Application of long short-term memory neural network technique for predicting monthly pan evaporation

• Published in: Scientific reports Vol. 11; no. 1; p. 20742
• Main Authors: Abed, Mustafa, Imteaz, Monzur Alam, Ahmed, Ali Najah, Huang, Yuk Feng
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20-10-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 704/242; Evaporation; Humanities and Social Sciences; Irrigation systems; Learning algorithms; Long short-term memory; Machine learning; multidisciplinary; Neural networks; Pan evaporation; Performance assessment; Relative humidity; Resource management; Science; Science (multidisciplinary); Solar radiation; Statistical analysis; Water resources management; Wind speed; Malaysia
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-99999-y

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 (R
2
 = 0.970, MAE = 0.135, MSE = 0.027, RMSE = 0.166, RAE = 0.173, RSE = 0.029) for Alor Setar and (R
2
 = 0.986, MAE = 0.058, MSE = 0.005, RMSE = 0.074, RAE = 0.120, RSE = 0.013) for Kota Bharu.