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通过在半岛马来西亚使用各种机器学习模型进行流程流程分类.
Nouar AlDahoul1, Mhd Adel Momo2, K L Chong3
1Computer Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates.
Scientific reports
|September 4, 2023
概括
在马来西亚,准确的流量预测对于减少洪水和干旱至关重要. 机器学习模型,特别是长短期记忆 (LSTM),在预测流量类别方面表现出卓越的表现,优于传统方法.
科学领域:
- 水文学的水文学
- 环境科学 环境科学
- 机器学习 机器学习
背景情况:
- 半岛马来西亚面临严重的洪水和干旱风险,原因是极端的河流流.
- 准确的流量预测对于减轻环境和市政损害至关重要.
- 由于固有的不确定性,预测连续流量值存在挑战.
研究的目的:
- 以时间序列分类问题来制定流量预测.
- 为了更好地管理不确定性,将流量分类为离散类别 (5或10类).
- 评估机器学习模型,用于马来西亚河流的流量流量类别预测.
主要方法:
- 使用时间序列分类方法.
- 使用了机器学习模型,包括长短期内存 (LSTM),支持矢量机 (SVM) 和梯度增强 (GB).
- 研究了SVM和GB模型的集体堆叠.
主要成果:
- 与SVM和GB相比,LSTM模型在预测流量类别方面表现优异,比SVM和GB提前2-3天.
- 在马来西亚的各种河流上,LSTM获得了更高的F1分数,这表明预测准确度有所提高.
- SVM和GB型号的组合堆叠也产生了高性能,佩拉克河的F1得分显著改善.
结论:
- 流量流量类别预测提供了一个有利的方法来管理预测中的不确定性.
- LSTM是短期流量类别预测的高效模型.
- 合并方法为提高流量预测准确性提供了强大的替代方案.
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