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Synoptic Maps Forecast Using Spatio-temporal Models

机译:使用时装型模型预测Synoptic Maps预测

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摘要

The objective of this paper is to study several approaches to forecasting the temporal evolution of meteorological synoptic maps that carry information in visual form but without objects. Window-based descriptors are used in order to accomplish continuity so the prediction task is possible. Linear and non-linear models are applied for the prediction task, the first one being based on a spatio-temporal autoregressive (STAR) model whereas the second one is based on artificial neural networks. The method and obtained results are discussed.
机译:本文的目的是研究几种方法,以预测携带视觉形式但没有物体的信息的气象概要地图的时间演变。基于窗口的描述符用于实现连续性,因此可以进行预测任务。用于预测任务的线性和非线性模型,第一是基于时空自回归(星)模型的第一,而第二个是基于人工神经网络。讨论了方法和得到的结果。

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