1,355 research outputs found

    Characterizing the performance of low impact development under changes in climate and urbanization

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    Over the past decades, climate change and urbanization have altered the regional hydro-environments, causing a series of stormwater management problems including urban flood and non-point pollution. Low impact development (LID) has been identified as a sustainable strategy for stormwater management. However, given the complex impacts of climate change and urbanization on hydro-environments, the performance of LID strategy under future changes remains largely unexplored. Accordingly, this research characterized the LID performance under changes in climate and urbanization. To provide an additional reference to sustainable stormwater management, the following specific topics were addressed: (1) Through hydraulic and water quality modeling, the LID performances of flood mitigation and pollution removal were systematically evaluated at the city scale. (2) Through uncertainty analysis, the impact of model parameter uncertainty on the LID performance was taken into account. (3) Through sensitivity analysis, the impact of LID technical parameters (e.g., surface features, soil textures) on the LID performance was quantified. (4) Through scenario analysis, the LID performances under different hydrological patterns were compared. (5) Through spatial analysis, the distribution of LID performance on different land-cover types was determined. (6) Through adopting general circulation model (GCM) projections, the LID performance under future climate scenarios with different representative concentration pathways (RCPs) was investigated. (7) Through adopting integrated assessment model (IAM) projections, the LID performance under future urbanization scenarios with different shared socioeconomic pathways (SSPs) was explored. (8) By coupling climate and urbanization projections with land-cover distribution, the spatiotemporal trends of LID performance in the future were characterized.:Table of Contents List of Abbreviations VII List of Peer-Reviewed Publications on the Ph.D. Topic IX List of Co-authored Peer-Reviewed Publications on the Ph.D. Topic X 1 General Introduction 1 1.1 Background 1 1.2 Objectives 3 1.3 Innovation and Contribution to the Knowledge 3 1.4 Outline of the Dissertation 4 1.5 References 5 2 Literature Review 9 2.1 Hydraulic and Water Quality Modeling 9 2.1.1 Hydraulic Model 9 2.1.2 Water Quality Model 10 2.2 Low Impact Development (LID) 10 2.2.1 LID Practice 10 2.2.2 LID Performance 11 2.3 Performance Evaluation 13 2.3.1 Scenario Analysis 13 2.3.2 Spatial Analysis 13 2.3.3 Uncertainty Analysis 14 2.3.4 Sensitivity Analysis 14 2.4 Future Changes in Climate and Urbanization 15 2.4.1 Climate Change 15 2.4.2 Future Urbanization 16 2.5 References 17 3 Impact of Technical Factors on LID Performance 27 3.1 Introduction 28 3.2 Methods 30 3.2.1 Study Area 30 3.2.2 Model Description 31 3.2.2.1 Model Theory 31 3.2.2.2 Model Construction 31 3.2.2.3 Model Calibration and Validation 32 3.2.2.4 Model Uncertainty Analysis by GLUE Method 34 3.2.3 Hydrological Pattern Design 35 3.2.4 LID Strategy Design 35 3.2.4.1 Implementation of LID Practices 35 3.2.4.2 Sensitivity Analysis by Sobol’s Method 36 3.2.5 Correlation Analysis Using a Self-Organizing Map 37 3.2.6 Description of the RDS Load Components 37 3.3 Results 38 3.3.1 RDS Migration and Distribution in Baseline Strategy 38 3.3.1.1 RDS Migration under Hydrological Scenarios 38 3.3.1.2 RDS Distribution on Land-Cover Types 39 3.3.2 RDS Removal in LID Strategies 40 3.3.2.1 RDS Removal by LID Strategies 40 3.3.2.2 Spatial Distribution of the RDS Removal 42 3.3.2.3 LID Parameter Sensitivity Analysis Result 43 3.4 Discussion 45 3.4.1 Factors Influencing RDS Migration in the Baseline Strategy 45 3.4.2 RDS Removal Performance by LID Strategy 46 3.5 Conclusions 47 3.6 References 47 4 Impact of Hydro-Environmental Factors on LID Performance 53 4.1 Introduction 54 4.2 Methods 56 4.2.1 Study Area 56 4.2.2 Modeling Approach 56 4.2.2.1 Model Theory 56 4.2.2.2 Model Construction 56 4.2.2.3 Model Calibration and Validation 57 4.2.2.4 Model Uncertainty Analysis 57 4.2.3 LID Performance Analysis 58 4.2.3.1 LID Practice Implementation 58 4.2.3.2 LID Performance Evaluation 58 4.2.4 Hydrological Pattern Analysis 59 4.2.4.1 Scenario of ADP Length 59 4.2.4.2 Scenario of Rainfall Magnitude 59 4.2.4.3 Scenario of Long-Term pre-Simulation 60 4.2.5 Sensitivity Analysis of Hydrological Scenarios 60 4.3 Results 61 4.3.1 LID Performance under Different ADP Lengths 61 4.3.2 LID Performance under Different Rainfall Magnitudes 62 4.3.3 Spatial Distribution of LID Performance 63 4.3.4 Sensitivities of LID Performance to ADP Length and Rainfall Magnitude 66 4.4 Discussion 68 4.4.1 Impact of ADP Length and Rainfall Magnitude on LID Performance 68 4.4.2 Spatial Heterogeneity of LID Performance 68 4.4.3 Research Implications 69 4.5 Conclusions 70 4.6 References 71 5 Impact of Future Climate Patterns on LID Performance 77 5.1 Introduction 78 5.2 Methods 80 5.2.1 Study Area 80 5.2.2 Hydraulic and Water Quality Model 80 5.2.2.1 Model Development 80 5.2.2.2 Model Calibration and Validation 81 5.2.3 Climate Change Scenario Analysis 81 5.2.3.1 GCM Evaluation 81 5.2.3.2 Greenhouse Gas (GHG) Concentration Scenario 82 5.2.3.3 GCM Downscaling 83 5.2.4 LID Performance Analysis 83 5.2.4.1 Implementation of LID Practices 83 5.2.4.2 Evaluation of LID Performance 84 5.2.4.3 Sensitivity Analysis on LID Performance 86 5.3 Results 86 5.3.1 Hydrological Characteristics under Future Climate Scenarios 86 5.3.2 LID Performance under Future Climate Scenarios 87 5.3.2.1 LID Short-Term Performance 87 5.3.2.2 LID Long-Term Performance 90 5.3.3 Impact of ADP Length and Rainfall Magnitude on LID Performance 92 5.3.3.1 LID Performance Uncertainty 92 5.3.3.2 Spatial Distribution of LID Performance 93 5.3.3.3 Sensitivity of LID Performance to Climate Change 95 5.4 Discussion 97 5.4.1 LID Performance in Short-Term Extremes and Long-Term Events 97 5.4.2 Impact of Climate Change on LID Performance 97 5.4.3 Research Implications 99 5.5 Conclusions 100 5.6 References 100 6 Impact of Climate and Urbanization Changes on LID Perfor-mance 109 6.1 Introduction 110 6.2 Methods 112 6.2.1 Study Area 112 6.2.2 Modeling Approach 112 6.2.2.1 Model Development 112 6.2.2.2 Model Calibration and Validation 113 6.2.3 Future Scenario Derivation 113 6.2.3.1 Climate Change Scenario 113 6.2.3.2 Urbanization Scenario 115 6.2.4 Flood Exposure Assessment 115 6.2.5 Implementation and Evaluation of LID Strategy 117 6.2.5.1 Implementation Scheme of LID Strategy 117 6.2.5.2 Performance Evaluation of LID Strategy 117 6.3 Results 118 6.3.1 Flood Exposure in Baseline and Future Scenarios 118 6.3.1.1 Hydrological Change in Future Climate Scenarios 118 6.3.1.2 Catchment Change in Future Urbanization Scenarios 118 6.3.1.3 Population and GDP Exposures to Flood in Future 121 6.3.2 Flood Exposure with Consideration of LID Strategy 123 6.3.2.1 Runoff Mitigation Performance of LID Strategy 123 6.3.2.2 Peak Mitigation Performance of LID Strategy 124 6.3.2.3 Population and GDP Exposures to Flood under LID Strategy 125 6.4 Discussion 126 6.4.1 Climate Change and Urbanization Exacerbated Flood Exposure Risk 126 6.4.2 LID Strategy Mitigated Flood Exposure Risk 126 6.5 Conclusions 127 6.6 References 127 7 Discussion and General Conclusions 133 7.1 Stormwater Management Performance of LID Strategy 133 7.2 Impact of Influencing Factors on LID Performance 134 7.3 LID Performance under Future Changes 135 7.4 Research Implications 136 7.5 References 137 8 Outlook of Future Research 139 8.1 Optimization of LID Performance 139 8.2 Cross-regional Study on Future Changes 139 8.3 Macro-scale Flood Risk Management 140 8.4 References 141 9 Appendices 143 9.1 Appendix for Chapter 3 143 9.1.1 The Determination of the GLUE Criteria 143 9.1.2 Model Uncertainty Analysis 143 9.1.3 The LID Installation Location 144 9.1.4 Figures 145 9.1.5 Tables 147 9.2 Appendix for Chapter 4 153 9.2.1 Scenario of Long-term pre-Simulation 153 9.2.2 Figures 153 9.2.3 Tables 158 9.3 Appendix for Chapter 5 164 9.3.1 Tables 164 9.4 Appendix for Chapter 6 169 9.4.1 Figures 169 9.4.2 Tables 170 9.5 Data Source 177 9.6 References 17

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Probabilistic short-term load forecasting at low voltage in distribution networks

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    Predmet istraživanja ove doktorske disertacije je kratkoročna probabili- stička prognoza opterećenja na niskom naponu u elektrodistributivnim mre- žama. Cilj istraživanja je da se razvije novo rešenje koje će uvažiti varija- bilnost opterećenja na niskom naponu i ponuditi konkurentnu tačnost prog- noze uz visoku efikasnost sa stanovišta zauzeća računarskih resursa. Predlo- ženo rešenje se zasniva na primeni statističkih metoda i metoda mašinskog (dubokog) učenja u reprezentaciji podataka (ekstrakciji i odabiru atributa), klasterovanju i regresiji. Efikasnost predloženog rešenja je verifikovana u studiji slučaja nad skupom realnih podataka sa pametnih brojila. Rezultat primene predloženog rešenja je visoka tačnost prognoze i kratko vreme izvr- šavanja u poređenju sa konkurentnim rešenjima iz aktuelnog stanja u oblasti.This Ph.D. thesis deals with the problem of probabilistic short-term load forecasting at the low voltage level in power distribution networks. The research goal is to develop a new solution that considers load variability and offers high forecasting accuracy without excessive hardware requirements. The proposed solution is based on the application of statistical methods and machine (deep) learning methods for data representation (feature extraction and selection), clustering, and regression. The efficiency of the proposed solution was verified in a case study on real smart meter data. The case study results confirm that the application of the proposed solution leads to high forecast accuracy and short execution time compared to related solutions

    Towards the cross-identification of radio galaxies with machine learning and the effect of radio-loud AGN on galaxy evolution

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    It is now well established that active galactic nuclei (AGN) play a fundamental role in galaxy evolution. On cosmic scales, the evolution over cosmic time of the star-formation rate density and black hole accretion rate appear to be closely related, and on galactic scales, the mass of the stellar bulge is tightly correlated to the mass of the black hole. In particular, radio-loud AGN, which are characterised by powerful jets extending hundreds of kiloparsecs from the galaxy, make a significant contribution to the evolution of the most massive galaxies. There exists a correlation between the prevalence of radio-loud AGN and the stellar and black hole masses, with the stellar mass being the stronger driver of AGN activity. Furthermore, essentially all of the most massive galaxies host a radio-loud AGN. AGN feedback is the strongest candidate for driving the quenching of star-formation activity, in particular at galaxies at the highest masses, as it is capable of maintaining these galaxies as "red and dead". However, the precise mechanisms by which AGN influence galaxy evolution remain poorly understood. The anticipation of the Square Kilometre Array (SKA) brought radio astronomy into a revolutionary new era. New-generation radio telescopes have been built to develop and test new technologies while addressing different scientific questions. These have already detected a large number of sources and many previously unknown galaxies. One of these telescopes is the Low Frequency Array (LOFAR), which has been conducting an extensive survey across the entire northern sky called the LOFAR Two-Metre Sky Survey (LoTSS). In LoTSS, the source density is higher than in any existing large-area radio survey, and in less than a third of the survey, LoTSS already detected more than 4 million radio sources. The large size of the LoTSS samples already allows the separation of the AGNs into bins of stellar mass, environment, black hole mass, star formation rate, and morphology independently, thus enabling the breaking of degeneracies between the different parameters. The radio, long used to identify and study AGNs, is a powerful tool when radio sources are matched to their optically identified host galaxies. This "cross-matching" process typically depends on a combination of statistical approaches and visual inspection. For compact sources, cross-matching is traditionally achieved using statistical methods. The task becoms significantly more difficult when the radio emission is extended, split into multiple radio components, or when the host galaxy is not detected in the optical. In these cases, sources need to be inspected, radio components need to be eventually associated together into physical sources, and then radio sources need to be cross-matched with their optical and/or infrared counterparts. With recent radio continuum surveys growing massively in size, it is now extremely laborious to visually cross-match more than a small fraction of the total sources. The new high-sensitivity radio telescopes are also better at detecting complex radio structures, resulting in an increase in the number of radio sources whose radio emission is separated into different radio components. In addition, due to a higher density of objects, more compact sources can be randomly positioned close enough to resemble extended sources. Consequently, the cross-matching of radio galaxies with their optical counterparts is becoming increasingly difficult. It is crucial to minimise the extent of unnecessary inspection, with the present cross-matching systems demanding improvement. In this thesis, I use Machine Learning (ML) to investigate solutions to improve the cross-matching process. ML is a rapidly evolving technique that has recently benefited from a vast increase in data availability, increased computing power, and significantly improved algorithms. ML is gaining popularity in the field of astronomy, and it is undoubtedly the most promising technique for managing the large radio astronomy datasets, while having available at the same time the amount of data required to train ML algorithms. Part of the work in this thesis was indeed focused on creating a dataset based on visual inspections of the first data release of the LoTSS survey (LoTSS DR1) in order to train and cross-validate the ML models, and apply the results to the second data release (LoTSS DR2). I trained tree-based ML models using this dataset to determine whether a statistical match is reliable. In particular, I implemented a classifier to identify the sources for which a statistical match to optical and infrared catalogues by likelihood ratio is not reliable in order to select radio sources for visual inspection. I used the properties of the radio sources, the Gaussians that compose a source, the neighbouring radio sources, as well as the optical counterparts. The best model, a gradient boosting classifier, achieves an accuracy of 95% on a balanced dataset and 96% on real unbalanced data after optimising the classification threshold. The results were incorporated in the cross-matching of LoTSS DR2. I further present a deep learning classifier for identifying sources that require radio component association. In order to improve spatial and local information about the radio sources, I create a multi-modal model that makes use of different types of input data, with a convolutional network component of the model receiving radio images as input and a neural network component using parameters measured from the radio source and its near neighbours. The model helps to recover 94% of the sources with multiple components in balanced dataset and has an accuracy of 97% on real unbalanced data. The method has already been applied with success to properly identify sources that require component association in order to get the correct radio fluxes for AGN population studies. The ML techniques used in this work can be adapted to other radio surveys. Furthermore, ML will be crucial to dealing with the next radio surveys, in particular for source detection, identification and cross-matching, where only with reliable source identification is it possible to combine radio data with other data at different wavelengths and maximally exploit the scientific potential of the radio data. The use of deep learning, in particular testing ways of combining different data types, can bring further advantages, as it may help with the comprehension of data with different origins. This is particularly important for any upcoming data integration within the SKA. Finally, I used the results of cross-matching the LoTSS DR2 data to understand the interaction between radio-loud AGN, the host galaxy, and the surrounding environment. Specifically, the investigation focused on the properties of the hosts of radio-loud AGN, such as stellar mass, bulge mass, and black hole mass, as well as morphology and environmental factors. The results consistently support the significant influence of stellar mass on radio-AGN activity. It was found that galaxy morphology (i.e. ellipticals vs. spirals) has a negligible dependence on AGN activity unless at higher masses, but those correlate with stellar mass as well as with the environment. The most relevant factor for radio AGN prevalence, after controlling for stellar mass, emerged as higher-density environments, in particular on a global scale. These outcomes provide valuable insights into the triggering and fuelling mechanisms of radio-loud AGN, aligning with cooling flow models and improving our understanding of the phenomenon

    Application of seismic attributes and unsupervised machine learning methods for identification of hidden faults in basement and carbonate rocks

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    Seismic fault interpretation is a critical task for any type of energy industry and correct fault mapping can be crucial for the success of a project. Common geometric seismic attributes such as coherence and curvature are routinely employed to enhance fault visualization in seismic data, but they can show limitations for sub-seismic faulting. Two projects are presented here showing how recently introduced geometric seismic attributes, such as total aberrancy, and unsupervised machine learning methods, such as self-organizing maps (SOM) and generative topographic mapping (GTM), can be applied for enhancing fault visualization. The first project focuses on an area with potential for CO2 storage in the carbonates of the Duperow Formation, northern Montana. In this study, we compared broadband and multispectral coherence, curvature, and aberrancy, and we compared SOM and GTM techniques when including and excluding aberrancy attributes. Our results showed that integrating aberrancy attributes during the multiattribute analysis and the machine learning steps considerably enhance the visualization of lineaments with strikes similar to those of fracture sets seen only with well log data and missed by the conventional geometric seismic attributes and the ML scenarios excluding aberrancy attributes. The second project is related to wastewater injection and induced seismicity in basement-rooted faults in northcentral Oklahoma. Here, different geometric seismic attributes were analyzed and integrated using unsupervised machine learning to identify potential basement-rooted faults and strike-slip-related structures. The machine learning results not only confirmed the existence of NE-SW faults that extend from the basement upward into the sedimentary section and that correlated with earthquake data but also the potential existence of other NE-SW structurally controlled features of anticlinorium shape

    Data-Driven Exploration of Coarse-Grained Equations: Harnessing Machine Learning

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    In scientific research, understanding and modeling physical systems often involves working with complex equations called Partial Differential Equations (PDEs). These equations are essential for describing the relationships between variables and their derivatives, allowing us to analyze a wide range of phenomena, from fluid dynamics to quantum mechanics. Traditionally, the discovery of PDEs relied on mathematical derivations and expert knowledge. However, the advent of data-driven approaches and machine learning (ML) techniques has transformed this process. By harnessing ML techniques and data analysis methods, data-driven approaches have revolutionized the task of uncovering complex equations that describe physical systems. The primary goal in this thesis is to develop methodologies that can automatically extract simplified equations by training models using available data. ML algorithms have the ability to learn underlying patterns and relationships within the data, making it possible to extract simplified equations that capture the essential behavior of the system. This study considers three distinct learning categories: black-box, gray-box, and white-box learning. The initial phase of the research focuses on black-box learning, where no prior information about the equations is available. Three different neural network architectures are explored: multi-layer perceptron (MLP), convolutional neural network (CNN), and a hybrid architecture combining CNN and long short-term memory (CNN-LSTM). These neural networks are applied to uncover the non-linear equations of motion associated with phase-field models, which include both non-conserved and conserved order parameters. The second architecture explored in this study addresses explicit equation discovery in gray-box learning scenarios, where a portion of the equation is unknown. The framework employs eXtended Physics-Informed Neural Networks (X-PINNs) and incorporates domain decomposition in space to uncover a segment of the widely-known Allen-Cahn equation. Specifically, the Laplacian part of the equation is assumed to be known, while the objective is to discover the non-linear component of the equation. Moreover, symbolic regression techniques are applied to deduce the precise mathematical expression for the unknown segment of the equation. Furthermore, the final part of the thesis focuses on white-box learning, aiming to uncover equations that offer a detailed understanding of the studied system. Specifically, a coarse parametric ordinary differential equation (ODE) is introduced to accurately capture the spreading radius behavior of Calcium-magnesium-aluminosilicate (CMAS) droplets. Through the utilization of the Physics-Informed Neural Network (PINN) framework, the parameters of this ODE are determined, facilitating precise estimation. The architecture is employed to discover the unknown parameters of the equation, assuming that all terms of the ODE are known. This approach significantly improves our comprehension of the spreading dynamics associated with CMAS droplets

    Machine learning-based estimation and clustering of statistics within stratigraphic models as exemplified in Denmark

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    Estimating a covariance model for kriging purposes is traditionally done using semivariogram analyses, where an empirical semivariogram is calculated, and a chosen semivariogram model, usually defined by a sill and a range, is fitted. We demonstrate that a convolutional neural network can estimate such a semivariogram model with comparable accuracy and precision by training it to recognise the relationship between realisations of Gaussian random fields and the sill and range values that define it, for a Gaussian type semivariance model. We do this by training the network with synthetic data consisting of many such realisations with the sill and range as the target variables. Because training takes time, the method is best suited for cases where many models need to be estimated since the actual estimation itself is about 70 times faster with the neural network than with the traditional approach. We demonstrate the viability of the method in three ways: (1) we test the model’s performance on the validation data, (2) we do a test where we compare the model to the traditional approach and (3) we show an example of an actual application of the method using the Danish national hydrostratigraphic model
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