应用软计算概念的天气预报系统:一种新方法

A. Sharma, M. Manoria
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引用次数: 42

摘要

天气预报和警报是气象专业提供的主要服务。许多政府和私人机构正在研究它的行为,但它仍然具有挑战性和不完整。我们提出了一种新的技术来构建代表实际数据的图像学习集。我们将这些数据与即将到来的天气事件联系起来,这些天气事件是基于它们以前的记录和历史,或者我们的系统所识别的任何东西。我们的工作提出了一种新的方法,其中使用软计算技术进行数据解释,即使用神经模糊系统在我们设计的天气系统的测量基础上预测气象位置。这个模型可以帮助我们预测不同的天气条件,如下雨和雷暴,晴天和晴天,也许是多云的天气,也就是说,这个模型的目的是代表一个可能的不利条件的警告系统。我们的模型是用自适应预测模型(AFD)的概念设计的。这个术语的简单含义是,我们的模型有可能捕捉到导致某些天气条件的许多因素之间的复杂关系。结果与气象部门的实际工作进行了比较,证实了基于软计算的模型在气象预报中具有成功应用的潜力。我们认为大气压力是主要关键参数,大气温度和相对湿度是次要关键参数。然而,在一些典型的情况下,我们将研究温度作为天气条件的标志,在这些情况下,我们可以观察到温度的影响。以瓜廖尔气象中心提供的不同天气预报数据为例,验证了该方法的实用性和可信度。预测总是基于神经模糊[2]系统,利用软计算的概念。对天气数据的实时处理表明,基于神经模糊的天气预报不仅优于数值模式的指导预报,而且优于当地官方气象服务预报。自适应预测模型(AFD)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Weather Forecasting System using concept of Soft Computing: A new approach
Weather forecasting and warnings are the major services provided by the meteorological profession.. Many government and private agencies are working on its behavior but still it is challenging and incomplete. We propose a new technique to construct the learning set of images, which represents the actual data. We relate this data to the forthcoming weather events based on their previous records and history or whatever recognized by our system. Our work presents a new approach where the data explanation is performed with soft computing technique i.e. a neuro-fuzzy system is used to predict meteorological position on the basis of measurements by a weather system designed by us. This model can help us in making forecast of different weather conditions like rain and thunderstorm, sunshine and dry day, and perhaps a cloudy weather i.e. purpose of this model is to represent a warning system for likely adverse conditions. Our model is designed with a concept of Adaptive Forecasting Model(AFD) in the mind. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain weather conditions. The results are compared with actual working of meteorological department and these results confirm that our model which is based on soft computing have the potential for successful application to weather forecasting. We considered atmospheric pressure a primary key parameter and atmospheric temperature and relative humidity secondary type. We will, however, examine temperature as signature of weather conditions in some typical cases where we could observe the effect of temperature. As an example, the estimations produced by the proposed methodology were applied on different weather forecasting data provided by the Gwalior meteorology center to make the result more practical and believable. The forecasts are always current are based on neuro-fuzzy[2] systems that utilize the concept of soft computing. Real time processing of weather data indicate that the neuro-fuzzy based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well.Adaptive Forecasting Model(AFD)
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