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  • It could rain: weather forecasting as a reasoning process

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    Olivieri5507878-Published.pdf (712.3Kb)
    File version
    Version of Record (VoR)
    Author(s)
    Cristani, Matteo
    Domenichini, Francesco
    Olivieri, Francesco
    Tomazzoli, Claudio
    Zorzi, Margherita
    Griffith University Author(s)
    Olivieri, Francesco
    Year published
    2018
    Metadata
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    Abstract
    Meteorological forecasting is the process of providing reliable prediction about the future weathear within a given interval of time. Forecasters adopt a model of reasoning that can be mapped onto an integrated conceptual framework. A forecaster essentially precesses data in advance by using some models of machine learning to extract macroscopic tendencies such as air movements, pressure, temperature, and humidity differentials measured in ways that depend upon the model, but fundamentally, as gradientS. Limit values are employed to transform these tendencies in fuzzy values, and then compared to each other in order to extract ...
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    Meteorological forecasting is the process of providing reliable prediction about the future weathear within a given interval of time. Forecasters adopt a model of reasoning that can be mapped onto an integrated conceptual framework. A forecaster essentially precesses data in advance by using some models of machine learning to extract macroscopic tendencies such as air movements, pressure, temperature, and humidity differentials measured in ways that depend upon the model, but fundamentally, as gradientS. Limit values are employed to transform these tendencies in fuzzy values, and then compared to each other in order to extract indicators, and then evaluate these indicators by means of priorities based upon distance in fuzzy valueS. We formalise the method proposed above in a workflow of evaluation steps, and propose an architecture that implements the reasoning techniques.
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    Conference Title
    Procedia Computer Science
    Volume
    126
    DOI
    https://doi.org/10.1016/j.procs.2018.08.019
    Copyright Statement
    © 2018 The Authors. Published by Elsevier Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Subject
    Information and computing sciences
    Science & Technology
    Automation & Control Systems
    Computer Science, Artificial Intelligence
    Computer Science, Cybernetics
    Publication URI
    http://hdl.handle.net/10072/411962
    Collection
    • Conference outputs

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