Department of Civil Engineering, Leading University, Sylhet, Bangladesh.
Department of Surveying and Built Environment, University of Southern Queensland, Toowoomba, Australia.
Environ Sci Pollut Res Int. 2024 Oct;31(50):60041-60064. doi: 10.1007/s11356-024-35182-w. Epub 2024 Oct 4.
Evapotranspiration is one of the crucial elements in water balance equations and plays a pivotal role in the water and energy cycle of an area. An accurate and precise estimation and prediction of reference evapotranspiration (ETo) is necessary for regional management of water resources and irrigation scheduling. The challenge of predicting daily evapotranspiration with limited meteorological data in Bangladesh. This study aims to predict daily evapotranspiration using limited meteorological data of Bangladesh by three deep learning (CNN, GRU, LSTM) and one hybrid (CNN-GRU) model. The novel method of hybrid CNN-GRU models, which have not been commonly used for this purpose. The performance of models was evaluated by five accuracy matrices R, RMSE, MAE, MAPE, and CE and comparison is visualized by radar graphs. The study's novelty lies in the use of hybrid CNN-GRU models to estimate reference evapotranspiration, as this algorithm has not been commonly used for this purpose. In the case of the Rangpur station, the hybrid CNN-GRU algorithm outperformed other models, achieving the best values across various statistical metrics during both the training and testing phases. The highest correlation coefficient values of approximately 0.994 and 0.995. Moreover, during training and testing stages, the hybrid model had the lowest MAE (0.076, 0.068) and RMSE (0.138, 0.106) at the Rangpur station. Additionally, in the Sreemangal station, it can be notable that the statistical parameter RSME found superior results in the hybrid model around 0.225 and 0.174, respectively. In addition, the highest R and CE values were noted as 0.986, 0.987 and 0.985, 0.986 during the training and testing phases, respectively. The comparison suggests that the hybrid model will be best suited for prediction with the limited meteorological data. The outcome of the present research signifies the ability of deep learning methods in the prediction of evapotranspiration and the dominant variables affecting the changes the in context of Bangladesh.
蒸散是水量平衡方程中的关键要素之一，在区域水和能量循环中起着关键作用。准确和精确地估计和预测参考蒸散量（ETo）对于水资源的区域管理和灌溉调度是必要的。在孟加拉国，由于有限的气象数据，预测日蒸散量是一项挑战。本研究旨在通过三种深度学习（CNN、GRU、LSTM）和一种混合模型（CNN-GRU），使用孟加拉国有限的气象数据来预测日蒸散量。该研究使用混合 CNN-GRU 模型的新方法，这种方法尚未被广泛用于这一目的。通过 R、RMSE、MAE、MAPE 和 CE 五个精度矩阵来评估模型的性能，并通过雷达图进行可视化比较。该研究的新颖之处在于使用混合 CNN-GRU 模型来估计参考蒸散量，因为这种算法尚未被广泛用于这一目的。在兰普尔站的情况下，混合 CNN-GRU 算法优于其他模型，在训练和测试阶段的各种统计指标中都取得了最佳值。相关系数的最高值约为 0.994 和 0.995。此外，在训练和测试阶段，混合模型在兰普尔站的 MAE（0.076、0.068）和 RMSE（0.138、0.106）值最低。此外，在 Sreemangal 站，可以注意到，在混合模型中，RSME 统计参数在大约 0.225 和 0.174 时分别找到了更好的结果。此外，在训练和测试阶段，最高的 R 和 CE 值分别为 0.986、0.987 和 0.985、0.986。比较表明，混合模型最适合于有限气象数据的预测。本研究的结果表明，深度学习方法在蒸散量预测以及孟加拉国相关背景下影响变化的主导变量方面具有很强的能力。
