ABSTRACT

Estimation of pan evaporation (Epan) is an important issue for planning and management of available water resources. In the present study, the accuracy of a new deep learning method, long short-term memory (LSTM) with grey wolf optimization (GWO), in modelling Epan using limited climatic variables as input is investigated. The outcomes of the LSTM-GWO are compared with the single LSTM and advanced machine learning (ML) methods. Minimum and maximum temperatures and extra-terrestrial radiation are used as inputs to the models. Three data splitting scenarios are considered and the outcomes of the abovementioned methods are also compared with the Stephen-Stewart (SS) and calibrated Hargreaves-Samani (CHS) empirical methods. The results reveal that the LSTM-GWO method has a better ability in estimating Epan using limited inputs compared to other ML and empirical methods. They also indicate that an increase in the amount of training data used improves the accuracy of the models.

摘要

泛蒸发量 (Epan) 的估算是规划和管理可用水资源的一个重要问题。在本研究中，研究了一种新的深度学习方法，即具有灰狼优化 (GWO) 的长短期记忆 (LSTM)，在使用有限的气候变量作为输入对 Epan 进行建模时的准确性。LSTM-GWO 的结果与单个 LSTM 和高级机器学习 (ML) 方法进行了比较。最低和最高温度以及地外辐射被用作模型的输入。考虑了三种数据拆分方案，并将上述方法的结果与 Stephen-Stewart (SS) 和校准的 Hargreaves-Samani (CHS) 经验方法进行了比较。结果表明，与其他 ML 和经验方法相比，LSTM-GWO 方法在使用有限输入估计 Epan 方面具有更好的能力。他们还表明，使用的训练数据量的增加提高了模型的准确性。