在中国黄河盆地使用复杂网络和GRNN-PSRLSTMTM进行相关性变化分析和NDVI预测
Ziyi Meng1, Yanling Lu2, Haixia Wang1
1School of Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China.
Environmental monitoring and assessment
|October 22, 2024
概括
温度和降水等环境因素影响植被健康 (NDVI). 一个新的GRNN-PSRLSTM模型准确地预测了每月的NDVI,帮助黄河流域的生态保护工作.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 规范差异植被指数 (NDVI) 对于监测植被健康至关重要,但受到复杂的环境相互作用的影响.
- 了解NDVI与温度,降水,土壤水分,阳光持续时间和PM2.5等环境因素之间的动态关系对于生态评估至关重要.
研究的目的:
- 通过复杂的网络分析,探索NDVI和关键环境因素之间的复杂,时间变化的相关性.
- 根据这些环境因素,开发和验证一个新的预测模型 (GRNN-PSRLSTM) 用于每月的NDVI预测.
主要方法:
- 构建一个相关性波动网络来分析NDVI和环境变量之间的相互作用.
- 应用阶段空间重建 (PSR) 结合一般化回归神经网络 (GRNN) 和长短期记忆 (LSTM) 网络 (GRNN-PSRLSTM模型).
主要成果:
- 在NDVI和温度,降水,土壤湿度,阳光持续时间和PM2.5之间确定了显著的相关性,相关性在3-6个月的时间内演变.
- 该GRNN-PSRLSTM模型在预测月度NDVI方面表现出很高的准确性,在九个省份实现了0.0232的平均RMSE和0.0564的MAPE.
结论:
- 该研究强调了环境因素和植被健康之间的动态相互作用,为植被变化检测提供了洞察力.
- 开发的GRNN-PSRLSTM模型为准确的月度NDVI预测提供了一个强大的工具,支持生态保护和管理策略,特别是在黄河流域.
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