Rainfall Prediction: Accuracy Enhancement Using Machine Learning and Forecasting Techniques

Urmay Shah, Sanjay Garg, Neha Sisodiya, N. Dube, Shashikant Sharma
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引用次数: 16

Abstract

The paper is focused to provide the insights of climate to the clients from various businesses, e.g, agriculturists, researchers etc., to comprehend the significance of changes in climate and atmosphere parameters like precipitation, temperature, humidity etc. Precipitation estimate is one of the critical investigations in field of meteorological research. In order to predict precipitation, an endeavor is made to a couple of factual procedures and machine learning techniques to forecast and estimate meteorological parameters. For experimentation purpose daily observations were considered. The accuracy assessment of forecasting model experimentation is done using validation of results with ground truth. The experimentation demonstrates that for forecasting meteorological parameters ARIMA and Neural Network works best, and best classification accuracy in comparison to other machine learning algorithms for forecasting precipitation for next season was given by Random Forest model.
降雨预测:使用机器学习和预测技术提高准确性
本文的重点是为来自不同行业的客户提供气候的见解,例如,农业学家,研究人员等,以了解气候和大气参数变化的意义,如降水,温度,湿度等。降水估算是气象研究领域的关键问题之一。为了预测降水,我们尝试了一些事实程序和机器学习技术来预测和估计气象参数。为了实验目的,考虑了日常观察。对预测模型试验的准确性进行了评估,并对实验结果进行了验证。实验表明,对于气象参数的预测,ARIMA和神经网络效果最好,与其他机器学习算法相比,随机森林模型在预测下一季降水方面的分类精度最高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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