Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods

• Main Authors: Rahmati, Omid, Choubin, Bahram, Fathabadi, Abolhasan, Coulon, Frederic, Soltani, Elinaz, Shahabi, Himan, Mollaefar, Eisa, Tiefenbacher, John, Cipullo, Sabrina, Ahmad, Baharin, Dieu, Tien Bui
• Format: Article
• Language: English
• Published: Elsevier B.V. 2019
• Subjects: NA Architecture; TH434-437 Quantity surveying
• Online Access: http://eprints.utm.my/id/eprint/89342/1/BaharinAhmad2019_PredictingUncertaintyofMachineLearningModels.pdf; http://eprints.utm.my/id/eprint/89342/; http://dx.doi.org/10.1016/j.scitotenv.2019.06.320

Although estimating the uncertainty of models used for modelling nitrate contamination of groundwater is essential in groundwater management, it has been generally ignored. This issue motivates this research to explore the predictive uncertainty of machine-learning (ML) models in this field of study...