机器学习模型用于预测Mahanadi河流盆地的植被条件
Deepak Kumar Raj1, T Gopikrishnan2
1Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India. dkraj.iitbhu2018@gmail.com.
Environmental monitoring and assessment
|November 2, 2023
概括
随机森林模型最好地使用气候数据预测植被健康 (NDVI). 降水对NDVI产生积极影响,而降水和陆地表面温度 (LST) 都与NDVI产生负相关.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 气候变化研究 气候变化研究
背景情况:
- 河流盆地植被受到降水和土地表面温度 (LST) 等气候因素的影响.
- 用规范差异植被指数 (NDVI) 衡量植被健康状况的预测对于环境管理至关重要.
研究的目的:
- 为了确定最佳的机器学习模型,用LST和降水来预测NDVI.
- 为了确定NDVI,LST和大河流域降水之间的相关性.
主要方法:
- 通过谷歌地球引擎 (GEE) 使用LST和NDVI的CHRS和MODIS产品的月度降水数据.
- 评估了四种机器学习模型:线性回归 (LR),随机森林 (RF),支持向量回归 (SVR) 和K-最近邻居 (KNN).
- 使用R2,RMSE,MSE,MAE和解释变异得分 (EVS) 评估模型性能.
主要成果:
- 随机森林 (RF) 模型在训练和测试数据集中显示了最高的R2值.
- K-最近邻近 (KNN) 模型在测试集中实现了最低的根平均平方误差 (RMSE).
- 确定了降水和NDVI之间的正相关性,以及降水和LST,NDVI和LST之间的负相关性.
结论:
- 随机森林模型是Mahanadi流域NDVI预测中最有效的.
- 结果提供了关于气候植物动态的见解,并有助于流域管理和气候变化影响评估.
关键词:
在GEEEE中,GEEEE是指GEEEE.在 KNN KNN 标签上.在这里,我们可以看到LRLRLRLR.时间 LST LST莫迪斯 (MODIS) 是一种模式.玛哈纳迪河流域的盆地NDVI NDVI 在线阅读降水量 降水量 降水量这就是为什么RF是RF,RF是RF这是一个SVRSVR.更多相关视频
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