发表机构
Lodz Universtiy of Technlogy; MicroComputer Systems Laboratory; University of Ioannina（罗兹工业大学; 微计算机系统实验室; 约阿尼纳大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究对深度学习架构进行比较评估，用于农业气象参数多变量预测，利用希腊约阿尼纳的观测数据，对比多种模型在24小时次日和168小时提前一周预测任务中的表现，发现混合模型在短期预测中更优且参数更少。
AI 中文摘要
准确的气象预报对农业规划、灌溉管理和环境决策支持至关重要。本研究对循环和混合深度学习架构进行比较评估，用于参考蒸散量（$ET_0$）、水汽压差（VPD）、风速以及风向的正弦和余弦分量的多变量预测。分析利用来自希腊约阿尼纳2011年1月至2026年4月的134376个每小时观测数据，通过OpenMeteo历史天气API从ERA5获取。将单层和多层GRU和LSTM网络与混合1D - CNN - GRU和1D - CNN - LSTM模型用于24小时次日预测和168小时提前一周预测这两个任务。使用归一化均方根误差、决定系数和复合加权商数得分（WQS）评估性能。最有效的纯循环模型是24小时预测的64单元LSTM（WQS为0.816755）和168小时预测的1024单元GRU（WQS为0.779465）。混合CNN - GRU模型在24小时和168小时预测中分别获得最高总分0.827535和0.782863，但单元数比LSTM模型多。CNN - LSTM模型以少得多的参数产生几乎相同的结果。与相应的循环基线相比，混合模型在24小时时将WQS提高了1.22 - 1.63%，在168小时时提高了0.44 - 0.45%，表明卷积特征提取对短期预测更有益。
英文摘要
Accurate meteorological forecasting is essential for agricultural planning, irrigation management, and environmental decision support. This study conducts a comparative evaluation of recurrent and hybrid deep learning architectures for multivariate forecasting of reference evapotranspiration ($ET_0$), vapour pressure deficit (VPD), wind speed, and the sine and cosine components of wind direction. The analysis utilizes 134,376 hourly observations from Ioannina, Greece, spanning January 2011 to April 2026, sourced from ERA5 via the OpenMeteo Historical Weather API. Single and multi-layer GRU and LSTM networks are compared with hybrid 1D-CNN-GRU and 1D-CNN-LSTM models for two forecasting tasks: a 24-hour next-day forecast and a 168-hour week-ahead forecast. Performance is evaluated using normalized root mean squared error, the coefficient of determination, and a composite Weighted Quotient Score (WQS). The most effective purely recurrent models are a 64-unit LSTM for the 24-hour horizon, with a WQS of 0.816755, and a 1024-unit GRU for the 168-hour horizon, with a WQS of 0.779465. The hybrid CNN-GRU models achieved the highest overall scores of 0.827535 and 0.782863 for the 24-hour and 168-hour horizons, but with additionally more number of units respectively to LSTM models, while the CNN-LSTM models yield nearly identical results with substantially fewer parameters. Compared to the corresponding recurrent baselines, the hybrid models improve WQS by 1.22--1.63\% at 24 hours and by 0.44--0.45\% at 168 hours, indicating that convolutional feature extraction is more beneficial for short-term forecasting.
Comments13 pages, 5 figures
