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长期短期记忆模型用于量化阿拉巴马州溪流排放和地下水深度的长期演变
Hossein Gholizadeh1, Yong Zhang1, Jonathan Frame2
1Department of Geological Sciences, University of Alabama, Tuscaloosa, AL 35487, USA.
The Science of the total environment
|July 30, 2023
概括
长短期记忆 (LSTM) 模型有效地预测了阿拉巴马州的地表和地下水位. 这项研究通过使用先进的人工智能量化时空水谱演变来推进水资源管理.
科学领域:
- 水文学的水文学
- 人工智能的人工智能
- 水资源管理 水资源管理
背景情况:
- 阿拉巴马州的水资源可持续性在空间时间尺度上并未得到充分量化.
- 长短期记忆 (LSTM) 模型在水文建模中显示了效率.
研究的目的:
- 应用LSTM模型来量化阿拉巴马州表面和地下水文图的时空演变.
- 评估LSTM对溪流流和地下水深度的预测能力.
- 在不同的降水场景下探索未来的地下水演变.
主要方法:
- 开发了一个表面水LSTM模型,使用动态 (天气) 和静态 (盆地特征) 输入进行流量预测.
- 提出了一个地下水LSTM (GW-LSTM) 模型,将水文地质和气象数据用于地下水深度预测.
- 在阿拉巴马州利用了19个测量盆地进行溪流流和21个井进行地下水深度数据.
主要成果:
- 表面水LSTM模型准确地预测了流量,排水密度提高了极端事件预测.
- 该GW-LSTM模型有效地预测了每日地下水深度趋势和极高水平.
- 该模型在预测保留地点的地下水深度方面表现出强的表现.
结论:
- LSTM模型是量化地表水和地下水的时空动态的有效工具.
- 这项研究增强了对阿拉巴马州综合水资源管理的理解.
- 这些发现支持LSTM在水文预测和水资源可持续性评估中的更广泛应用.
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