Volume 26, Issue 4 (2023)
MJSP 2023, 26(4): 139-160 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Shamsoddini A, Esmaeili S. Modelling of Soil Heavy Metal contamination using Machine learning techniques and spectroscopic data. MJSP 2023; 26 (4) :139-160
URL: http://hsmsp.modares.ac.ir/article-21-56457-en.html
URL: http://hsmsp.modares.ac.ir/article-21-56457-en.html
Modelling of Soil Heavy Metal contamination using Machine learning techniques and spectroscopic data
1- Department of Remote Sensing and GIS, Faculty of Humanities, tarbiat Modares University, Tehran, Iran , ali.shamsoddini@modares.ac.ir
2- MSc., Department of Remote Sensing and GIS, Islamic Azad University, Science and Research branch of Tehran, Iran.
2- MSc., Department of Remote Sensing and GIS, Islamic Azad University, Science and Research branch of Tehran, Iran.
Abstract: (2477 Views)
Mines and their related-industries are able to affect their surrounding environment, not only by their activities, but also after being abandoned. Among their different harmful effects, under water and surface water contaminations, and soil contamination can be mentioned. In order to manage these environmental effects, it is necessary to use reasonable methods for modelling heavy metal concentration in soil. This study aims to present a framework for modelling heavy metal soil contamination based on spectroscopy and statistical models. For this purpose, the spectral curves of the 53 soil samples, derived from an abandoned mine and its surrounding areas in New South Wales, Australia, were collected using a spectroradiometer in visible to short wavelength infrared (SWIR) wavelengths. Calculating the second derivative of the collected spectral data, random forest feature selection method (RFFS) was used to determine the most important spectral data for modelling heavy metal concentrations including lead, silver, cadmium and mercury. Then, the modelling techniques including multiple linear regression, random forest regression, and support vector regression (SVR) were applied on the selected spectral data. The results indicated that SWIR wavelengths are the most important spectral data for modelling heavy metal concentrations. Moreover, the non-linear machine learning methods, especially random forest with RMSE of 0.8 ppm and R2 of 0.51 for lead and RMSE of 9.4 ppm and R2 of 0.46 for cadmium performed better than multiple linear regression.
Article Type: Original Research |
Subject:
techniques if spatial / locational data processing in environmental planning
Received: 2021/10/17 | Accepted: 2022/07/12 | Published: 2023/03/1
Received: 2021/10/17 | Accepted: 2022/07/12 | Published: 2023/03/1
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |