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Weather forecasting using Hidden Markov Model

机译:使用隐马尔可夫模型进行天气预报

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摘要

Since the weather conditions in India are unpredictable, an approach must be developed to forecast weather efficiently. By forecasting weather precisely we can prevent and overcome many hazards that could lead to great loss to a nation. So, in order to do this, Hidden Markov Model was chosen. A Hidden Markov Model (HMM) is a statistical Markov model in which the framework being modeled is thought to be a Markov chain with in hidden (secret) states. In this paper HMM is used to predict weather using Markov Chain property. The training of the model and probability of occurrence of an event is calculated by observing weather data for last 21 years. The data is firstly categorized based on standard values set apart. The result obtained shows that our model is reliable and works very well in predicting weather for next 5 days based on today's weather pattern.
机译:由于印度的天气状况无法预测,因此必须开发一种有效预测天气的方法。通过精确地预测天气,我们可以预防和克服许多可能导致国家蒙受巨大损失的危险。因此,为了做到这一点,选择了隐马尔可夫模型。隐马尔可夫模型(HMM)是统计马尔可夫模型,其中建模的框架被认为是具有隐藏(秘密)状态的马尔可夫链。在本文中,HMM用于利用马尔可夫链属性来预测天气。通过观察最近21年的天气数据来计算模型的训练和事件发生的概率。首先根据分开设置的标准值对数据进行分类。获得的结果表明,我们的模型是可靠的,并且根据今天的天气模式在预测未来5天的天气方面效果很好。

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