geelong, Victoria. The bridge site is situated on an alluvial plain of the Barwon River delta and is underlain by a layer of very loose sand sediments. Concerns were raised regarding the potential liquefaction of the very loose sand sediments and the potential impact that soil liquefaction may have on the proposed pile foundations of the bridge. A soil liquefaction assessment was carried out using the procedures initially developed in the 1970s and 1980s (Seed and Idress 1971 and 1982) and lately revised by Youd and Idress et al. in 2001. While the majority of the piled foundations at the site were assessed not to be at risk of liquefaction, two pier locations where very loose sand was encountered were assessed to be potentially liquefiable under the design earthquake loading. At these locations, the pile groups were designed under earthquake loading to allow for the potential liquefaction of the loose layers.
Phillips M.A.,URS Australia |
Lesleighter E.J.,Hydraulics Engineering Specialist
Labyrinth and Piano Key Weirs II, PKW 2013 - Proceedings of the 2nd International Workshop on Labyrinth and Piano Key Weirs 2013 | Year: 2014
A spillway upgrade conceptual design and selection process was undertaken to identify options for upgrading the Dartmouth Dam to pass the Probable Maximum Flood (PMF). The piano key weir was initially developed from the limited available publications on the weir design, and further developed with the use of a 1:60 scale model. The piano key weir, a variation of the labyrinth weir, is a passive spillway that utilises a total weir length several times that of the existing spillway width. For the Dartmouth Dam study, the piano key weir design that was developed consisted of a 7-cycle, 9m high structure, with a total weir length of nearly 600 m, or more than 6 times the existing spillway width of 91 m. The spillway was designed to pass the routed PMF outflow of approximately 11,500m3/s with a head of approximately 11m. ©2014 Taylor & Francis Group.
Makarynskyy O.,URS Australia |
Makarynska D.,URS Australia
20th Australasian Coastal and Ocean Engineering Conference 2011 and the 13th Australasian Port and Harbour Conference 2011, COASTS and PORTS 2011 | Year: 2011
This study presents an application of improved algorithms to calculate bed shear stresses under combined waves and currents in a high-energy macro-tidal estuarine environment. Processes of water circulation and wave transformation were modelled by combining outcomes of the ADCIRC hydrodynamic model with the steady-state spectral wave model STWAVE. The hydrodynamic model results were compared with field data obtained from ADCP records showing good agreement. A grid nesting technique was used to produce high resolution solutions for wind waves in East Arm. Bed shear stress estimates for before-dredging and afterdredging scenarios were produced and inter-compared to identify possible changes in erosion and accretion patters due to dredging and construction developments.