Data Publication

Hybrid Machine Learning Models for Estimating Petroleum Hydrocarbon Concentration in Groundwater

Wu, Chen Lester | Wagterveld, R. Martijn | Rietveld, Luuk C. | van Breukelen, Boris M

4TU.ResearchData

(2026)

Descriptions

Jupyter notebook containing scripts for training and evaluating machine learning models including long short-term memory network and classical regression models for continuous monitoring of petroleum hydrocarbons in groundwater. The design of a hybrid Kalman filter was also included, as well as the Jupyter notebooks for visualizing the results.

Keywords

MSL enriched keywords
civil engineered setting
MSL vocabulary keywords corresponding to originally assigned keywords
civil engineered setting
Originally assigned keywords
Geochemistry
FOS: Earth and related environmental sciences
Civil Engineering
FOS: Civil engineering
Artificial Intelligence and Image Processing
FOS: Computer and information sciences
Information Systems
Chemical Engineering
FOS: Chemical engineering
Earth Sciences
Engineering
Information and Computing Sciences
Sensor fusion
Groundwater pollution
Kalman filter, Data-driven modeling
Contaminant plume monitoring
BTEX
Real-time monitoring

Metadata


Resource Type

Dataset


Source

https://data.4tu.nl/datasets/0a23147e-ba85-4ba2-a058-ba199c65d711/2

Source publisher

4TU.ResearchData

DOI

10.4121/0a23147e-ba85-4ba2-a058-ba199c65d711.v2

Creators

Wu, Chen Lester
Personal
000-0002-6706-8240
Wagterveld, R. Martijn
Rietveld, Luuk C.
van Breukelen, Boris M
Personal
0000-0001-9642-1077

Contributors

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management, Sanitary Engineering
Organizational
Wetsus, European Centre of Excellence for Sustainable Water Technology
Organizational

Citation

Wu, C. L., Wagterveld, R. M., Rietveld, L. C., & van Breukelen, B. M. (2026). Hybrid Machine Learning Models for Estimating Petroleum Hydrocarbon Concentration in Groundwater (Version 2) [Dataset]. 4TU.ResearchData. https://doi.org/10.4121/0A23147E-BA85-4BA2-A058-BA199C65D711.V2


Dates

Issued 2026-04-16

Language

en



Rights

Name Creative Commons Attribution 4.0 International
URI https://creativecommons.org/licenses/by/4.0/legalcode
Identifier cc-by-4.0
Identifier Scheme SPDX
Scheme URI https://spdx.org/licenses/

Locations

- no geo-locations found -