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A platform for research: civil engineering, architecture and urbanism
Uncertainty Quantification of Contaminated Soil Volume with Deep Neural Networks and Predictive Models
The estimation of the soil volume exceeding a contamination threshold over decommissioned industrial sites is critical for the design of remediation strategies. In practice, the volume calculation is mostly based on preliminary sampling surveys and the use of interpolation methods. However, if the volume is not estimated correctly, this can lead to environmental and economic risks. Geostatistical-oriented methodologies have been developed to better assess the volume using uncertainty ranges. In our study, we propose a methodology entitled “Evol” to better estimate the volume and reduce the uncertainty ranges with a combination of classic non-parametrical interpolation techniques and deep learning. Evol consists of generating a synthetic model from a real polluted site, extracting descriptive variables (features) from multiple sample sets, and evaluating the error in the volume calculation. A Deep Neural Network model is then trained with the features to estimate the volume and uncertainty range for any sample set. Our methodology demonstrated high accuracy in error estimation, as evidenced by a low RMSE of 0.008 across most sample sets. Additionally, the confidence volume intervals produced by our approach were narrower than those generated by classic techniques, resulting in interval size reductions of up to 89%.
Uncertainty Quantification of Contaminated Soil Volume with Deep Neural Networks and Predictive Models
The estimation of the soil volume exceeding a contamination threshold over decommissioned industrial sites is critical for the design of remediation strategies. In practice, the volume calculation is mostly based on preliminary sampling surveys and the use of interpolation methods. However, if the volume is not estimated correctly, this can lead to environmental and economic risks. Geostatistical-oriented methodologies have been developed to better assess the volume using uncertainty ranges. In our study, we propose a methodology entitled “Evol” to better estimate the volume and reduce the uncertainty ranges with a combination of classic non-parametrical interpolation techniques and deep learning. Evol consists of generating a synthetic model from a real polluted site, extracting descriptive variables (features) from multiple sample sets, and evaluating the error in the volume calculation. A Deep Neural Network model is then trained with the features to estimate the volume and uncertainty range for any sample set. Our methodology demonstrated high accuracy in error estimation, as evidenced by a low RMSE of 0.008 across most sample sets. Additionally, the confidence volume intervals produced by our approach were narrower than those generated by classic techniques, resulting in interval size reductions of up to 89%.
Uncertainty Quantification of Contaminated Soil Volume with Deep Neural Networks and Predictive Models
Environ Model Assess
Guridi, Ignacio (author) / Chassagne, Romain (author) / Pryet, Alexandre (author) / Atteia, Olivier (author)
Environmental Modeling & Assessment ; 29 ; 621-640
2024-06-01
20 pages
Article (Journal)
Electronic Resource
English
Soil contamination , Error estimation , Integrated approach , Deep learning , Deep Neural Network (DNN) , Sample set analysis Environment , Math. Appl. in Environmental Science , Mathematical Modeling and Industrial Mathematics , Operations Research/Decision Theory , Applications of Mathematics , Earth and Environmental Science
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