2,507 research outputs found
Characterizing the performance of low impact development under changes in climate and urbanization
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 Sobolâs 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
Forest planning utilizing high spatial resolution data
This thesis presents planning approaches adapted for high spatial resolution data from remote sensing and evaluate whether such approaches can enhance the provision of ecosystem services from forests. The presented methods are compared with conventional, stand-level methods. The main focus lies on the planning concept of dynamic treatment units (DTU), where treatments in small units for modelling ecosystem processes and forest management are clustered spatiotemporally to form treatment units realistic in practical forestry. The methodological foundation of the thesis is mainly airborne laser scanning data (raster cells 12.5x12.5 m2), different optimization methods and the forest decision support system Heureka. Paper I demonstrates a mixed-integer programming model for DTU planning, and the results highlight the economic advances of clustering harvests. Paper II and III presents an addition to a DTU heuristic from the literature and further evaluates its performance. Results show that direct modelling of fixed costs for harvest operations can improve plans and that DTU planning enhances the economic outcome of forestry. The higher spatial resolution of data in the DTU approach enables the planning model to assign management with higher precision than if stand-based planning is applied. Paper IV evaluates whether this phenomenon is also valid for ecological values. Here, an approach adapted for cell-level data is compared to a schematic approach, dealing with stand-level data, for the purpose of allocating retention patches. The evaluation of economic and ecological values indicate that high spatial resolution data and an adapted planning approach increased the ecological values, while differences in economy were small. In conclusion, the studies in this thesis demonstrate how forest planning can utilize high spatial resolution data from remote sensing, and the results suggest that there is a potential to increase the overall provision of ecosystem services if such methods are applied
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Mechanics and Mechanisms of Fracture for an Eastern Spruce Subject to Transverse Loading Using Acoustic Emission
Due to its excellent structural qualities and accessibility, wood is among the most often utilized structural materials. Despite its ubiquity, wood poses numerous challenges. It is heterogeneous and anisotropic. It has a complex hierarchical ultrastructure, and the properties can have wide variation within a species, and indeed within an individual tree. This work aims to improve our understanding of the strength and fracture behavior of spruce-pine-fir (south) (SPFs), particularly in cross-grain direction. This studyâs primary goal is to examine the relationship between crack propagation and cross grain morphology under the following loading configurations: compact tension, compression, and rolling shear. The broader goal is to be able to use this information to improve our ability to predict the performance of mass timber structures. In order to better characterize micromechanical processes and damage progression, acoustic emission (AE) techniques were applied.
In this investigation, fracture in compact tension specimens was characterized by both R-curve and bulk fracture energy approaches. Our results show that the fracture follows a distinct route that deviates from the initial crack direction depending on the end-grain angles. This deviation is driven by a competition between maximum strain energy release rate and minimum crack resistance. For crack propagation in the tangential direction, cracks are confined to an earlywood region, which corresponds to the direction of least resistance. This pattern continues even as the end-grain shifts until an angle of about 40°, when the crack begins to jump across earlywood/latewood rings. At roughly 45°, the crack path shifts to a strictly radial direction, corresponding to a path of least resistance. In order to further quantify different micromechanical mechanisms, acoustic emission monitoring was used to track the propagation of damage. To identify different damage sources, an artificialneural network (ANN) technique was used to detect, classify, and quantify the AE energy sources. Results showed that earlywood cell wall tearing, dominant at 0°, produced higher energy release than cell wall separation, which dominates 90°crack propagation. Fiber bridging was also identified as another damage mechanism that occurs in the later stages of the crack growth, but in cross-grain fracture, it produces minimal AE energy. The same ANN approach was used to identify the damage mechanisms in specimens under rolling shear. Cross-laminated timberâs (CLT) mechanical performance is greatly influenced byrolling shear characteristics. In this work, the impact of end-grain orientation on rolling shear strength and modulus was evaluated. AE signal classification was applied to separate the associated damage modes and to determine the AE energy sources. Macroscopically, damage typically initiates along the glue line, but further crack growth is highly dependent on end grain morphology. Specimens with end-grain parallel to the axis of shear showed tangential propagation along an earlywood line, but as the dominant grain angle changes, cracks jump across growth rings, or if the angle is high enough, shift to a radial direction. AE results showed cell wall tearing to be the dominant energy dissipation mechanism, but cell wall peeling and bridging have significant contributions at higher end-grain angles.
Through this research, we are better able to link damage sources to particular micro mechanical energy dissipation. This information is in a suitable form for inclusion in computational models that can be used to simulate structural performance as a function of material morphology
An Intelligent Time and Performance Efficient Algorithm for Aircraft Design Optimization
Die Optimierung des Flugzeugentwurfs erfordert die Beherrschung der komplexen ZusammenhĂ€nge mehrerer Disziplinen. Trotz seiner AbhĂ€ngigkeit von einer Vielzahl unabhĂ€ngiger Variablen zeichnet sich dieses komplexe Entwurfsproblem durch starke indirekte Verbindungen und eine daraus resultierende geringe Anzahl lokaler Minima aus. KĂŒrzlich entwickelte intelligente Methoden, die auf selbstlernenden Algorithmen basieren, ermutigten die Suche nach einer diesem Bereich zugeordneten neuen Methode. TatsĂ€chlich wird der in dieser Arbeit entwickelte Hybrid-Algorithmus (Cavus) auf zwei HauptdesignfĂ€lle im Luft- und Raumfahrtbereich angewendet: Flugzeugentwurf- und Flugbahnoptimierung. Der implementierte neue Ansatz ist in der Lage, die Anzahl der Versuchspunkte ohne groĂe Kompromisse zu reduzieren. Die Trendanalyse zeigt, dass der Cavus-Algorithmus fĂŒr die komplexen Designprobleme, mit einer proportionalen Anzahl von PrĂŒfpunkten konservativer ist, um die erfolgreichen Muster zu finden.
Aircraft Design Optimization requires mastering of the complex interrelationships of multiple disciplines. Despite its dependency on a diverse number of independent variables, this complex design problem has favourable nature as having strong indirect links and as a result a low number of local minimums. Recently developed intelligent methods that are based on self-learning algorithms encouraged finding a new method dedicated to this area. Indeed, the hybrid (Cavus) algorithm developed in this thesis is applied two main design cases in aerospace area: aircraft design optimization and trajectory optimization. The implemented new approach is capable of reducing the number of trial points without much compromise. The trend analysis shows that, for the complex design problems the Cavus algorithm is more conservative with a proportional number of trial points in finding the successful patterns
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
The role of artificial intelligence-driven soft sensors in advanced sustainable process industries: a critical review
With the predicted depletion of natural resources and alarming environmental issues, sustainable development has become a popular as well as a much-needed concept in modern process industries. Hence, manufacturers are quite keen on adopting novel process monitoring techniques to enhance product quality and process efficiency while minimizing possible adverse environmental impacts. Hardware sensors are employed in process industries to aid process monitoring and control, but they are associated with many limitations such as disturbances to the process flow, measurement delays, frequent need for maintenance, and high capital costs. As a result, soft sensors have become an attractive alternative for predicting quality-related parameters that are âhard-to-measureâ using hardware sensors. Due to their promising features over hardware counterparts, they have been employed across different process industries. This article attempts to explore the state-of-the-art artificial intelligence (Al)-driven soft sensors designed for process industries and their role in achieving the goal of sustainable development. First, a general introduction is given to soft sensors, their applications in different process industries, and their significance in achieving sustainable development goals. AI-based soft sensing algorithms are then introduced. Next, a discussion on how AI-driven soft sensors contribute toward different sustainable manufacturing strategies of process industries is provided. This is followed by a critical review of the most recent state-of-the-art AI-based soft sensors reported in the literature. Here, the use of powerful AI-based algorithms for addressing the limitations of traditional algorithms, that restrict the soft sensor performance is discussed. Finally, the challenges and limitations associated with the current soft sensor design, application, and maintenance aspects are discussed with possible future directions for designing more intelligent and smart soft sensing technologies to cater the future industrial needs
Adaptive vehicular networking with Deep Learning
Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networksâ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities.
In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects.
The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup.
The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios.
The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the âidealâ optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark
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