预测锡金喜马拉雅地区的冰川面积分数,印度:使用响应表面方法,随机森林和人工神经网络进行比较研究
Sweta Kumari1,2, Anirban Middey3,4
1Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
Environmental monitoring and assessment
|September 20, 2023
概括
气候变化影响东喜马拉雅地区的冰川地区. 这项研究使用机器学习模型预测了变化,发现随机森林 (RF) 对于监测这一重要资源最可靠.
科学领域:
- 冰川学的冰川学
- 气候科学 气候科学
- 机器学习应用 机器学习应用
背景情况:
- 高海拔的冰川对于水,粮食和能源安全至关重要.
- 气候变化对冰川的稳定性和区域构成重大威胁.
- 由于可访问性和恶劣的条件,对冰川的地面监测具有挑战性.
研究的目的:
- 调查环境参数和污染物对东喜马拉雅山脉冰川面积部分的影响.
- 用各种机器学习模型预测冰川面积分数.
- 确定冰川面积变化最可靠的预测模型.
主要方法:
- 利用了来自NASA的GES DISC档案中的数据.
- 采用随机森林 (RF),多层感知器 (MLP),辐射基函数网络 (RBFN) 和响应表面方法 (RSM) 的模型.
- 使用R2值和错误矩阵评估模型性能.
主要成果:
- 所有选定的输入参数都显示出高的对线性 (R2 > 0.9).
- MLP,RBFN和RF模型实现了高R2值 (分别为0.843,0.839,0.838).
- 射频模型展示了最小的误差矩阵,表明尽管R2略低于神经网络,但可靠性优越.
结论:
- 机器学习模型,特别是射频,对于预测冰川面积分数变化是非常有效的.
- 环境参数和污染物显著影响冰川区域.
- 建议对混合模型进行进一步的研究,以提高预测准确度.
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