Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate-Reference-Cited by-同舟云学术

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Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate

Published:2024-01 Issue:1 Volume:15 Page:101732
ISSN:1674-9871
Container-title:Geoscience Frontiers
language:en
Short-container-title:Geoscience Frontiers

Author:

Xu Yaning,Lu Wenxi,Pan Zidong,Luo Chengming,Bai Yukun,Qiu Shuwei

Funder

National Natural Science Foundation of China

Program for Jilin University Science and Technology Innovative Research Team

Publisher

Elsevier BV

Subject

General Earth and Planetary Sciences

Reference46 articles.

1. An integrated Bayesian and machine learning approach application to identification of groundwater contamination source parameters;An;Water,2022

2. Advances in surrogate based modeling, feasibility analysis, and optimization: A review;Bhosekar;Comput. Chem. Eng.,2018

3. Random forests;Breiman;Mach. Learn.,2001

4. Estimating the performance of random forest versus multiple regression for predicting prices of the apartments;Ceh;ISPRS Int. J. Geo Inf.,2018

5. Chen, T., Guestrin, C., 2016. XGBoost: A Scalable Tree Boosting System, in: Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California, CA, 785-794.

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