38 research outputs found

    Enhancing weather data reconstruction through hybridmethods with dimensionality reduction

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    Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáAccurate weather analysis and forecasting rely on complete historical data. However, missing weather data often occurs due to sensor failures, data transmission issues, or limited monitoring capabilities. Reconstructing this missing data is crucial for reliableweather analysis. The Analog Ensemble (AnEn) method leverages past weather events and information from nearby stations to reconstruct and forecast data. However, incorporating nearby stations significantly increases computational costs, making the reconstruction process time consuming. To address this challenge, this dissertation integrates AnEn with dimension reduction techniques: Principal Component Analysis (PCA) and Partial Least Squares (PLS). Four hybrid methods—PCAnEn, PLSAnEn, PCClustAnEn, and PLSClustAnEn—are developed to enhance computational performance while maintaining or improving accuracy. Through four studies using three datasets, this research focuses on reconstructing six variables: wind-related variables, temperature, pressure, and humidity. The hybrid methods improved accuracy compared to the original AnEn. Notably, PLSAnEn achieves the highest reconstruction accuracy, while PLSR exhibits the fastest processing times. Additionally, PLSClustAnEn also proves to be a alternative for data reconstruction. The findings of this research contribute to the portfolio of strategies for addressing missing weather data.A análise e a previsão climática beneficiam de dados históricos completos. No entanto, é comum faltarem dados meteorológicos devido a falhas nos sensores, problemas na transmissão de dados ou limitações nas capacidades de monitoramento. A reconstrução desses dados ausentes é crucial para uma análise climática confiável. O método Analog Ensemble (AnEn) utiliza eventos meteorológicos passados e informações de estações próximas para reconstruir e prever dados. No entanto, a incorporação de estações próximas aumenta significativamente os custos computacionais, tornando o processo de reconstrução bastante demorado. Para enfrentar esse desafio, esta dissertação integra o AnEn com técnicas de redução de dimensionalidade: Análise de Componentes Principais (PCA) e Mínimos Quadrados Parciais (PLS). Quatro métodos híbridos - PCAnEn, PLSAnEn, PCClustAnEn e PLSClustAnEn - são desenvolvidos para melhorar o desempenho computacional, mantendo ou aumentando a precisão. Por meio de quatro estudos utilizando três conjuntos de dados, esta pesquisa concentrase na reconstrução de variáveis metereológicas. Os métodos híbridos aprimoraram a precisão em comparação como AnEn original. Notavelmente, o PLSAnEn alcança a maior precisão de reconstrução, enquanto o PLSR é mais eficiente em termos computacionais. Além disso, o PLSClustAnEn também se mostra uma alternativa eficiente para a reconstrução de dados. Os resultados desta pesquisa contribuem para um portfólio de estratégias de reconstrução de dados meteorológicos

    Machine learning in solar physics

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    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

    A review of machine learning applications in wildfire science and management

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    Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

    A NOVEL PATH LOSS FORECAST MODEL TO SUPPORT DIGITAL TWINS FOR HIGH FREQUENCY COMMUNICATIONS NETWORKS

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    The need for long-distance High Frequency (HF) communications in the 3-30 MHz frequency range seemed to diminish at the end of the 20th century with the advent of space-based communications and the spread of fiber optic-connected digital networks. Renewed interest in HF has emerged as an enabler for operations in austere locations and for its ability to serve as a redundant link when space-based and terrestrial communication channels fail. Communications system designers can create a “digital twin” system to explore the operational advantages and constraints of the new capability. Existing wireless channel models can adequately simulate communication channel conditions with enough fidelity to support digital twin simulations, but only when the transmitter and receiver have clear line of sight or a relatively simple multi-path reflection between them. With over-the-horizon communications, the received signal depends on refractions of the transmitted signal through ionospheric layers. The time-varying nature of the free electron density of the ionosphere affects the resulting path loss between the transmitter and receiver and is difficult to model over several days. This dissertation examined previous efforts to characterize the ionosphere and to develop HF propagation models, including the Voice of America Coverage Analysis Prediction (VOACAP) tool, to support path loss forecasts. Analysis of data from the Weak Signal Propagation Reporter Network (WSPRnet), showed an average Root Mean Squared Error (RMSE) of 12.9 dB between VOACAP predictions and actual propagation reports on the WSPRnet system. To address the significant error in VOACAP forecasts, alternative predictive models were developed, including the Forecasting Ionosphere-Induced Path Loss (FIIPL) model and evaluated against one month of WSPRnet data collected at eight geographically distributed sites. The FIIPL model leveraged a machine learning algorithm, Long Short Term Memory, to generate predictions that reduced the SNR errors to an average of 4.0 dB RMSE. These results could support more accurate 24-hour predictions and provides an accurate model of the channel conditions for digital twin simulations. Advisor: Hamid R. Sharif-Kashan

    Piecewise Linear Manifold Clustering

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    This work studies the application of topological analysis to non-linear manifold clustering. A novel method, that exploits the data clustering structure, allows to generate a topological representation of the point dataset. An analysis of topological construction under different simulated conditions is performed to explore the capabilities and limitations of the method, and demonstrated statistically significant improvements in performance. Furthermore, we introduce a new information-theoretical validation measure for clustering, that exploits geometrical properties of clusters to estimate clustering compressibility, for evaluation of the clustering goodness-of-fit without any prior information about true class assignments. We show how the new validation measure, when used as regularization criteria, allows creation of clusters that are more informative. A final contribution is a new metaclustering technique that allows to create a model-based clustering beyond point and linear shaped structures. Driven by topological structure and our information-theoretical criteria, this technique provides structured view of the data on new comprehensive and interpretation level. Improvements of our clustering approach are demonstrated on a variety of synthetic and real datasets, including image and climatological data

    Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

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    This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems

    Impactos del Cambio Climático en la Generación de Energía Renovable y Evaluación de Escenarios de Generación Energética

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, leída el 26-04-2022This Thesis was titled Climate Change Impacts on Renewable Energy Generation and Energy Generation Scenarios.Climate change is attributed, among other factors, to greenhouse gas emissions produced by the energy sector (including the transport). At the same time, climate change is expected to affect this sector by changing the availability of resources, altering its enabling conditions and transforming demand patterns. This thesis addresses climate change impacts on renewable generation and electricity demand by providing an overview of the most relevant transformations projected in literature and by developing methodologies and quantitative analysis to ascertain the specific infuence in three case studies.The first and second chapters are focus on estimating climate change impacts in wind and photovoltaic generation in specific plants. Both provide physical and economic projections of expected changes, along with conclusions for the development of energy policies. The last chapter delves into how climate change and the scenarios proposed to curb it, can affect the demand for electricity in a region, due to the expected changes in the generation infrastructure and changes on the demand side such as a high penetration of electric vehicles...Esta Tesis se tituló Impactos del Cambio Climático en la Generación de Energía Renovable y Escenarios de Generación de Energía. El cambio climático se atribuye, entre otras variables, a las emisiones de gases de efecto invernadero producidas por el sector energético (incluyendo el transporte). Al mismo tiempo, el cambio climático se espera que pueda afectar a este sector cambiando la disponibilidad de sus recursos, alterando sus condiciones habilitantes y transformando los patrones de la demanda. Esta Tesis aborda los impactos del cambio climático en la generación renovable y cambios en el comportamiento de la demanda de electricidad, proporcionando una introducción a las transformaciones más relevantes proyectadas por la literatura y desarrollando metodologías y análisis cuantitativos que determinan el impacto específico en tres casos de estudio. El primer y el segundo capítulo se centran en determinar los cambios esperados en la generación eólica y fotovoltaica en plantas específicas, con especial atención en el calentamiento global. Ambos proporcionan proyecciones físicas y económicas de los cambios esperados, junto con conclusiones para el desarrollo de políticas energéticas. El último capítulo profundiza en cómo el cambio climático y los escenarios propuestos para frenarlo, pueden afectar a la demanda de electricidad de una región, debido a los cambios esperados en las infraestructuras de generación y en cambios por el lado de la demanda como sería una elevada penetración de los vehículos eléctricos...Fac. de Ciencias Económicas y EmpresarialesTRUEunpu

    Avances en la regionalización estadística de escenarios de cambio climático para precipitación basados en técnicas de aprendizaje automático

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    A pesar de ser la principal herramienta para estudiar el cambio climático, los modelos globales de clima (GCM) siguen teniendo una resolución espacial limitada y presentan errores sistemáticos considerables con respecto al clima observado. La regionalización estadística pretende resolver este problema aprendiendo relaciones empíricas entre variables de larga escala, bien reproducidas por los GCM (por ejemplo, los vientos sinópticos o el geopotencial), y observaciones locales de la variable en superficie de interés, como la precipitación, objeto de esta tesis. Proponemos una serie de desarrollos novedosos que permiten mejorar la consistencia de los campos regionalizados y producir escenarios regionales plausibles de cambio climático. Los resultados de esta tesis tienen importantes implicaciones para los diferentes sectores que necesitan información fiable de precipitación para llevar a cabo sus evaluaciones de impactos.Even though they are the main tool to study climate change, global climate models (GCMs) still have a limited spatial resolution and exhibit considerable systematic errors with respect to the observed climate. Statistical downscaling aims to solve this issue by learning empirical relationships between large-scale variables, well reproduced by GCMs (such as synoptic winds or geopotential), and local observations of the target surface variable, such as precipitation, the focus of this thesis. We propose a series of novel developments which allow for improving the consistency of the downscaled fields and producing plausible local-to-regional climate change scenarios. The results of this thesis have important implications for the different sectors in need of reliable precipitation information to undertake their impact assessments
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