Groundwater contaminant source identification based on an ensemble learning search framework associated with an auto xgboost surrogate. (January 2023)

• Record Type: Journal Article
• Authors: Pan, Zidong; Lu, Wenxi; Wang, Han; Bai, Yukun
• Is Part Of: Environmental modelling & software. Volume 159(2023)
• Publication Date: 2023-01
• Subjects: Auto xgboost -- Ensemble learning search -- Iterative ensemble smoother -- Particle filter -- Differential evolution -- Swarm evolution algorithm; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105588
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Groundwater contaminant source identification (GCSI) is commonly accompanied by search process which tweaks the unknown contaminant source information to match the simulation model outputs with the measurements. When solving identification task, search accuracy and time cost have always been challenges that must be tackled. In the present study, a novel ensemble learning search framework associated with auto extreme gradient boosting tree (xgboost) was proposed to solve GCSI. In particular, auto xgboost was employed to reduce the calculation burden caused by repeatedly running simulation model. To promote search efficiency, boosting strategy (BOS) was employed to sequentially concatenate iterative ensemble smoother, differential evolution particle filter (DEPF), and swarm evolution algorithm. The identification results indicated that: 1. Auto xgboost could substitute a numerical simulation model with desired accuracy and expeditious running speed. 2. BOS could achieve better search accuracy, but with the sacrifice of infinitesimal calculated time cost, when compared with bagging strategy. Highlights: Novel and easy-to-perform auto xgboost was proposed as surrogate of high-calculation-cost simulation model. Differential evolution was introduced to improve PF search capacity. Boosting strategy was used to integrate IES, DEPF, and SEA to promote the accuracy of source identification.