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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jacob Nesslage Brittany Lopez Barreto Adam Weingram Erin Hestir Environmental Systems Graduate Group University of California Merced Department of Civil and Environmental Engineering University of California Merced Department of Electrical Engineering and Computer Science University of California Merced

High resolution fractional vegetation cover (HR-FVC) is important for many applications, including precision agriculture, forestry, and conservation. For land managers, HR-FVC is most useful when the data can be produced quickly with minimal effort. In this study, we perform data fusion of RGB drone data and multispectral cubesat data for synthetic daily HR-FVC estimation. First, binary classification of 10cm resolution drone data was used to identify vegetation. An AdaBoost model (Accuracy = 0.868, F1-score = 0.840) was selected for further analysis. HR-FVC training data was then produced from drone vegetation maps by calculating the FVC in a 3m pixel – Planet SuperDove resolution, resulting in 238,270 training points. A random forest regression model was used to predict HR-FVC from Planet SuperDove data. The final model’s performance is comparable to similar studies (R 2 = 0.720, RMSE = 0.213), suggesting the methodology could be viable for applications requiring daily HR-FVC datasets.

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Deep Temporal Neural Networks for Water Level Predictions of Watershed systems

Deep Temporal Neural Networks for Water Level Predictions of...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： Jordan Huff Jeremy Watts Anahita Khojandi Jon Hathaway Department of Industrial and Systems Engineering University of Tennessee Knoxville TN USA Department of Civil and Environmental Engineering University of Tennessee Knoxville TN USA

Rainfall-runoff systems are complex hydrological environments that play a critical role in flood prevention. Currently, physics-based, process-driven computational models are often used to forecast future flooding events. However, these physics-based models are computationally expensive and require intensive physical measurements of hydrological environments beyond remote data collection. There is a growing body of literature that applies deep neural networks to time-series data for computationally efficient, real-time flooding predictions without the need for the complete virtual modeling of the hydrological system. However, these deep-learning networks’ robustness at forecasting far into the future remains an open question. In this study, we examine the capabilities of Long Short-Term Memory (LSTM) networks and Temporal Convolutional Networks (TCN), state-of-the-art temporal deep neural networks, to forecast rainfall-runoff system depths. Specifically, this study leverages primary, multi-modal, time-series data collected by remote sensors in the watershed system of Conner Creek, a tributary of the Clinch River in eastern Tennessee. These data were collected in 5-minute intervals over a course of 5 months. Notably, the Conner Creek watershed system consists of four interconnected reservoir basins. We forecast the water level of each reservoir basin independently for times ranging from five minutes to two hours into the future. Our results show that both the LSTM and TCN can effectively model and forecast future reservoir basin water levels. Specifically, when averaged across the four reservoir basins, the LSTM has an mean absolute error (MAE), with a 95% confidence interval, of 0.158 ± 0.049 ft and 0.490 ± 0.260 ft at 5 minutes and 120 minutes into the future, respectively. In comparison, the TCN has an MAE of 0.258 ± 0.160 ft and 0.375 ± 0.245 ft at 5 minutes and 120 minutes into the future, respectively. Our results show that the LSTM model outperforms the TCN fo

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A Meta-Heuristic Solution Approach to Isolated Evacuation Problems 22

A Meta-Heuristic Solution Approach to Isolated Evacuation Pr...

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Proceedings of the Winter Simulation Conference

作者： Klaas Fiete Krutein Linda Ng Boyle Anne Goodchild Department of Industrial & Systems Engineering University of Washington Seattle WA USA Department of Civil & Environmental Engineering University of Washington Seattle WA USA

This paper provides an approximation method for the optimization of isolated evacuation operations, modeled through the recently introduced Isolated Community Evacuation Problem (ICEP). This routing model optimizes the planning for evacuations of isolated areas, such as islands, mountain valleys, or locations cut off through hostile military action or other hazards that are not accessible by road and require evacuation by a coordinated set of special equipment. Due to its routing structure, the ICEP is NP-complete and does not scale well. The urgent need for decisions during emergencies requires evacuation models to be solved quickly. Therefore, this paper investigates solving this problem using a Biased Random-Key Genetic Algorithm. The paper presents a new decoder specific to the ICEP, that allows to translate in between an instance of the S-ICEP and the BRKGA. This method approximates the global optimum and is suitable for parallel processing. The method is validated through computational experiments.

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Modeling System Dynamics of Interacting Cruising Trains to Reduce the Impact of Power Peaks

SSRN

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SSRN 2023年

作者： Trivella, Alessio Corman, Francesco Industrial Engineering and Business Information Systems University of Twente Hallenweg 17 Enschede7522 NH Netherlands Department of Civil Environmental and Geomatic Engineering

Rail operators around the globe are striving to improve the efficiency, automation, safety, and sustainability of railway systems. Despite significant advances in technologies such as artificial intelligence and automated train operations (ATO), achieving these goals is challenging for complex rail networks when accounting for unpredictable factors that alter real-time operations. In this paper, we model railway traffic in a corridor as a string of interacting cruising trains, each subject to random speed variations that are described by a stochastic process. We simulate this dynamic system under assumptions that model human drivers and ATO systems, and compute performance measures focusing on energy consumption and the power peaks arising when multiple trains accelerate simultaneously. We then investigate strategies to smooth these peaks including the use of regenerative braking energy, potentially coupled with an electric energy storage, and a rule that uses fixed waiting times before re-acceleration. Our findings shed light on when and why these strategies can be effective at reducing energy consumption and/or shaving the peaks. We also show that employing a well-calibrated ATO controller in which vehicles exchange information about their location improves energy performance compared to a model of a human driven. We finally expose a trade-off between energy performance and traffic regularity, i.e., strategies to reduce power peaks may slow rail traffic down, reducing capacity utilization. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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Cell Transmission Model Based Connected Automated

SSRN

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SSRN 2024年

作者： Yao, Yifan Pu, Fan Zhou, Yang Wang, Xin Li, Xiaotian Ran, Bin Department of Civil and Environmental Engineering University of Wisconsin-Madison MadisonWI53706 United States Zachry Department of Civil and Environmental Engineering Texas A&M University College StationTX77840 United States Department of Industrial and Systems Engineering University of Wisconsin-Madison MadisonWI53706 United States

The rapid progress in connected automated vehicle (CAV) technology presents new opportunities for advanced traffic management strategies at signalized intersections. Traditional methods have been largely focused on vehicular microscopic control, encountering substantial computational challenges and scalability issues. To overcome these hurdles and fully leverage CAV technology, we introduce a pioneering connected automated flow control (CAFC) strategy. This approach integrates the collective behavior of CAVs using a customized Cell Transmission Model (CTM). It aims to maximize traffic throughput through a comprehensive optimization process by effectively managing each cell’s inflow and outflow, and aligning traffic flow to complement signal patterns, meanwhile satisfying physical traffic laws. The detailed formulation is given as a quadratic programming problem which can be efficiently solved by current solvers. The efficiency and robustness are further validated through extensive numerical experiments, which show its ability to adapt to uncertain vehicle arrivals and achieve real-time coordination. © 2024, The Authors. All rights reserved.

关键词： Quadratic programming

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Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization

arXiv

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arXiv 2024年

作者： Feng, Siqi Yao, Rui Hess, Stephane Daziano, Ricardo A. Brathwaite, Timothy Walker, Joan Wang, Shenhao Department of Systems Engineering Cornell University United States School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Switzerland Institute for Transport Studies and Choice Modelling Centre University of Leeds United Kingdom School of Civil and Environmental Engineering Cornell University United States Department of Civil and Environmental Engineering University of California Berkeley United States Department of Urban and Regional Planning University of Florida United States

Deep neural networks (DNNs) have been increasingly applied in travel demand modeling because of their automatic feature learning, high predictive performance, and economic interpretability. Nevertheless, DNNs frequently present behaviorally irregular patterns, significantly limiting their practical potentials and theoretical validity in travel behavior modeling. This study proposes strong and weak behavioral regularities as novel metrics to evaluate the monotonicity of individual demand functions (known as the "law of demand"), and further designs a constrained optimization framework with six gradient regularizers to enhance DNNs’ behavioral regularity. The empirical benefits of this framework are illustrated by applying these regularizers to travel survey data from Chicago and London, which enables us to examine the trade-off between predictive power and behavioral regularity for large versus small sample scenarios and in-domain versus out-of-domain generalizations. The results demonstrate that, unlike models with strong behavioral foundations such as the multinomial logit, the benchmark DNNs cannot guarantee behavioral regularity. However, after applying gradient regularization, we increase DNNs’ behavioral regularity by around 6 percentage points while retaining their relatively high predictive power. In the small sample scenario, gradient regularization is more effective than in the large sample scenario, simultaneously improving behavioral regularity by about 20 percentage points and log-likelihood by around 1.7%. Compared with the in-domain generalization of DNNs, gradient regularization works more effectively in out-of-domain generalization: it drastically improves the behavioral regularity of poorly performing benchmark DNNs by around 65 percentage points, highlighting the criticality of behavioral regularization for improving model transferability and applications in forecasting. Moreover, the proposed optimization framework is applicable to other neural ne

关键词： Constrained optimization

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Concurrent Geometry, Control, and Layout Optimization of Wave Energy Converter Farms in Probabilistic Irregular Waves using Surrogate Modeling

arXiv

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arXiv 2024年

作者： Azad, Saeed Herber, Daniel R. Khanal, Suraj Jia, Gaofeng Systems Engineering Department Colorado State University Fort CollinsCO United States Civil & Environmental Engineering Department Colorado State University Fort CollinsCO United States

A promising direction towards improving the performance of wave energy converter (WEC) farms is to leverage a system-level integrated approach known as control co-design (CCD). A WEC farm CCD problem may entail decision variables associated with the geometric attributes, control parameters, and layout of the farm. However, solving the resulting optimization problem, which requires the estimation of hydrodynamic coefficients through numerical methods such as multiple scattering (MS), is computationally prohibitive. To mitigate this computational bottleneck, in this article, we construct data-driven surrogate models (SMs) using artificial neural networks in combination with concepts from many-body expansion. The resulting SMs, developed using an active learning strategy known as query by committee, are validated through a variety of methods to ensure acceptable performance in estimating the hydrodynamic coefficients, (energy-related) objective function, and decision variables. To rectify inherent errors in SMs, a hybrid optimization strategy is devised. It involves solving an optimization problem with a genetic algorithm and SMs to generate a starting point that will be used with a gradient-based optimizer and MS. The effectiveness and efficiency of the proposed approach are demonstrated by solving a series of optimization problems with increasing levels of complexity and integration for a 5-WEC farm. For a layout optimization study, the proposed framework offers a 91-fold increase in computational efficiency compared to the direct usage of MS. Previously unexplored investigations of much further complexity are also performed, leading to a concurrent geometry, farm- or device-level control, and layout optimization of heaving cylinder WEC devices in probabilistic irregular waves for a variety of coastal locations in the US. The scalability of the method is evaluated by increasing the farm size to include 25 WEC devices. The results indicate promising directions toward

关键词： Machine learning

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Heuristic Selection in Disaster Recovery Sequencing

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Journal of Infrastructure systems 2025年第3期31卷

作者： Crocker, Abigail J. Boyles, Stephen D. Dept. of Systems Engineering United States Military Academy West PointNY10996 United States Dept. of Civil Architectural and Environmental Engineering Univ. of Texas at Austin AustinTX78712-1700 United States

Transportation network recovery after an extreme hazard or natural disaster is time sensitive and resource intensive, with hundreds or thousands of damaged links needing repair. The associated optimization problem is difficult, and experiments in the published literature are largely confined to smaller-scale instances. We aim to bridge this gap by comparing eight algorithms proposed for repair sequencing on larger-scale instances. These methods include algorithms proposed in prior literature, an improved bidirectional beam search heuristic, and a simulated annealing heuristic newly tailored for network repair sequencing. Our experiments involved over 1,900 random problem instances over two test networks with 8-64 broken links, significantly greater than what has been reported in past literature. We assessed the solution quality and computational needs of these methods. In particular, our simulated annealing heuristic offers high-quality solutions in less than a day for problems with up to 175 and 185 broken links on the Anaheim and Berlin-Mitte-Center test networks, respectively, corresponding to 18%-20% of network links. We also show transferability of the simulated annealing heuristic by tuning its parameters on the Anaheim network, then applying it without further tuning to Berlin-Mitte-Center. Comparable performance was obtained on both networks. © 2025 American Society of civil Engineers.

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Discussing the Spectrum of Physics-Enhanced Machine Learning;a Survey on Structural Mechanics Applications

arXiv

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arXiv 2023年

作者： Haywood-Alexander, Marcus Liu, Wei Bacsa, Kiran Lai, Zhilu Chatzi, Eleni Department of Civil Environmental and Geomatic Engineering ETH Zürich Zürich8049 Switzerland Department of Industrial Systems Engineering and Management National University of Singapore Singapore Future Resilient Systems Singapore-ETH Centre Singapore Guangzhou China Department of Civil and Environmental Engineering HKUST Hong Kong

The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of physics-enhanced machine learning methods, expressed across the defining axes of physics and data, is discussed by engaging in a comprehensive exploration of its characteristics, usage, and motivations. In doing so, we present a survey of recent applications and developments of PEML techniques, revealing the potency of PEML in addressing complex challenges. We further demonstrate application of select such schemes on the simple working example of a single degree-of-freedom Duffing oscillator, which allows to highlight the individual characteristics and motivations of different ‘genres’ of PEML approaches. To promote collaboration and transparency, and to provide practical examples for the reader, the code generating these working examples is provided alongside this paper. As a foundational contribution, this paper underscores the significance of PEML in pushing the boundaries of scientific and engineering research, underpinned by the synergy of physical insights and machine learning capabilities. © 2023, CC BY.

关键词： Degrees of freedom (mechanics)

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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