使用随机变量选择和高斯素朴贝叶斯分类器对短期洪水事件的分类:孟加拉国西拉吉甘吉区的案例研究
Chandan Mondal1,2, Md Jahir Uddin1
1Department of Civil Engineering, Khulna University of Engineering & Technology, Khulna, 9203, Bangladesh.
Heliyon
|February 3, 2025
概括
准确的洪水预报对于拯救生命和财产至关重要. 这项研究使用了高斯素朴贝叶斯 (GNB) 分类器,并确定了表面短波向下辐射作为有效预测洪水的关键预测因子.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水文学的水文学
背景情况:
- 天然洪水事件导致全球严重的死亡和经济损失.
- 准确的洪水预报对于减轻这些灾害的影响至关重要.
- 复杂的洪水预测模型往往阻碍了解释性和实际应用.
研究的目的:
- 开发和评估一个简化的洪水预测模型.
- 使用相互信息 (MI) 识别洪水分类的最佳预测变量.
- 评估高斯素朴贝叶斯 (GNB) 分类器对于洪水预测的适用性.
主要方法:
- 使用高斯素朴贝叶斯 (GNB) 分类器进行洪水/非洪水年分类.
- 使用相互信息 (MI) 技术从19个随机变量中选择预测变量.
- 确定了"全天表面短波向下辐射"作为最敏感的预测变量.
主要成果:
- 国民总收入模型成功地将年份分类为被洪水淹没或未被洪水淹没.
- 全天表面短波向下辐射被确定为最佳预测因素.
- 四次交叉验证产生了25%的平均错误率,表明尽管数据有限,模型的适用性.
结论:
- 高斯纳夫贝叶斯 (GNB) 分类模型对洪水预测具有前景.
- 模型的准确性受到培训数据的数量的影响;对于GNB,建议使用更大的数据集.
- 这项研究为Sirajganj区的水文项目提供了基础.
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