基于深度学习的框架,用于增强参考蒸发透气估计:评估准确性和预测策略
Suman Saurabh Sarkar1, Jatin Bedi2, Sushma Jain2
1Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, India. ssarkar_phd20@thapar.edu.
Scientific reports
|April 30, 2025
概括
时间卷积网络 (TCN) 模型准确地预测每日参考蒸发透气 (ETo),表现优于LSTM和N-BEATS. 使用TCN的递归策略进一步提高了在数据稀缺环境中的灌规划.
科学领域:
- 农业工程 农业工程
- 数据科学数据科学数据科学
- 水文学的水文学
背景情况:
- 优化农业资源管理,包括作物产量和用水量,对于粮食安全至关重要.
- 准确估计参考蒸发透气 (ETo) 对于精确的灌至关重要,但传统方法往往昂贵且数据密集.
- 有限的气象数据对可靠的ETo预测和灌规划构成重大挑战.
研究的目的:
- 评估深度学习序列模型 (LSTM,N-BEATS,TCN) 的性能,以预测每日ETo.
- 为了比较标准与递归预测策略的有效性,使用TCN进行ETo估计.
- 确定ETo时间序列预测的有效方法,特别是在数据稀缺的农业环境中.
主要方法:
- 三种深度学习序列模型 - - 长期短期记忆 (LSTM),时间序列神经基础扩展分析 (N-BEATS) 和时间卷积网络 (TCN) - - 被评估为每日ETo预测.
- 确定为表现最佳的TCN模型进一步用于评估两个预测策略:使用历史数据的标准预测和涉及预测气候数据的递归方法.
- 模型性能通过使用诸如纳什-萨特克利夫效率 (NSE),Theil U2,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 等指标进行了定量评估.
主要成果:
- 时间卷积网络 (TCN) 模型在预测每日ETo方面表现优于LSTM和N-BEATS.
- 该TCN模型实现了高精度的NSE=0.99,Theil U2=0.005,RMSE=0.092,和MAE=0.048. 这样可以达到高精度.
- 使用TCN的递归预测策略比标准方法产生了更准确的ETo值,这对于数据稀缺的灌规划是有益的.
结论:
- 深度学习模型,特别是TCN,为预测ETo时间序列提供了一个高效和准确的替代方案.
- TCN模型的性能及其递归策略的有效性为农业中精确的水资源管理提供了宝贵的工具,特别是当气象数据有限时.
- 这项研究强调了先进的人工智能技术的潜力,通过更好的灌规划来提高农业效率和可持续性.
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