Application of long short-term memory neural network technique for predicting monthly pan evaporation.
Application of long short-term memory neural network technique for predicting monthly pan evaporation.
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DOI：
10.1038/s41598-021-99999-y
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发表时间：
2021-10-20
影响因子：
4.6
通讯作者：
Huang YF
中科院分区：
文献类型：
--
作者：
Abed M;Imteaz MA;Ahmed AN;Huang YF
蒸发是水资源管理、水文建模和灌溉系统设计的关键要素。通过部署极端梯度提升、ElasticNet 线性回归和长短期记忆等三种机器学习 (ML) 模型来预测每月蒸发量 (Ep)；以及两种经验技术，即 Stephens-Stewart 和 Thornthwaite。本研究的目的是开发一个可靠的广义模型来预测整个马来西亚的蒸发量。在此背景下，利用马来西亚两个气象站的月度气象统计数据，根据2000年至2019年期间最高气温、平均气温、最低气温、风速、相对湿度和太阳辐射等气候方面的情况来训练和测试模型。对于每种方法，都通过利用输入参数和其他模型因素的各种组合来制定多个模型。通过使用标准统计措施来评估模型的性能。结果表明，这三种机器学习模型的表现优于经验模型，即使使用相同的输入组合，也可以显着提高每月 Ep 估计的精度。此外，性能评估表明，长短期记忆神经网络 (LSTM) 为两个站点的所有研究模型提供了最精确的每月 Ep 估计。亚罗士打和(R2 = 0.986, MAE = 0.058, MSE = 0.005, RMSE = 0.074, RAE = 0.120, RSE = 0.013) 哥打巴鲁。
Evaporation is a key element for water resource management, hydrological modelling, and irrigation system designing. Monthly evaporation (Ep) was projected by deploying three machine learning (ML) models included Extreme Gradient Boosting, ElasticNet Linear Regression, and Long Short-Term Memory; and two empirical techniques namely Stephens-Stewart and Thornthwaite. The aim of this study is to develop a reliable generalised model to predict evaporation throughout Malaysia. In this context, monthly meteorological statistics from two weather stations in Malaysia were utilised for training and testing the models on the basis of climatic aspects such as maximum temperature, mean temperature, minimum temperature, wind speed, relative humidity, and solar radiation for the period of 2000–2019. For every approach, multiple models were formulated by utilising various combinations of input parameters and other model factors. The performance of models was assessed by utilising standard statistical measures. The outcomes indicated that the three machine learning models formulated outclassed empirical models and could considerably enhance the precision of monthly Ep estimate even with the same combinations of inputs. In addition, the performance assessment showed that Long Short-Term Memory Neural Network (LSTM) offered the most precise monthly Ep estimations from all the studied models for both stations. The LSTM-10 model performance measures were (R2 = 0.970, MAE = 0.135, MSE = 0.027, RMSE = 0.166, RAE = 0.173, RSE = 0.029) for Alor Setar and (R2 = 0.986, MAE = 0.058, MSE = 0.005, RMSE = 0.074, RAE = 0.120, RSE = 0.013) for Kota Bharu.
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