Forecasting the accumulation of <sup>137</sup>Cs by trees and crops using the decision tree method-Reference-Cited by-同舟云学术

Forecasting the accumulation of <sup>137</sup>Cs by trees and crops using the decision tree method

Published:2024-08-01 Issue:3 Volume:69 Page:237-248
ISSN:2524-230X
Container-title:Proceedings of the National Academy of Sciences of Belarus, Biological Series
language:
Short-container-title:Vescì Akademìì navuk Belarusì. Seryâ biâlagičnyh navuk

Author:

Nikitin A. N.¹^ORCID,Kudin M. V.²^ORCID,Kalinichenko S. A.²,Lasko T. V.³,Shurankova O. A.³,Mishchanka E. V.³

Affiliation:

1. Institute of Microbiology of the National Academy of Sciences of Belarus

2. Polessie State Radioecological Reserve

3. Institute of Radiobiology of the National Academy of Sciences of Belarus

Abstract

The article provides a profound analysis of the accumulation of the radionuclide 137Cs in the stems of pine trees and harvest of crops, employing decision tree methods and SHAP analysis. In pine forests situated in the Chernobyl exclusion zone, a nonlinear relationship between the aggregated transfer factor and elevation above sea level is identified, along with the influence of vegetation indices pointing to overall stand condition, biological productivity, and potassium deficiency. In agroecosystems situated in Gomel and Mogilev regions, the impact of plant species, K+ concentration in the soil solution on aggregated transfer factor is confirmed. Interpretable machine learning method shows dependence of aggregated transfer factor from soil moisture and the persistence of transfoliar contamination at low soil pollution levels at late stage after Chernobyl catastrophe. The application of decision trees and SHAP analysis offers a deeper understanding of complex interactions in the “soil-plant” system, opening perspectives for effective monitoring and management of radioactive contamination in diverse natural and agricultural environments.

Publisher

Publishing House Belorusskaya Nauka

Reference27 articles.

1. Debeljak M., Džeroski S. Decision trees in ecological modelling. Modelling complex ecological dynamics: An introduction into ecological modelling for students, teachers & scientists. Berlin, Heidelberg, 2011, pp. 197–209. https://doi.org/10.1007/978-3-642-05029-9_14

2. Zhang B., Valentine I., Kemp P. Modelling the productivity of naturalised pasture in the North Island, New Zealand: a decision tree approach. Ecological Modelling, 2005, vol. 186, no. 3, pp. 299–311. https://doi.org/10.1016/j.ecolmodel.2004.12.016

3. Xu Y., Zhang D., Lin J., Peng Q., Lei X., Jin T., Wang J., Yuan R. Prediction of phytoplankton biomass and identification of key influencing factors using interpretable machine learning models. Ecological Indicators, 2024, vol. 158, art. 111320. https://doi.org/10.1016/j.ecolind.2023.111320

4. Cai J., Xu K., Zhu Y., Hu F., Li L. Prediction and analysis of net ecosystem carbon exchange based on gradient boosting regression and random forest. Applied Energy, 2020, vol. 262, art. 114566. https://doi.org/10.1016/j.apenergy.2020.114566

5. Ndraha N., Hsiao H., Hsieh Y., Pradhan A. K. Predictive models for the effect of environmental factors on the abundance of vibrio parahaemolyticus in oyster farms in Taiwan using extreme gradient boosting. Food Control, 2021, vol. 130, art. 108353. https://doi.org/10.1016/j.foodcont.2021.108353