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

Simple item page
dc.citedby11
dc.contributor.authorAbed M.en_US
dc.contributor.authorImteaz M.A.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorHuang Y.F.en_US
dc.contributor.authorid36612762700en_US
dc.contributor.authorid6506146119en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55807263900en_US
dc.date.accessioned2023-05-29T09:05:22Z
dc.date.available2023-05-29T09:05:22Z
dc.date.issued2021
dc.descriptionarticle; evaporation; long short term memory network; Malaysia; relative humidity; solar radiation; weather; wind speeden_US
dc.description.abstractEvaporation 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. � 2021, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo20742
dc.identifier.doi10.1038/s41598-021-99999-y
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85117701192
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85117701192&doi=10.1038%2fs41598-021-99999-y&partnerID=40&md5=c7e05736631203e9b0ab7272541cfafc
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25875
dc.identifier.volume11
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleApplication of long short-term memory neural network technique for predicting monthly pan evaporationen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS