Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning
https://doi.org/10.18599/grs.2022.1.8
Abstract
According to the data of remote sensing of the Earth, the accuracy of the spatial prediction of soil indicators determining sorption properties in relation to pollutants was compared. To build spatial maps of changes in soil properties, machine learning methods based on support vector regression models (SVMr) and random forest (RF) were used. It was shown that the methods of machine modeling using remote sensing can be successfully used for spatial prediction of the content of particle size fractions, organic matter, pH and the capacity of cation exchange of soils in small areas. It is shown that the spatial prediction of the content of silt fraction is best modeled using the RF algorithm, while the other properties of soils that can determine their sorption potential in relation to pollutants are better modeled using the SVMr method. In general, both machine learning methods have similar spatial prediction results.
Keywords
sorption properties of soil,
spatial prediction,
remote sensing data of the Earth,
machine learning methods
Supporting agencies: This research was funded by the Russian Foundation for Basic Research, research project No. 19-29-05061-mk.
About the Authors
K. G. Giniyatullin
Kazan Federal University
Russian Federation
Russian Federation
Kamil G. Giniyatullin – PhD (Biology), Associate Professor
18, Kremlevskaya st., Kazan, 420008
I. A. Sahabiev
Kazan Federal University
Russian Federation
Russian Federation
Ilnas A. Sahabiev – Senior Lecturer
18, Kremlevskaya st., Kazan, 420008
E. V. Smirnova
Kazan Federal University
Russian Federation
Russian Federation
Elena V. Smirnova – PhD (Biology), Associate Professor
18, Kremlevskaya st., Kazan, 420008
I. A. Urazmetov
Kazan Federal University
Russian Federation
Russian Federation
Ildar A. Urazmetov – PhD (Pedagogic), Associate Professor
18, Kremlevskaya st., Kazan, 420008
R. V. Okunev
Kazan Federal University
Russian Federation
Russian Federation
Rodion V. Okunev – PhD (Biology), Associate Professor
18, Kremlevskaya st., Kazan, 420008
K. A. Gordeeva
Kazan Federal University
Russian Federation
Russian Federation
Karina A. Gordeeva – PhD student
18, Kremlevskaya st., Kazan, 420008
References
1. Al-Ruzouq R., Gibril M. A., Abdallah S., Kais A., Hamed O., Saeed Al-M., Mohamad K. (2020). Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review. Remote Sensing, 12, 3338. https://doi.org/10.3390/rs12203338
2. Andronikov S.V., Davidson D.A., Spiers R.B. (2000). Variability in Contamination by Heavy Metals: Sampling Implications. Water, Air, & Soil Pollution, 120, pp. 29–45. https://doi.org/10.1023/A:1005261522465
3. Beucher A., Adhikari K., Breuning-Madsen H., Greve M.B., Österholm P., Fröjdö S., Jensen N.H., Greve M.H. (2017). Mapping potential acid sulfate soils in Denmark using legacy data and LiDAR-based derivatives. Geoderma, 308, pp. 363–372. https://doi.org/10.1016/j.geoderma.2016.06.001
4. Biau G., Scornet E. (2016). A random forest guided tour. Test, 25, pp. 197–227. https://doi.org/10.1007/s11749-016-0481-7
5. Caubet M., Dobarco R. M., Arrouays D.,Minasny B., Saby N. (2019). Merging country, continental and global predictions of soil texture: Lessons from ensemble modelling in France. Geoderma, 337. pp. 99–110. https://doi.org/10.1016/j.geoderma.2018.09.007
6. Cho K.H., Sthiannopkao S., Pachepsky Y.A., Kim K.W., Kim J.H. (2011). Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network. Water Res, 45(17), pp. 5535–5544. https://doi.org/10.1016/j.watres.2011.08.010
7. Cortes C., Vapnik V. (1995). Support-vector networks. Mach. Learn., 20, pp. 273–297. https://doi.org/10.1007/BF00994018
8. Cui Y.-Q., Yoneda M., Shimada Y., Matsui Y. (2016). Cost-Effective Strategy for the Investigation and Remediation of Polluted Soil Using Geostatistics and a Genetic Algorithm Approach. Journal of Environmental Protection, 07(01), pp. 99–115. https://doi.org/10.4236/jep.2016.71010
9. Deiss L., Margenot A.J., Culman S.W., Demyan M.S. (2020). Tuning support vector machines regression models improves prediction accuracy of soil properties in MIR spectroscopy, Geoderma, 365, 114227. https://doi.org/10.1016/j.geoderma.2020.114227
10. Digital soil cartography (2017). Moscow: RUDN University, 152 p. (In Russ.)
11. Einax J., Soldt U., Geostatistical investigations of polluted soils. (1995). Fresenius’ Journal of Analytical Chemistry, 351, pp. 48–53. https://doi.org/10.1016/j.envpol.2012.06.006
12. Grunwald S. (2009). Multi-criteria characterization of recent digital soil mapping and modeling approaches. Geoderma, 152, рр. 195–207. https://doi.org/10.1016/j.geoderma.2009.06.003
13. Güler C., Alpaslan M., Kurt M.A. (2010). Deciphering factors controlling trace element distribution in the soils of Karaduvar industrial-agricultural area (Mersin, SE Turkey). Environ Earth Sci, 60, pp. 203–218. https://doi.org/10.1007/s12665-009-0180-8
14. Ha H., Olson J.R., Bian L., Rogerson P.A. (2014). Analysis of Heavy Metal Sources in Soil Using Kriging Interpolation on Principal Components. Environmental Science & Technology, 48, pp. 4999–5007. https://doi.org/10.1021/es405083f
15. Harrell F.E.Jr. (2001). Regression Modeling Strategies. With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer, 507 p. https://doi.org/10.1007/978-1-4757-3462-1
16. Hengl T., Nussbaum M., Wright M.N., Heuvelink G.B.M., Gräler B. (2018). Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, e5518. https://doi.org/10.7717/peerj.5518
17. Hooda, P.S., Glavinandp R.J. (2005). A Practical Examination of the Use of Geostatistics in the Remediation of a Site with a Complex Metal Contamination History. Soil and Sediment Contamination, 14, pp. 155–169. https://doi.org/10.1080/15320380590911814
18. Juang K.-W., Liao W.-J., Liu T.-L., Tsui L., Lee D.-Y. (2008). Additional sampling based on regulation threshold and kriging variance to reduce the probability of false delineation in a contaminated site. Science of the Total Environment, 389, pp. 20–28. https://doi.org/10.1016/j.scitotenv.2007.08.025
19. Kabata-Pendias A. (2000). Trace Elements in Soils and Plants. CRC Press, 403 p. https://doi.org/10.1201/9781420039900
20. Laborczi A., Gábor S., Kaposi A., László P. (2018). Comparison of soil texture maps synthetized from standard depth layers with directly compiled products. Geoderma, 352, pp. 360–372. https://doi.org/10.1016/j.geoderma.2018.01.020
21. Levy D.B., Barbarrick K.A., Siemer E.G., Sommers L.E. (1992). Distribution and partitioning of trace metals in contaminated soils near Leadville, Colorado. J Environ Qual, 21, pp. 185–195. https://doi.org/10.2134/jeq1992.00472425002100020006x
22. Lin Y.-P., Cheng B.-Y., Chu H.-J., Chang T.-K., Yu H.-L. (2011). Assessing how heavy metal pollution and human activity are related by using logistic regression and kriging methods. Geoderma, 163(3–4), pp. 275–282. https://doi.org/10.1016/j.geoderma.2011.05.004
23. Lin Y.-P., Chu H.-J., Huang Y.-L., Cheng B.-Y., Chang T.-K. (2010). Modeling Spatial Uncertainty of Heavy Metal Content in Soil by Conditional Latin Hypercube Sampling and Geostatistical Simulation. Environmental Earth Sciences, 62, pp. 299–311. https://doi.org/10.1007/s12665-010-0523-5
24. Loiseau T., Arrouays D., Richer-de-Forges A., Lagacherie P., Ducommun C., Minasny B. (2021). Density of soil observations in digital soil mapping: A study in the Mayenne region, France. Geoderma Reg., 24, e00358. https://doi.org/10.1016/j.geodrs.2021.e00358
25. Lv J., Yang L., Zhang Z., Dai J. (2013). Factorial kriging and stepwise regression approach to identify environmental factors influencing spatial multiscale variability of heavy metals in soils. Journal of Hazardous Materials, 261(15), pp. 387–397. https://doi.org/10.1016/j.jhazmat.2013.07.065
26. Mahmoudzadeh H., Matinfar H.R., Taghizadeh-Mehrjardi R., Kerry R. (2020). Spatial prediction of soil organic carbon using machine learning techniques in western Iran. Geoderma Reg., 21, e00260. https://doi.org/10.1016/j.geodrs.2020.e00260
27. Matinfara H. R., Maghsodi Z., Mousavi S. R., Rahmani A. (2021). Evaluation and Prediction of Topsoil organic carbon using Machine learning and hybrid models at a Field-scale. Catena, 202, pp. 105258. https://doi.org/10.1016/j.catena.2021.105258
28. McBratney A.B., Mendonçа Santos M.L., Minasny B. (2003). On digital soil mapping. Geoderma, 117, pp. 3–52. https://doi.org/10.1016/S0016-7061(03)00223-4
29. Milillo T.M., Sinha G., Gardella J.A. (2012). Use of Geostatistics for Remediation Planning to Transcend Urban Political Boundaries. Environmental Pollution, 170, pp. 52–62. https://doi.org/10.1016/j.envpol.2012.06.006
30. Mishra R., Naseer M., Nilanjan R. (2016). Soil pollution: Causes, effects and control. Van Sangyan, 3, pp. 1–14.
31. Pahlavan-Rad M.R., Dahmardeh K., Brungard C. (2018). Predicting soil organic carbon concentrations in a low relief landscape, eastern Iran. Geoderma Reg., 15, e00195. https://doi.org/10.1016/j.geodrs.2018.e00195
32. Pasolli L., Notarnicola C., Bruzzone L. (2011). Estimating soil moisture with the support vector regression technique. IEEE Geosci. Remote Sens. Lett., 8, pp. 1080–1084. https://doi.org/10.1109/LGRS.2011.2156759
33. Paterson S., Minasny B., Mcbratney A. (2018). Spatial variability of Australian soil texture: A multiscale analysis. Geoderma, 309, pp. 60–74. https://doi.org/10.1016/j.geoderma.2017.09.005
34. Platenburg R.J.P.M., Tuinhof H., Bot A.P., Iwaco B.V. (1988). Geostatistics in Soil Pollution Research. Contaminated Soil ‘88. Springer, Dordrecht, pp. 209–211. https://doi.org/10.1007/978-94-009-2807-7_32
35. Ryazanov S. S., Ivanov D. V., Kulagina V. I. (2019). Heavy metals in topsoils of the Republic of Tatarstan. Russian Journal of Ecosystem Ecology, 4(3), pp. 1–14. https://doi.org/10.21685/2500-0578-2019-3-4
36. Saby N., Thioulouse J., Jolivet C., Ratie C., Boulonne L., Bispo A., Arrouays D. (2009). Multivariate analysis of the spatial patterns of 8 trace elemets using the French monitoring network data. Science of the Total Environment, 407, pp. 5644–5652. https://doi.org/10.1016/j.scitotenv.2009.07.002
37. Sakizadeh M., Martín J.A.R. (2021). Spatial methods to analyze the relationship between Spanish soil properties and cadmium content. Chemosphere, 268, 129347. https://doi.org/10.1016/j.chemosphere.2020.129347
38. Schneckenburger T., Thiele-Bruhn S. (2020). Sorption of PAHs and PAH derivatives in peat soil is affected by prehydration status: the role of SOM and sorbate properties. J Soils Sediments, 20, pp. 3644–3655. https://doi.org/10.1007/s11368-020-02695-z
39. Sergeev A.P., Buevich A.G., Baglaeva E.M., Shichkin A.V. (2019). Combining spatial autocorrelation with machine learning increases prediction accuracy of soil heavy metals. Catena, 174, pp. 425–435. https://doi.org/10.1016/j.catena.2018.11.037
40. Shi B., Ngueleu S.K., Rezanezhad F., Slowinski S., Pronk G.J., Smeaton C.M., Stevenson K., Al-Raoush R.I., Van Cappellen P. (2020) Sorption and Desorption of the Model Aromatic Hydrocarbons Naphthalene and Benzene: Effects of Temperature and Soil Composition. Front. Environ. Chem, 1, 581103. https://doi.org/10.3389/fenvc.2020.581103
41. Shi T., Yang C., Liu H., Wu C., Wang Z., Li H., Zhang H., Guo L., Wu G., Su F. (2021). Mapping lead concentrations in urban topsoil using proximal and remote sensing data and hybrid statistical approaches. Environmental Pollution, 272, 116041. https://doi.org/10.1016/j.envpol.2020.116041
42. Smola A.J., Scholköpf B. (2004). A tutorial on support vector regression. Stat. Comput., 14, pp. 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88
43. Taghizadeh-Mehrjardi R., Schmidt K., Toomanian N., Heung B., Behrens T., Mosavi A., Band S.S., Amirian-Chakan A., Fathabadi A., Scholten T. (2021). Improving the spatial prediction of soil salinity in arid regions using wavelet transformation and support vector regression models. Geoderma, 383, 114793. https://doi.org/10.1016/j.geoderma.2020.114793
44. Tarasov D.A., Buevich A.G., Sergeev A.P., Shichkin A.V. (2018). High variation topsoil pollution forecasting in the Russian Subarctic: Using artificial neural networks combined with residual kriging, Applied Geochemistry, 88, Part B, pp. 188–197. https://doi.org/10.1016/j.apgeochem.2017.07.007
45. Tsibart A.S., Gennadiev A.N. (2013). Polycyclic aromatic hydrocarbons in soils: sources, behavior, and indication significance (a review). Eurasian Soil Science, 46(7), pp. 728–741. https://doi.org/10.1134/S1064229313070090
46. Vincent S., Lemercier B., Berthier L., Walte C. (2018). Spatial disaggregation of complex Soil Map Units at the regional scale based on soil-landscape relationships. Geoderma, 311, pp. 130–142. https://doi.org/10.1016/j.geoderma.2016.06.006
47. Were K., Bui D.T., Dick Ø.B., Singh B.R. (2015). A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol. Indic., 52, pp. 394–403. https://doi.org/10.1016/j.ecolind.2014.12.028
48. Yuan G., Sun T., Han P., Li J. (2013). Environmental geochemical mapping and multivariate geostatistical analysis of heavy metals in topsoils of a closed steel smelter: Capital Iron and Steel Factory, Bejing, China. Journal of Geochemical Exploration, 130, pp. 15–21. https://doi.org/10.1016/j.gexplo.2013.02.010
49. Zhang L., Liu Y., Li X., Huang L., Yu D., Shi X., Chen H., Xing S. (2018). Effects of soil map scales on simulating soil organic carbon changes of upland soils in Eastern China. Geoderma, 312, pp. 159–169. https://doi.org/10.1016/j.geoderma.2017.10.017
50. Zwolak A., Sarzyńska M., Szpyrka E., Stawarczyk K. (2019). Sources of Soil Pollution by Heavy Metals and Their Accumulation in Vegetables: a Review. Water, Air, & Soil Pollution, 230(164). https://doi.org/10.1007/s11270-019-4221-y
Review
For citations:
Giniyatullin K.G., Sahabiev I.A., Smirnova E.V., Urazmetov I.A., Okunev R.V., Gordeeva K.A. Digital mapping of indicators that determine the sorption properties of soils in relation to pollutants, according to remote sensing data of the Earth using machine learning. Georesursy = Georesources. 2022;24(1):84-92. (In Russ.) https://doi.org/10.18599/grs.2022.1.8
Views:
124
.png){kind=logo}
Email this article 