3,616 research outputs found

    Integrating expert-based objectivist and nonexpert-based subjectivist paradigms in landscape assessment

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    This thesis explores the integration of objective and subjective measures of landscape aesthetics, particularly focusing on crowdsourced geo-information. It addresses the increasing importance of considering public perceptions in national landscape governance, in line with the European Landscape Convention's emphasis on public involvement. Despite this, national landscape assessments often remain expert-centric and top-down, facing challenges in resource constraints and limited public engagement. The thesis leverages Web 2.0 technologies and crowdsourced geographic information, examining correlations between expert-based metrics of landscape quality and public perceptions. The Scenic-Or-Not initiative for Great Britain, GIS-based Wildness spatial layers, and LANDMAP dataset for Wales serve as key datasets for analysis. The research investigates the relationships between objective measures of landscape wildness quality and subjective measures of aesthetics. Multiscale geographically weighted regression (MGWR) reveals significant correlations, with different wildness components exhibiting varying degrees of association. The study suggests the feasibility of incorporating wildness and scenicness measures into formal landscape aesthetic assessments. Comparing expert and public perceptions, the research identifies preferences for water-related landforms and variations in upland and lowland typologies. The study emphasizes the agreement between experts and non-experts on extreme scenic perceptions but notes discrepancies in mid-spectrum landscapes. To overcome limitations in systematic landscape evaluations, an integrative approach is proposed. Utilizing XGBoost models, the research predicts spatial patterns of landscape aesthetics across Great Britain, based on the Scenic-Or-Not initiatives, Wildness spatial layers, and LANDMAP data. The models achieve comparable accuracy to traditional statistical models, offering insights for Landscape Character Assessment practices and policy decisions. While acknowledging data limitations and biases in crowdsourcing, the thesis discusses the necessity of an aggregation strategy to manage computational challenges. Methodological considerations include addressing the modifiable areal unit problem (MAUP) associated with aggregating point-based observations. The thesis comprises three studies published or submitted for publication, each contributing to the understanding of the relationship between objective and subjective measures of landscape aesthetics. The concluding chapter discusses the limitations of data and methods, providing a comprehensive overview of the research

    Comparative Evaluation of Deep Learning Techniques in Streamflow Monthly Prediction of the Zarrine River Basin

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    Predicting monthly streamflow is essential for hydrological analysis and water resource management. Recent advancements in deep learning, particularly long short-term memory (LSTM) and recurrent neural networks (RNN), exhibit extraordinary efficacy in streamflow forecasting. This study employs RNN and LSTM to construct data-driven streamflow forecasting models. Sensitivity analysis, utilizing the analysis of variance (ANOVA) method, also is crucial for model refinement and identification of critical variables. This study covers monthly streamflow data from 1979 to 2014, employing five distinct model structures to ascertain the most optimal configuration. Application of the models to the Zarrine River basin in northwest Iran, a major sub-basin of Lake Urmia, demonstrates the superior accuracy of the RNN algorithm over LSTM. At the outlet of the basin, quantitative evaluations demonstrate that the RNN model outperforms the LSTM model across all model structures. The S3 model, characterized by its inclusion of all input variable values and a four-month delay, exhibits notably exceptional performance in this aspect. The accuracy measures applicable in this particular context were RMSE (22.8), R2 (0.84), and NSE (0.8). This study highlights the Zarrine River’s substantial impact on variations in Lake Urmia’s water level. Furthermore, the ANOVA method demonstrates exceptional performance in discerning the relevance of input factors. ANOVA underscores the key role of station streamflow, upstream station streamflow, and maximum temperature in influencing the model’s output. Notably, the RNN model, surpassing LSTM and traditional artificial neural network (ANN) models, excels in accurately mimicking rainfall–runoff processes. This emphasizes the potential of RNN networks to filter redundant information, distinguishing them as valuable tools in monthly streamflow forecasting

    Sustainable Collaboration: Federated Learning for Environmentally Conscious Forest Fire Classification in Green Internet of Things (IoT)

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    Forests are an invaluable natural resource, playing a crucial role in the regulation of both local and global climate patterns. Additionally, they offer a plethora of benefits such as medicinal plants, food, and non-timber forest products. However, with the growing global population, the demand for forest resources has escalated, leading to a decline in their abundance. The reduction in forest density has detrimental impacts on global temperatures and raises the likelihood of forest fires. To address these challenges, this paper introduces a Federated Learning framework empowered by the Internet of Things (IoT). The proposed framework integrates with an Intelligent system, leveraging mounted cameras strategically positioned in highly vulnerable areas susceptible to forest fires. This integration enables the timely detection and monitoring of forest fire occurrences and plays its part in avoiding major catastrophes. The proposed framework incorporates the Federated Stochastic Gradient Descent (FedSGD) technique to aggregate the global model in the cloud. The dataset employed in this study comprises two classes: fire and non-fire images. This dataset is distributed among five nodes, allowing each node to independently train the model on their respective devices. Following the local training, the learned parameters are shared with the cloud for aggregation, ensuring a collective and comprehensive global model. The effectiveness of the proposed framework is assessed by comparing its performance metrics with the recent work. The proposed algorithm achieved an accuracy of 99.27 % and stands out by leveraging the concept of collaborative learning. This approach distributes the workload among nodes, relieving the server from excessive burden. Each node is empowered to obtain the best possible model for classification, even if it possesses limited data. This collaborative learning paradigm enhances the overall efficiency and effectiveness of the classification process, ensuring optimal results in scenarios where data availability may be constrained

    Information actors beyond modernity and coloniality in times of climate change:A comparative design ethnography on the making of monitors for sustainable futures in Curaçao and Amsterdam, between 2019-2022

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    In his dissertation, Mr. Goilo developed a cutting-edge theoretical framework for an Anthropology of Information. This study compares information in the context of modernity in Amsterdam and coloniality in Curaçao through the making process of monitors and develops five ways to understand how information can act towards sustainable futures. The research also discusses how the two contexts, that is modernity and coloniality, have been in informational symbiosis for centuries which is producing negative informational side effects within the age of the Anthropocene. By exploring the modernity-coloniality symbiosis of information, the author explains how scholars, policymakers, and data-analysts can act through historical and structural roots of contemporary global inequities related to the production and distribution of information. Ultimately, the five theses propose conditions towards the collective production of knowledge towards a more sustainable planet

    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

    Weather and climate data for energy applications

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    Weather information plays a critical role in energy applications — from designing and planning to the management and maintenance of building energy systems, renewable energy applications, and smart utility grids. This research examines weather and climate data for energy applications, covering their sources, generation, implementation, and forecasting. Drivers for the use of weather data, data acquisition methods, and parameter characteristics, as well as their impact on energy applications, are critically reviewed. The study also analyses weather data availability from 32 commonly used online sources, considering their cost, features, and resolution. A comprehensive weather data classification is developed based on measurement type, information period, data resolution, and time horizon. The findings indicate that real-time local weather data with high temporal resolution is crucial for optimal energy management and accurate forecasting of energy and environmental behaviours. However, limitations and uncertainties exist in weather data from online sources, particularly for developing countries, due to the limited spatio-temporal coverage

    Efficient data-driven machine learning models for scour depth predictions at sloping sea defences

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    Seawalls are critical defence infrastructures in coastal zones that protect hinterland areas from storm surges, wave overtopping and soil erosion hazards. Scouring at the toe of sea defences, caused by wave-induced accretion and erosion of bed material imposes a significant threat to the structural integrity of coastal infrastructures. Accurate prediction of scour depths is essential for appropriate and efficient design and maintenance of coastal structures, which serve to mitigate risks of structural failure through toe scouring. However, limited guidance and predictive tools are available for estimating toe scouring at sloping structures. In recent years, Artificial Intelligence and Machine Learning (ML) algorithms have gained interest, and although they underpin robust predictive models for many coastal engineering applications, such models have yet to be applied to scour prediction. Here we develop and present ML-based models for predicting toe scour depths at sloping seawall. Four ML algorithms, namely, Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Artificial Neural Networks (ANNs), and Support Vector Machine Regression (SVMR) are utilised. Comprehensive physical modelling measurement data is utilised to develop and validate the predictive models. A Novel framework for feature selection, feature importance, and hyperparameter tuning algorithms are adopted for pre- and post-processing steps of ML-based models. In-depth statistical analyses are proposed to evaluate the predictive performance of the proposed models. The results indicate a minimum of 80% prediction accuracy across all the algorithms tested in this study and overall, the SVMR produced the most accurate predictions with a Coefficient of Determination (r2) of 0.74 and a Mean Absolute Error (MAE) value of 0.17. The SVMR algorithm also offered most computationally efficient performance among the algorithms tested. The methodological framework proposed in this study can be applied to scouring datasets for rapid assessment of scour at coastal defence structures, facilitating model-informed decision-making

    What can we learn from a master plot of energy rate versus mass for a very wide variety of (complex) systems?

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    Mass and energy rate (ER) data have been collected for a wide variety of (complex) systems from the biological, cultural, and cosmological realms. They range from the cytochrome oxidase protein (10-22 kg and 6x10-19 W) to the observable universe (1.5x1053 kg and 1048 W) and, thus, span 75 mass and 66 ER orders of magnitude. Many of these systems are relevant for the big history (BH) narrative, i.e., the development of complexity over “big time” from the Big Bang up to the human society on Earth of today. The purpose of this paper is not per se to describe their history though, but to explore a master plot of ER vs. mass. Notably, the development of systems over big time has followed a rather tortuous path criss-crossing over this ER vs. mass master plot. The true mass of the system as a whole is used (for example trees including the non-living wood, living organisms including their intrinsic water, and social systems including the built constructs), because these inactive parts are essential for the performance of the system and facilitate its ER. A double logarithmic master plot of all ER vs. mass data shows clusters of data points. To some extent, this provides quantitative support for the distinction between the (sub-)realms, which is based on a qualitative description of their material structure and energy processing. In the master plot, small systems with low mass and ER converge into larger systems with larger mass and ER, which is typically accompanied by a decrease of the energy rate density (ERD = ER/mass). Correlation of ER with mass for various groups of systems demonstrates both sub- and supra-linear scaling with the power law ÎČ constant varying between 0.5 and 4.0, showing that the mechanisms of self-organisation are quite different for the corresponding system groups. The combination of convergence and scaling with ÎČ always larger than zero explains why the ER & mass data points fall in a diagonal band with a width of 17 orders of magnitude. ER and mass have changed over wide ranges during the evolution of groups of systems, suggesting that evolution can be viewed as a process of systems exploring a larger ER vs. mass area until they run into ER and/or mass limitations. Indeed, there is a diagonal ER vs. mass limit for stable systems in all realms, corresponding to an ERD value of around 105 W/kg. Systems with ER & mass combinations above this limit, such as bombs, super-novae and cosmological transients, are unstable and “explosive”. This raises the interesting question of whether such an ERD maximum puts a limit on the development of complexity over big time. It seems that the low, right side of the master plot is empty. However, it is argued here that it is full of systems with low ER, such as dormant, living organisms, technological systems with their power adjusted or even switched off, as well as cooling, cosmological objects. Such systems are typically considered of less interest in a BH context, but they are viewed here as simple, complex systems which are out of equilibrium with matter, energy and information stored in their structure. While ERD appears to increase with the ‘advancement’ of systems over big time [5,51,52], there are quite some confounding factors regarding the efficacy of ERD as a metric for complexity in BH. For example, ERD decreases during the lifetime of a human and the human society (the mass of human-made constructs has grown faster than the global energy consumption), as well as during the evolution of living organisms and stars, whereas complexity is considered to increase. High ERD values of system parts may be illustrative for the complexity of the larger system, but are not representative for ERD of the system itself. Machines with an increased efficiency of energy conversion have a lower ERD, but could be considered more complex. The smallest and largest ERD values observed for the various realms appear to correlate with activity level and reciprocally with size, which do not per se reflect complexity. It is hoped that the raw data collected and the major trends observed in this paper will offer new insights into various aspects of the evolution of the universe over big time, and serve as an important resource for other related studies

    Data-assisted modeling of complex chemical and biological systems

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    Complex systems are abundant in chemistry and biology; they can be multiscale, possibly high-dimensional or stochastic, with nonlinear dynamics and interacting components. It is often nontrivial (and sometimes impossible), to determine and study the macroscopic quantities of interest and the equations they obey. One can only (judiciously or randomly) probe the system, gather observations and study trends. In this thesis, Machine Learning is used as a complement to traditional modeling and numerical methods to enable data-assisted (or data-driven) dynamical systems. As case studies, three complex systems are sourced from diverse fields: The first one is a high-dimensional computational neuroscience model of the Suprachiasmatic Nucleus of the human brain, where bifurcation analysis is performed by simply probing the system. Then, manifold learning is employed to discover a latent space of neuronal heterogeneity. Second, Machine Learning surrogate models are used to optimize dynamically operated catalytic reactors. An algorithmic pipeline is presented through which it is possible to program catalysts with active learning. Third, Machine Learning is employed to extract laws of Partial Differential Equations describing bacterial Chemotaxis. It is demonstrated how Machine Learning manages to capture the rules of bacterial motility in the macroscopic level, starting from diverse data sources (including real-world experimental data). More importantly, a framework is constructed though which already existing, partial knowledge of the system can be exploited. These applications showcase how Machine Learning can be used synergistically with traditional simulations in different scenarios: (i) Equations are available but the overall system is so high-dimensional that efficiency and explainability suffer, (ii) Equations are available but lead to highly nonlinear black-box responses, (iii) Only data are available (of varying source and quality) and equations need to be discovered. For such data-assisted dynamical systems, we can perform fundamental tasks, such as integration, steady-state location, continuation and optimization. This work aims to unify traditional scientific computing and Machine Learning, in an efficient, data-economical, generalizable way, where both the physical system and the algorithm matter
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