Litcius/Paper detail

Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data

Jin-Kyu Kang, Donmoon Lee, Kimberly Etombi Muambo, Jae‐Won Choi, Jeong‐Eun Oh

2023Water Research29 citationsDOI

Topics & Concepts

Molecular descriptorAutoencoderArtificial intelligenceFeature (linguistics)Process (computing)Computer scienceSupport vector machineWater treatmentEncoding (memory)Machine learningBiological systemChemistryArtificial neural networkEnvironmental scienceQuantitative structure–activity relationshipEnvironmental engineeringLinguisticsPhilosophyBiologyOperating systemPharmaceutical and Antibiotic Environmental ImpactsAdvanced Chemical Sensor TechnologiesWater Quality Monitoring and Analysis
Development of an embedded molecular structure-based model for prediction of micropollutant treatability in a drinking water treatment plant by machine learning from three years monitoring data | Litcius