Prediction of Dichloroethene Concentration in the Groundwater of a Contaminated Site Using XGBoost and LSTM

Chlorinated aliphatic hydrocarbons (CAHs) are widely used in agriculture and industries and have become one of the most common groundwater contaminations. With the excellent performance of the deep learning method in predicting, LSTM and XGBoost were used to forecast dichloroethene (DCE) concentrati...

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Bibliographic Details
Published in:International journal of environmental research and public health 2022-07, Vol.19 (15), p.9374
Main Authors: Xia, Feiyang, Jiang, Dengdeng, Kong, Lingya, Zhou, Yan, Wei, Jing, Ding, Da, Chen, Yun, Wang, Guoqing, Deng, Shaopo
Format: Article
Language:English
Subjects:
Aliphatic hydrocarbons
Biodegradation
Chlorinated hydrocarbons
Chromatography
Contamination
Deep learning
Flow velocity
Groundwater
Groundwater pollution
Hydrocarbons
Long short-term memory
Mass spectrometry
Model accuracy
Natural attenuation
Pesticides
Pollutants
Pollution
Scientific imaging
Vinyl chloride
VOCs
Volatile organic compounds
Water quality
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Summary:Chlorinated aliphatic hydrocarbons (CAHs) are widely used in agriculture and industries and have become one of the most common groundwater contaminations. With the excellent performance of the deep learning method in predicting, LSTM and XGBoost were used to forecast dichloroethene (DCE) concentrations in a pesticide-contaminated site undergoing natural attenuation. The input variables included BTEX, vinyl chloride (VC), and five water quality indicators. In this study, the predictive performances of long short-term memory (LSTM) and extreme gradient boosting (XGBoost) were compared, and the influences of variables on models’ performances were evaluated. The results indicated XGBoost was more likely to capture DCE variation and was robust in high values, while the LSTM model presented better accuracy for all wells. The well with higher DCE concentrations would lower the model’s accuracy, and its influence was more evident in XGBoost than LSTM. The explanation of the SHapley Additive exPlanations (SHAP) value of each variable indicated high consistency with the rules of biodegradation in the real environment. LSTM and XGBoost could predict DCE concentrations through only using water quality variables, and LSTM performed better than XGBoost.
ISSN:1660-4601
1661-7827
1660-4601
DOI:10.3390/ijerph19159374