在统计和ANN方法学中对严重雷暴的比较研究
Sonia Bhattacharya1, Himadri Chakraborty Bhattacharyya2
1State Aided College Teacher, Department of Computer Science, Panihati Mahavidyalaya Barasat Road, Sodepur, Kolkata, India. sonia.rpe2020@gmail.com.
Scientific reports
|July 25, 2023
概括
准确的严重雷暴预测对于公共安全至关重要. 机器学习模型,特别是辐射基函数网络 (RBFN),在预测10-12小时的暴风雨时显示出高准确度 (95%).
科学领域:
- 气象学和大气科学 气象学和大气科学
- 人工智能用于天气预报
背景情况:
- 严重的雷暴是极端天气事件,造成当地重大破坏.
- 准确的预测和足够的交付时间对于减轻风暴相关灾害至关重要.
- 现有的预测方法可以通过先进的计算技术来增强.
研究的目的:
- 引入和评估新的机器学习方法,天真贝叶斯和辐射基函数网络 (RBFN),用于严重雷暴预测.
- 将RBFN和Naïve Bayes的性能与多层感知器 (MLP) 和K-最近邻居 (KNN) 等传统方法进行比较.
- 通过使用特定的天气参数来评估严重风暴事件的预测准确性和预测时间.
主要方法:
- 机器学习算法的应用,包括天真贝叶斯,MLP,KNN和RBFN对气象数据的应用.
- 使用特定的天气参数,这些预测模型以前没有强调.
- 对所选算法的预测性能进行比较分析.
主要成果:
- 辐射基函数网络 (RBFN) 与天真贝叶斯,MLP和KNN相比表现优越.
- 对于严重的暴风雨,RBFN实现了95%的预测准确度,对于没有暴风雨的场景,预测准确度为94%.
- 开发的模型为预测提供了相当大的10-12小时的预测时间.
结论:
- 辐射基函数网络 (RBFN) 是用于准确预测严重雷暴的高效方法.
- 该研究强调了先进的机器学习技术在改善天气预报的交付时间和准确性方面的潜力.
- 来自印度加尔各答的研究结果表明,在类似的气象环境中更广泛的适用性.
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