{"product_id":"on-spatiotemporal-data-modelling-and-uncertainty-quantification-using-machine-learning-and-information-theory-9783030952334","title":"On Spatio-Temporal Data Modelling and Uncertainty Quantification Using Machine Learning and Information Theory","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThe growth of data indexed in space and time requires advanced tools to analyze them. This thesis proposes a framework for modeling with machine learning algorithms and uncertainty quantification using the extreme learning machine and Fisher-Shannon analysis. Examples include wind speed modeling and renewable energy assessment in environmental sciences. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 158 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 13 March 2023\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Springer Nature Switzerland AG\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThe exponential growth of data indexed in space and time has necessitated the development of advanced and tailored tools to analyze it effectively. This thesis focuses on exploring and modeling complex high-frequency and non-stationary spatio-temporal data. It proposes an efficient framework for modeling spatio-temporal fields measured on irregular monitoring networks, accounting for high-dimensional input spaces and large data sets. The uncertainty quantification is enabled by specifying this framework with the extreme learning machine, a specialized type of artificial neural network for which analytical results, variance estimation, and confidence intervals are developed. Additionally, the thesis emphasizes a highly versatile exploratory data analysis tool based on information theory, the Fisher-Shannon analysis, which can be used to assess the complexity of distributional properties of temporal, spatial, and spatio-temporal data sets.\u003cbr\u003e\u003cbr\u003eExamples of the proposed methodologies are concentrated on data from environmental sciences, with an emphasis on wind speed modeling in complex mountainous terrain and the resulting renewable energy assessment. The contributions of this thesis have broad applications in various research domains where exploration, understanding, clustering, interpolation, and forecasting of complex phenomena are of paramount importance.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 279g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 235 x 155 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9783030952334\u003cbr\u003e \u003cstrong\u003eEdition number\u003c\/strong\u003e: 1st ed. 2022\u003c\/p\u003e","brand":"Fabian Guignard","offers":[{"title":"Paperback \/ softback","offer_id":44304023519482,"sku":"9783030952334","price":108.28,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/noImage_1_eacb60b9-e37d-471d-9e20-719a04010288.jpg?v=1688020899","url":"https:\/\/shulphink.com\/products\/on-spatiotemporal-data-modelling-and-uncertainty-quantification-using-machine-learning-and-information-theory-9783030952334","provider":"Shulph Ink","version":"1.0","type":"link"}