It could rain: weather forecasting as a reasoning process

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Author(s)
Cristani, Matteo
Domenichini, Francesco
Olivieri, Francesco
Tomazzoli, Claudio
Zorzi, Margherita
Griffith University Author(s)
Year published
2018
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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 ...
View more >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.
View less >
View more >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.
View less >
Conference Title
Procedia Computer Science
Volume
126
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