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Comparative study of different weather forecasting models

机译:不同天气预报模式的比较研究

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摘要

Nowadays, Analysis of atmosphere conditions has become a vital process; where in prediction of state weather for a future time is made based on location. To improve the performance of system, collection of data related to current state of the atmosphere is important. Further using the understanding of atmospheric processes, determine or predict the atmosphere of future by applying ML techniques. The paper discusses application suitable Data Mining techniques, Regression approaches and Artificial Neural Network models to predict weather parameters. A study further ends by comparing different techniques for weather forecasting. The main moto of this study is to compare and identify a precise weather forecasting model. Weather prediction will be effective if, Input Data of years is taken instead of just 2-3 days. Thus, if we train the system by considering huge data the performance will be more effective - “Better the training, better the result”.
机译:如今,大气条件分析已成为至关重要的过程。在预测州天气的情况下,将根据位置进行预测。为了提高系统性能,收集与大气层当前状态相关的数据非常重要。进一步利用对大气过程的理解,通过应用机器学习技术确定或预测未来的大气。本文讨论了适用的数据挖掘技术,回归方法和人工神经网络模型来预测天气参数。通过比较不同的天气预报技术,进一步研究结束。这项研究的主要目的是比较和确定精确的天气预报模型。如果采用年输入数据而不是2-3天,则天气预报将是有效的。因此,如果我们通过考虑大量数据来训练系统,则性能将更加有效-“更好的训练,更好的结果”。

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