Infrastructure Engineering - Research Publications
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ItemSpatial and Spatiotemporal Matching Framework for Causal Inference(Schloss Dagstuhl, 2022-09-01)Matching is a procedure aimed at reducing the impact of observational data bias in causal analysis. Designing matching methods for spatial data reflecting static spatial or dynamic spatio-temporal processes is complex because of the effects of spatial dependence and spatial heterogeneity. Both may be compounded with temporal lag in the dependency effects on the study units. Current matching techniques based on similarity indexes and pairing strategies need to be extended with optimal spatial matching procedures. Here, we propose a decision framework to support analysts through the choice of existing matching methods and anticipate the development of specialized matching methods for spatial data. This framework thus enables to identify knowledge gaps.
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ItemNo Preview AvailablePerformance of a wheat yield prediction model and factors influencing the performance: A review and meta-analysis(ELSEVIER SCI LTD, 2021-09-22)CONTEXT: Process-based crop models provide ways to predict crop growth, evaluate environmental impacts on crops, test various crop management options, and guide crop breeding. They can be used to explore options for mitigating climate change impacts when combined with climate projections and explore mitigation of environmental impacts of production. The Agricultural Production Systems SIMulator (APSIM) is a widely adopted crop model that offers modules for simulation of various crops, soil processes, climate, and grazing within a modelling system that enables robust addition of new components. OBJECTIVE: This study uses APSIM Classic-Wheat as an example to examine yield prediction accuracy of biophysically based crop yield modelling and to analyse the factors influencing the model performance. METHODS: We analysed yield prediction results of APSIM Classic-Wheat from 76 published studies across thirteen countries on four continents. In addition, a meta-database of modelled and observed yields from 30 studies was established and used to identify factors that influence yield prediction uncertainty. RESULTS AND CONCLUSIONS: Our analysis indicates that, with site-specific calibration, APSIM predicts yield with a root mean squared error (RMSE) smaller than 1 t/ha and a normalised RMSE (NRMSE) of about 28%, across a wide range of environmental conditions for independent evaluation periods. The results show increasing errors in yield with limited modelling information and adverse environmental conditions. Using soil hydraulic parameters derived from site-specific measurements and/or tuning cultivar parameters improves yield prediction accuracy: RMSE decreases from 1.25 t/ha to 0.64 t/ha and NRMSE from 32% to 14%. Lower model accuracy was found where APSIM overestimates yield under high water deficit condition and when it underestimates yield under nitrogen limitation. APSIM severely over-predicts yield when some abiotic stresses such as heatwaves and frost affect the crop growth. SIGNIFICANCE: This paper uses APSIM-Wheat as an example to provide perspectives on crop model yield prediction performance under different conditions covering a wide spectrum of management practices, and environments. The findings deepen the understanding of model uncertainty associated with different calibration processes or under various stressed conditions. The results also indicate the need to improve the model's predictive skill by filling functional gaps in the wheat simulations and by assimilating external observations (e.g., biomass information estimated by remote sensing) to adjust the model simulation for stressed crops.
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ItemIntegrating sustainability into higher education curricula(Engineers Australia, 2021)Education has been widely recognised as a key instrument to achieve sustainability. Integrating sustainability knowledge, skills and values are considered paramount to enable individuals to contribute to sustainable development. The paper presents a pilot study conducted at the University of Melbourne to investigate the links between the subjects offered by the University and sustainability. The pilot study is a part of the Sustainability in the Curriculum program, which addresses the Sustainability Plan Teaching and Learning Target 1, aimed to incorporate sustainability knowledge and values in the University's curricula. The 17 Sustainability Development Goals have been used as a framework to measure how well the curricula are linked to sustainability. A study first undertaken to establish the link between subjects and the Sustainability Development Goals is presented. The study involved data collection through published literature on Sustainable Development Goals and the University's subject handbook, followed by a survey involving the subject coordinators. The findings of the study show that the strength of linkages between subjects with sustainability varies, highlighting the challenge in some technical subjects in linking their contents with sustainability. Approaches adopted in the Faculty of Engineering and Information Technology in embedding sustainability in the curriculum are presented with some examples and discussions for the next steps.
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ItemModelling electrical conductivity variation using a travel time distribution approach in the Duck River catchment, Australia(WILEY, 2022-11-01)Solute dynamics depend strongly on hydrologic flow paths and transit times within catchments. In this paper, we use a travel time tracking method to simulate stream salinity (as measured by electrical conductivity) in the Duck River catchment, NW Tasmania, Australia. The study couples storage selection function transit time modelling with two alternate approaches to model electrical conductivity (EC). The first approach assumes the catchment has a cyclic salt balance (i.e., rainfall source, stream flow sink) that is in dynamic equilibrium and evapoconcentration of salt is the only process changing concentration. The second approach assumes that the salinity of water in catchment storages is a function of water age in those stores, without explicitly simulating salt mass balance processes. The paper compares these alternate approaches in terms of EC simulation performance, simulated stream water age distributions, and simulated storage age distributions. A split sample calibration-validation analysis was conducted using the 2008 and 2009 water years. Both EC simulation approaches reproduced stream EC variations very well under both calibration and validation. The simulations using the age-related EC simulation approach produced less biased results and, consequently, higher model coefficient of efficiency for validation periods. This approach also produced more consistent model parameter estimates between periods. There were systematic differences in the resultant age distributions between models, particularly for the solute balance-based simulations where parameters (catchment storage size) changed more between the two calibration periods. The effect of time varying versus static storage selection functions were compared, with clear evidence that time varying storage selection functions with parameters linked to catchment conditions (flow) are essential for adequate simulation of EC dynamics during flow events.
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ItemExplaining changes in rainfall-runoff relationships during and after Australia's Millennium Drought: a community perspective(COPERNICUS GESELLSCHAFT MBH, 2022-12-06)The Millennium Drought lasted more than a decade and is notable for causing persistent shifts in the relationship between rainfall and runoff in many southeastern Australian catchments. Research to date has successfully characterised where and when shifts occurred and explored relationships with potential drivers, but a convincing physical explanation for observed changes in catchment behaviour is still lacking. Originating from a large multi-disciplinary workshop, this paper presents and evaluates a range of hypothesised process explanations of flow response to the Millennium Drought. The hypotheses consider climatic forcing, vegetation, soil moisture dynamics, groundwater, and anthropogenic influence. The hypotheses are assessed against evidence both temporally (e.g. why was the Millennium Drought different to previous droughts?) and spatially (e.g. why did rainfall–runoff relationships shift in some catchments but not in others?). Thus, the strength of this work is a large-scale assessment of hydrologic changes and potential drivers. Of 24 hypotheses, 3 are considered plausible, 10 are considered inconsistent with evidence, and 11 are in a category in between, whereby they are plausible yet with reservations (e.g. applicable in some catchments but not others). The results point to the unprecedented length of the drought as the primary climatic driver, paired with interrelated groundwater processes, including declines in groundwater storage, altered recharge associated with vadose zone expansion, and reduced connection between subsurface and surface water processes. Other causes include increased evaporative demand and harvesting of runoff by small private dams. Finally, we discuss the need for long-term field monitoring, particularly targeting internal catchment processes and subsurface dynamics. We recommend continued investment in the understanding of hydrological shifts, particularly given their relevance to water planning under climate variability and change.
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ItemForeword(The University of Melbourne, 2022-09-27)
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ItemNo Preview AvailableValue of Intersection Cross Box Data in Traffic Signal Control(ITS World Congress, 2021)
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ItemMultimodal relationships: shared and automated vehicles and high-capacity public transit(The Institution of Engineering and Technology, 2021-12-01)Shared mobility is gaining increasing attention in private and public sectors. Serving as a source of information on how best to shape shared vehicle systems of the future, this book contributes knowledge on key facets of shared mobility.
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ItemNo Preview AvailableA Graph and Attentive Multi-Path Convolutional Network for Traffic Prediction(Institute of Electrical and Electronics Engineers, 2022-06-02)Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.