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检查优化机器学习模型用于准确的多个月干旱预测:美国的一个代表性案例研究

Mohammed Majeed Hameed1,2, Siti Fatin Mohd Razali3,4, Wan Hanna Melini Wan Mohtar3,4

  • 1Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia. mohmmag1@gmail.com.

Environmental science and pollution research international
|August 12, 2024
PubMed
概括

气候变化导致科罗拉多河干旱. 一种使用贝卢加优化 (BWO) 与调节极端学习机器 (RELM) 的新方法可以提前四个月提高干旱预报的准确性.

关键词:
全球多标准决策分析分析水文干旱 水文干旱多变量标准化流量指数.规范化的极端学习机器.预警系统 预警系统

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