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April 16, 2026Scientific Reports0 citationsOpen Access

Predictive modelling of Uranium (238U) and Thorium (232Th) in soils of Central India: integrating ICP-MS/MS, spectroscopic, and machine learning models

GTG. S. TagoreDSDevid Kumar SahuYSY.M. Sharma

Key Points

  • To model the concentrations of Uranium (238U) and Thorium (232Th) in soils of Central India using machine learning techniques.
  • Collected 2216 surface soil samples across Central India using GPS-based multi-layer sampling.
  • Analyzed soil samples for Uranium and Thorium concentrations using ICP-MS/MS and spectral data.
  • Evaluated several machine learning algorithms for predicting elemental concentrations.
  • Used geostatistical analysis to assess the spatial distribution of Uranium and Thorium.
  • Concentrations of 238U ranged from 0.15 to 852.15 ppb with a mean of 94.92 ppb.
  • Concentrations of 232Th ranged from 0.06 to 1385.06 ppb with a mean of 71.73 ppb.
  • The ANN model achieved the highest testing R² of 0.56 for 232Th and 0.36 for 238U.
  • Geostatistical analysis indicated moderate spatial dependence for 238U and moderate to strong for 232Th.
  • Spatial maps showed specific regions of enrichment for both elements.

Abstract

Global Positioning System (GPS) based 2216 surface soil (0–15 cm) samples were collected across the Kymore Plateau and Satpura hill zone of Madhya Pradesh, India using multi-layer sampling strategy to ensure spatial representativeness. The concentrations of Uranium (238U) and Thorium (232Th) were analysed using inductively coupled plasma mass spectrometry (ICP-MS/MS) with spectral data acquired through a Spectro-radiometer. The 238U concentrations ranged from 0. 15 to 852. 15 ppb (mean: 94. 92 ppb), while 232Th ranged from 0. 06 to 1385. 06 ppb (mean: 71. 73 ppb). Soil pH exhibited a positive correlation with 238U and a negative correlation with ²³²Th. Several machine learning (ML) models were evaluated for prediction, including Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), Extreme Gradient Boosting (XGBoost), Light Gradient-Boosting Machine (LightGBM), Categorical Boosting (CatBoost) and Extreme Learning Machine (ELM), the ANN demonstrated the balanced and robust performance. For 238U, ANN achieved a testing coefficient of determination (R²) of 0. 36 (\: ^238U transformed data), root mean square error (RMSE) of 2. 87, and ratio of performance to deviation (RPD) of 1. 25, while for ²³²Th it achieved the highest testing R² of 0. 56, RMSE of 0. 46, and RPD of 1. 51. Data transformation square root of 238U (\: ^238U) and logarithmic of 232Th (ln 232Th) improved model performance by stabilizing variance. Geostatistical analysis using an exponential semivariogram model indicated moderate spatial dependence (SD) for 238U (nugget-to-sill (N/S) ratio ~ 43%, range 18. 23 km) and moderate to strong SD for ²³²Th (30%, range 11. 06 km). Validation results root mean square standardised error (RMSS ≈ 1) confirmed reliable uncertainty estimation. Spatial distribution maps revealed 232Th enrichment in the Panna-Katni region and 238U enrichment in the Northern districts (Katni-Panna-Satna) and Southern Seoni, with Eastern districts of study area showing relatively lower concentrations.

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Cite This Study

Tagore et al. (2026) studied this question.

synapsesocial.com/papers/69e07e242f7e8953b7cbf0f1https://doi.org/10.1038/s41598-026-47578-4
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