Spatial Distribution Prediction and Migration Characteristic of Petroleum Hydrocarbons in Soil Based on Artificial Neural Networks


Aili Gao, Lan Chen, Xiaohan Wei, Chao Liu, Lihua Cheng, Journal of Information Processing Systems Vol. 20, No. 6, pp. 841-852, Dec. 2024  

https://doi.org/10.3745/JIPS.04.0330
Keywords: Feedforward Neural Networks, Migration Characteristics, Petroleum Hydrocarbon, spatial distribution
Fulltext:

Abstract

Soil pollution resulting from petroleum hydrocarbons (PHCs) arising from industrialization and human activities has emerged as a progressively severe global concern. Establishing an accurate spatial distribution prediction model for PHCs through limited sampling data play an important role in understanding the migration characteristics of PHCs and effectively preventing soil pollution. This article employs soil samples within 8 m of a chemical plant, in conjunction with hydrogeological data, to model the spatial distribution of PHC content using a feedforward neural network (FNN). The prediction outcomes are characterized through three-dimensional visualization. The findings indicate that FNN demonstrates superior estimation accuracy compared to traditional interpolation method. Regarding the horizontal distribution within surface soil, there is pronounced lateral migration of PHC content in both the storage area and manufacturing shop, with migration aligning following the direction of groundwater. Vertically, PHC content exhibits a consistent pattern of increasing and then decreasing with greater depth. It is predominantly enriched in the lower section of the aeration zone and the upper part of the saturated zone, particularly within 4 m, under influence of groundwater. In this study, the prediction model offers an original approach to the spatial distribution of soil pollutants.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Gao, A., Chen, L., Wei, X., Liu, C., & Cheng, L. (2024). Spatial Distribution Prediction and Migration Characteristic of Petroleum Hydrocarbons in Soil Based on Artificial Neural Networks. Journal of Information Processing Systems, 20(6), 841-852. DOI: 10.3745/JIPS.04.0330.

[IEEE Style]
A. Gao, L. Chen, X. Wei, C. Liu, L. Cheng, "Spatial Distribution Prediction and Migration Characteristic of Petroleum Hydrocarbons in Soil Based on Artificial Neural Networks," Journal of Information Processing Systems, vol. 20, no. 6, pp. 841-852, 2024. DOI: 10.3745/JIPS.04.0330.

[ACM Style]
Aili Gao, Lan Chen, Xiaohan Wei, Chao Liu, and Lihua Cheng. 2024. Spatial Distribution Prediction and Migration Characteristic of Petroleum Hydrocarbons in Soil Based on Artificial Neural Networks. Journal of Information Processing Systems, 20, 6, (2024), 841-852. DOI: 10.3745/JIPS.04.0330.