使用长期短期记忆模型预测维拉里卡湖 (智利巴塔哥尼亚) 的表面温度
Lien Rodríguez-López1, David Bustos Usta2, Lisandra Bravo Alvarez3
1Facultad de Ingeniería, Universidad San Sebastián, Lientur 1457, Concepción, Chile. lien.rodriguez@uss.cl.
Scientific reports
|November 17, 2025
概括
维拉里卡湖 (Villarrica) 是一个湖泊.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 遥感 遥感 遥感 遥感
背景情况:
- 维拉里卡湖的水温趋势显示出变暖,特别是在春季和夏季.
- 长期的地表温度数据 (1986-2020年) 显示了每年的最大值和最小值.
- 内陆水质监测对于环境管理至关重要.
研究的目的:
- 分析维拉里卡湖的长期水温趋势.
- 开发和评估用于预测湖面温度的机器学习模型.
- 整合遥感和现场数据,以加强水源监测.
主要方法:
- 分析了34年的水温时间序列数据.
- 使用物理,化学,生物,气象和卫星光谱数据开发机器学习模型.
- 长短期记忆 (LSTM) 模型用于温度估计的应用.
主要成果:
- 在研究期间,维拉里卡湖的春季和夏季表面温度在增加.
- LSTM模型在估计湖面表面温度方面表现出高准确度.
- 湖泊的西部地区显示出最好的模型性能与低误差指标 (RMSE=2.79,MedAE=2.13).
结论:
- 机器学习,特别是LSTM,对于监测湖面温度是有效的.
- 遥感和机器学习的整合为内陆水资源管理提供了强大的方法.
- 持续监测和先进建模对于理解和管理水生生态系统至关重要.
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