INTEGRATED STATISTICAL MODELLING OF IRON EXCEEDANCE RISK: A MONTE CARLO, LOGISTIC REGRESSION, RANDOM FOREST, AND SOBOL ANALYSIS APPROACH

  • Rachid El Chaal ENSA of Kenitra, Engineering Sciences Laboratory, Data Analysis, Mathematical Modeling and Optimization Team, Ibn Tofail University, Morocco https://orcid.org/0000-0002-6399-3471
  • Hamid Dalhi ENSA of Kenitra, Engineering Sciences Laboratory, Data Analysis, Mathematical Modeling and Optimization Team, Ibn Tofail University, Morocco https://orcid.org/0009-0009-2690-1898
  • Otmane Darbal ENSA of Kenitra, Engineering Sciences Laboratory, Data Analysis, Mathematical Modeling and Optimization Team, Ibn Tofail University, Morocco https://orcid.org/0009-0003-7381-1015
  • Moulay Othman Aboutafail ENSA of Kenitra, Engineering Sciences Laboratory, Data Analysis, Mathematical Modeling and Optimization Team, Ibn Tofail University, Morocco https://orcid.org/0000-0003-3634-6129
DOI: https://doi.org/10.30598/barekengvol20iss1pp0637-0656
Keywords: Kolmogorov-Smirnov test, Log-Normal, Monte Carlo simulation, Sobol sensitivity analysis, Statistical modelling, Water quality

Abstract

The quality of water resources in the Inaouen watershed, northern Morocco, is increasingly threatened by metal contamination, particularly iron (Fe). This study implements an integrated statistical framework to assess the risk of exceeding regulatory iron concentration thresholds. After preprocessing local physico-chemical data, a binary indicator variable was constructed to flag exceedances of the critical 30 µg/L threshold. Iron concentrations were modeled using log-normal and Weibull distributions, with a Monte Carlo simulation (n = 10,000) based on the log-normal law estimating exceedance probabilities across multiple thresholds (30, 50, 100 µg/L), revealing an 18% risk at 30 µg/L. Predictive modeling via logistic regression and random forest analysis identified calcium (Ca) as the dominant driver of iron exceedances, a finding corroborated by Sobol sensitivity analysis (S1 index = 0.74), with bicarbonate (HCO₃⁻) emerging as a secondary factor (S1 = 0.10). These results demonstrate the power of combining distribution fitting, machine learning, and global sensitivity analysis to effectively quantify and interpret iron contamination risks in vulnerable watersheds such as Inaouen. The proposed methodology offers a robust decision-support tool for sustainable water resource management and public health protection.

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Published
2025-11-24
How to Cite
[1]
R. E. Chaal, H. Dalhi, O. Darbal, and M. O. Aboutafail, “INTEGRATED STATISTICAL MODELLING OF IRON EXCEEDANCE RISK: A MONTE CARLO, LOGISTIC REGRESSION, RANDOM FOREST, AND SOBOL ANALYSIS APPROACH”, BAREKENG: J. Math. & App., vol. 20, no. 1, pp. 0637-0656, Nov. 2025.
Issue
Vol 20 No 1 (2026): BAREKENG: Journal of Mathematics and Its Application
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Copyright (c) 2025 Rachid El Chaal, Hamid Dalhi, Otmane Darbal, Moulay Othman Aboutafail

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