自我注意 (SA) 时间卷积网络 (SATCN) 长期短期记忆神经网络 (SATCN-LSTM):用于预测地下水位的高级 Python 代码
Mohammad Ehteram1, Elham Ghanbari-Adivi2
1Department of Water Engineering, Semnan University, Semnan, Iran.
概括
一个新的自我注意时间卷积网络-长期短期记忆神经网络 (SATCN-LSTM) 模型提高了地下水位预测的准确性. 这种先进的模型提供了更好的水资源管理决策和可持续的资源使用.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 人工智能的人工智能
背景情况:
- 有效的水资源管理依赖于准确的地下水位预测.
- 像LSTM这样的现有模型在捕捉复杂的时间依赖性方面存在局限性.
研究的目的:
- 引入和评估一种新的SATCN-LSTM模型,用于改进地下水位预测.
- 提高地下水预测的准确性和可靠性,以更好地管理水资源.
主要方法:
- 开发了一种混合SATCN-LSTM模型,将时间卷积网络 (TCN) 与长短期内存 (LSTM) 集成.
- 在TCN组件中使用自我注意机制和跳过连接,以解决消失梯度和识别相关数据.
- 利用气象数据作为预测地下水位 (GWL) 的输入.
主要成果:
- 该SATCN-LSTM模型实现了最低的0.09的平均绝对误差 (MAE) 和0.14的根平均平方误差 (RMSE).
- 其表现优于其他模型,包括SATCN (MAE: 0.12,RMSE: 0.15),SALSTM (MAE: 0.16),TCN-LSTM (MAE: 0.17),TCN (MAE: 0.22),以及LSTM (MAE: 0.23).
结论:
- 在地下水位预测方面,SATCN-LSTM模型表现出卓越的性能和稳定性.
- 预测准确度的提高有助于为水分配,抽取和干旱准备做出明智的决策.
- 该模型有助于可持续和有效地管理地下水资源.
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