提高抗旱能力:基于机器学习的脆弱性评估在印度北方邦
Barnali Kundu1, Narendra Kumar Rana1, Sonali Kundu2
1Department of Geography, Institute of Science, Banaras Hindu University, Varanasi, Uttar Pradesh, India, 221005.
概括
机器学习模型确定了印度北方邦东部地区的高干旱脆弱性. 这项研究有助于为该地区制定有效的干旱性战略.
科学领域:
- 环境科学 环境科学
- 气候学 气候学 气候学
- 数据科学数据科学数据科学
背景情况:
- 干旱是一个复杂的气候危险,具有重大自然和社会影响.
- 评估干旱脆弱性对于有效减少和管理灾害风险至关重要.
研究的目的:
- 应用机器学习算法 (MLA) 来评估印度北方邦的干旱脆弱性 (DVM).
- 通过使用一组全面的物理和气象因素来确定极易干旱的地区.
主要方法:
- 利用了18个因素,分为物理和气象干旱指标.
- 使用人工神经网络 (ANN) 来进行DVM评估.
- 使用接收器操作特征曲线 (ROC) 分析验证模型性能.
主要成果:
- 确定了Uttar Pradesh 31.38%的地区,特别是东部地区,极易受到干旱的影响.
- 人工神经网络模型表现出强的性能,曲线下的面积 (AUC) 值为0.843.
- 该研究提供了一种基于数据的方法,用于绘制干旱易受影响的地区的地图.
结论:
- 基于机器学习的干旱脆弱性地图是为政策决策提供信息的宝贵工具.
- 调查结果突显了针对北方邦脆弱地区的干旱缓解和适应战略的迫切需要.
- 未来的研究应该专注于改进MLA模型和整合社会经济数据,以提高抗旱能力.
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