Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater-Reference-Cited by-同舟云学术

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Interpretable machine learning for predicting the fate and transport of pentachlorophenol in groundwater

Published:2024-03 Issue: Volume:345 Page:123449
ISSN:0269-7491
Container-title:Environmental Pollution
language:en
Short-container-title:Environmental Pollution

Author:

Rad Mehran^ORCID,Abtahi Azra,Berndtsson Ronny^ORCID,McKnight Ursula S.^ORCID,Aminifar Amir

Publisher

Elsevier BV

Reference44 articles.

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

2. XGBoost: a scalable tree boosting system;Chen,2016

3. Groundwater contaminant transport modeling using MODFLOW and MT3DMS: a case study in Rajshahi City;Chowdhury;Water Pract. Technol.,2023

4. Attenuation and biodegradation of chlorophenols in ground water at a former wood treating facility;Davis;Ground Water,1994

5. Comparative study of machine learning models for evaluating groundwater vulnerability to nitrate contamination;Elzain;Ecotoxicol. Environ. Saf.,2022

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1. Spatial distribution and hydrogeochemical processes of high iodine groundwater in the Hetao Basin, China;Science of The Total Environment;2024-11

2. Exploring spatiotemporal patterns of algal cell density in lake Dianchi with explainable machine learning;Environmental Pollution;2024-09

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