Infrastructure Engineering - Research Publications
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ItemNo Preview AvailableAssessment of surrogate models for flood inundation: The physics-guided LSG model vs. state-of-the-art machine learning models(PERGAMON-ELSEVIER SCIENCE LTD, 2024-03-15)Hydrodynamic models can accurately simulate flood inundation but are limited by their high computational demand that scales non-linearly with model complexity, resolution, and domain size. Therefore, it is often not feasible to use high-resolution hydrodynamic models for real-time flood predictions or when a large number of predictions are needed for probabilistic flood design. Computationally efficient surrogate models have been developed to address this issue. The recently developed Low-fidelity, Spatial analysis, and Gaussian Process Learning (LSG) model has shown strong performance in both computational efficiency and simulation accuracy. The LSG model is a physics-guided surrogate model that simulates flood inundation by first using an extremely coarse and simplified (i.e. low-fidelity) hydrodynamic model to provide an initial estimate of flood inundation. Then, the low-fidelity estimate is upskilled via Empirical Orthogonal Functions (EOF) analysis and Sparse Gaussian Process models to provide accurate high-resolution predictions. Despite the promising results achieved thus far, the LSG model has not been benchmarked against other surrogate models. Such a comparison is needed to fully understand the value of the LSG model and to provide guidance for future research efforts in flood inundation simulation. This study compares the LSG model to four state-of-the-art surrogate flood inundation models. The surrogate models are assessed for their ability to simulate the temporal and spatial evolution of flood inundation for events both within and beyond the range used for model training. The models are evaluated for three distinct case studies in Australia and the United Kingdom. The LSG model is found to be superior in accuracy for both flood extent and water depth, including when applied to flood events outside the range of training data used, while achieving high computational efficiency. In addition, the low-fidelity model is found to play a crucial role in achieving the overall superior performance of the LSG model.
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ItemSizing behind-the-meter solar PV for pumped water distribution systems: A comparison of methods(Elsevier BV, 2024-01-01)Water distribution systems (WDSs) are vital urban infrastructure systems. To meet increasing pumping energy demands and minimise environmental impacts, behind-the-meter (BTM) solar photovoltaic (PV) systems have been considered by water utilities. However, there currently is not a systematic approach to size BTM solar PV for WDSs, considering the life cycle performance of the integrated systems. This study evaluates three methods to size BTM solar PV in pumped WDSs: 1) the heuristic method developed from current industry practice; 2) the minimum total life cycle cost (TLCC) method based on the system minimum TLCC; and 3) the minimum payback method to minimise the time needed to pay off the solar capital investment. The performance of the integrated water-solar system has been assessed against economic, energy and emissions performance metrics using two case studies. The results indicate that the heuristic method leads to the largest solar PV size, potentially oversizing the system. The minimum payback method leads to the smallest solar PV system, potentially under-sizing the system. The minimum TLCC method leads to more balanced system performance, but the solar PV size determined using this method is sensitive to the discount rate used. The insights into the performance of the system sized using the three methods provide decision-makers guidance to select appropriate solar PV systems for WDSs.
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ItemNo Preview AvailableSpatial-Mode-Based Calibration (SMoC) of Forecast Precipitation Fields with Spatially Correlated Structures: An Extended Evaluation and Comparison with Gridcell-by-Gridcell Postprocessing(American Meteorological Society, 2023-09-01)Abstract Postprocessing forecast precipitation fields from numerical weather prediction models aims to produce ensemble forecasts that are of high quality at each grid cell and, importantly, are spatially structured in an appropriate manner. A conventional approach, the gridcell-by-gridcell postprocessing, typically consists of two steps: 1) perform statistical calibration separately at individual grid cells to generate unbiased, skillful, and reliable ensemble forecasts and 2) employ ensemble reordering to link ensemble members of all grid cells according to certain templates to form spatially structured ensemble forecasts. However, ensemble reordering techniques are generally problematic in practical use. For example, the well-known Schaake shuffle is often criticized for not considering real physical atmospheric conditions. In this context, a fundamentally new approach, namely, spatial-mode-based calibration (SMoC), has recently been developed for postprocessing forecast precipitation fields with inbuilt spatial structures, thereby eliminating the need for ensemble reordering. SMoC was tested on 1-day-ahead forecasts of heavy precipitation events and was found to produce ensemble forecasts with appropriate spatial structures. In this paper, we extend SMoC to calibrate forecasts of light and no precipitation events and forecasts at long lead times. We also compare SMoC with the gridcell-by-gridcell postprocessing. Results based on multiple evaluation metrics show that SMoC performs well in calibrating both forecasts of light and no precipitation events and forecasts at long lead times. Compared with the gridcell-by-gridcell postprocessing, SMoC produces ensemble forecasts with similar forecast skill, improved forecast reliability, and clearly better spatial structures. In addition, SMoC is computationally far more efficient.
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ItemNo Preview AvailableEnergy recovery strategies in water distribution networks: literature review and future directions in the net-zero transition(TAYLOR & FRANCIS LTD, 2023-01-01)
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ItemNo Preview AvailableWater Distribution System Optimization Considering Behind-the-Meter Solar Energy with a Hydraulic Power-Based Search-Space Reduction Method(ASCE-AMER SOC CIVIL ENGINEERS, 2023-09-01)
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ItemNo Preview AvailableSupercharging hydrodynamic inundation models for instant flood insight(Springer Science and Business Media LLC, 2023-10-01)
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ItemNo Preview AvailableOptimal design of micro pumped-storage plants in the heart of a city(ELSEVIER, 2024-02)
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ItemNo Preview AvailableExploding the myths: An introduction to artificial neural networks for prediction and forecasting(Elsevier BV, 2023-09-01)
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