论文标题
使用机器学习的明智天气预测：田纳西州的案例研究
Smart Weather Forecasting Using Machine Learning:A Case Study in Tennessee
论文作者
论文摘要
传统上，天气预测是在大型复杂物理模型的帮助下进行的，该物理学在很长一段时间内利用了不同的大气条件。由于天气系统的扰动，这些条件通常是不稳定的，导致模型提供不准确的预测。这些模型通常在大型高性能计算（HPC）环境中以数百个节点运行，该节点消耗了大量能量。在本文中，我们提出了一种天气预报技术，该技术利用来自多个气象站的历史数据来训练简单的机器学习模型，该模型可以在很短的时间内提供有关在不久的将来的某些天气条件的可用预测。这些模型可以在资源密集型环境少得多。评估结果表明，模型的准确性足够好，可以与当前的最新技术一起使用。此外，我们表明，利用来自多个相邻区域的气象站数据比仅执行天气预报的区域的数据是有益的。
Traditionally, weather predictions are performed with the help of large complex models of physics, which utilize different atmospheric conditions over a long period of time. These conditions are often unstable because of perturbations of the weather system, causing the models to provide inaccurate forecasts. The models are generally run on hundreds of nodes in a large High Performance Computing (HPC) environment which consumes a large amount of energy. In this paper, we present a weather prediction technique that utilizes historical data from multiple weather stations to train simple machine learning models, which can provide usable forecasts about certain weather conditions for the near future within a very short period of time. The models can be run on much less resource intensive environments. The evaluation results show that the accuracy of the models is good enough to be used alongside the current state-of-the-art techniques. Furthermore, we show that it is beneficial to leverage the weather station data from multiple neighboring areas over the data of only the area for which weather forecasting is being performed.
