洪水期间降雨预测的机器学习拟分析

K. Bhargavi, G. Suma
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引用次数: 1

摘要

洪水是最常见的自然灾害,研究人员将重点放在了降雨预测上,以便在洪水到来之前或之后挽救人们的生命。洪水的强度主要取决于强降雨。如果提前很好地预报了降雨,就有助于采取预防措施。本文采用分类模型和回归模型进行预测分析。利用数据集中的rain Tomorrow特征对预测分析进行评价。计算分析表明,使用随机森林分类器和n次多项式回归进行预测具有较高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quasi Analysis of Rainfall Prediction during Floods using Machine Learning
Floods are the most common natural disasters and researchers turned their spotlight on prediction of rainfall for rescuing lives of the people before or after its arrival. The intensity of flood majorly relies on heavy rainfall. If the rainfall is predicted well in advance it will be useful for taking precautionary measures. In this paper, predictive analysis is carried out using both classification and regression models. The prediction analysis is evaluated with the feature rain Tomorrow feature in the dataset. The computation analysis shows that prediction using Random Forest classifier and nth Polynomial regression gives exactness for assessment.
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