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机器学习模型的开发,以准确多个月的干旱预测:北美大湖地区的案例研究
Mohammed Majeed Hameed1, Siti Fatin Mohd Razali1,2, Wan Hanna Melini Wan Mohtar1,2
1Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi, Selangor, Malaysia.
PloS one
|October 31, 2023
概括
准确的干旱预测对于大湖地区至关重要. 一个新的模型,RELM-BWO,显著改善了多个月干旱预测,有助于水资源管理和气候变化适应.
科学领域:
- 水文和气候科学 水文和气候科学
- 水资源管理 水资源管理
- 机器学习应用 机器学习应用
背景情况:
- 大湖是重要的淡水资源,受到气候变化引起的干旱威胁.
- 干旱造成的经济和社会影响需要改善预测,以实现有效的水资源管理.
- 准确的多个月干旱预测对于减轻这些后果至关重要.
研究的目的:
- 为干旱评估引入多变量标准化湖水位指数 (MSWI).
- 开发和评估混合机器学习模型,用于大湖地区多个月的干旱预测.
- 为了确定最准确的模型可靠的干旱预测在所有五大湖.
主要方法:
- 利用历史湖泊水位数据 (1920-2020年) 来开发MSWI.
- 开发了四种混合型号:SVR-BWO,RF-BWO,ELM-BWO和RELM-BWO,其中包括贝卢加的优化.
- 应用蒙特卡洛模拟来评估预测的不确定性和可靠性.
主要成果:
- 贝卢加的优化显著提高了所有测试的经典模型的准确性.
- 与贝卢加优化 (RELM-BWO) 模型相比,规范化极端学习机器表现出优异的准确性 (7.21-76.74%) 与其他模型相比.
- RELM-BWO模型为所有大湖提供了可靠的干旱预报,预期时间为2-6个月.
结论:
- RELM-BWO模型为大湖地区的多个月干旱预测提供了可靠和准确的解决方案.
- 调查结果为决策者和水资源管理者提供了关键数据,帮助他们制定有效的干旱减缓战略.
- 该研究强调了混合机器学习模型在应对气候变化对水资源的影响方面的潜力.
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