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使用机器学习,深度学习和时间序列模型在西北喜马拉雅山脉的高度梯度上预测降雨量
Owais Ali Wani1, Syed Sheraz Mahdi2,3, Md Yeasin4
1Division of Agronomy, Faculty of Agriculture Wadoora, Sher-e-Kashmir University of Agricultural Sciences & Technology of Kashmir (SKUAST-K), Jammu and Kashmir, 193201, India.
Scientific reports
|November 13, 2024
概括
准确的降雨预测对印度至关重要.
科学领域:
- 气象学 天气学
- 数据科学数据科学数据科学
- 环境科学 环境科学
背景情况:
- 准确的降雨预测对印度至关重要,影响农业和防灾准备.
- 西北喜马拉雅山脉有限的气象数据需要改进的预报模型.
- 季风降雨至关重要,支持印度约60%的农田.
研究的目的:
- 使用先进的ML和DL算法,提高西北喜马拉雅山脉降雨预测的准确性.
- 为了比较各种ML,DL和时间序列模型的降雨预测性能.
- 调查海拔梯度对模型准确性的影响.
主要方法:
- 机器学习 (ML) 算法的应用:随机森林 (RF),支持向量回归 (SVR),人工神经网络 (ANN),k-最近邻居 (KNN).
- 深度学习 (DL) 算法的实现:长短期记忆 (LSTM),双向LSTM,深度LSTM,封闭循环单元 (GRU),简单循环神经网络 (RNN).
- 使用的时间序列技术:自回归集成移动平均 (ARIMA),三角形,Box-Cox转换,ARMA错误,趋势和季节组件 (TBATS).
- 在六个高度气象站中使用1980-2021年的气象数据评估模型性能.
主要成果:
- 深度学习 (DL) 方法在降雨预测方面表现出最高的准确性,其次是ML和时间序列技术.
- 双向LSTM和LSTM模型在DL算法中显示出优异的性能.
- 人工神经网络 (ANN) 是ML算法中最准确的.
- 模型的准确性受到高度的显著影响,这表明需要在山区提供更多数据.
结论:
- 先进的DL和ML算法在降雨预测准确度上比传统方法提供了显著的改进.
- 高度是影响西北部喜马拉雅山脉降雨预测模型精度的关键因素.
- 建议从各种海拔站进一步收集数据,以提高预测可靠性.
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