Model Agreement and Disagreement in Rainfall Classification: A Region-Dependent Comparative Analysis of ARIMA and Random Forest across Punjab Districts, India

Authors

  • Vanshita Arora

    School of Chemical Engineering, Lovely Professional University, Phagwara 144411, India
  • Mohammad Shahfaraz Khan

    College of Economics and Business Administration, University of Technology and Applied Sciences-Salalah, Salalah 211, Oman
  • Imran Azad

    College of Economics and Business Administration, University of Technology and Applied Sciences-Salalah, Salalah 211, Oman
  • Amir Ahmad Dar

    School of Chemical Engineering, Lovely Professional University, Phagwara 144411, India
  • Amit Kishore Sinha

    Department of Commerce, Faculty of Business & Commerce, Chandigarh University, Uttar Pradesh Campus, Unnao 209801, India
  • Mohammed Wamique Hisam

    College of Commerce and Business Administration, Dhofar University, Salalah 211, Oman

DOI:

https://doi.org/10.30564/re.v8i4.13458
Received: 27 April 2026 | Revised: 21 May 2026 | Accepted: 26 May 2026 | Published Online: 3 August 2026

Abstract

For agricultural planning and management in monsoon-dependent regions, accurate classification of annual rainfall into risk categories is crucial. The predictive effectiveness of several models for rainfall forecasting has been compared in numerous studies, but the diagnostic significance of model disagreement has not been examined. This study aimed to analyse whether there is a link between the disagreement between autoregressive integrated moving average (ARIMA) and Random Forest (RF) and the difference in the accuracy of the predictions given by the two models in seven districts of Punjab, India (1970–2024). Prior to modelling, a pre-specified, data-driven district selection protocol was used, which was based on the coefficient of variation (CV) of rainfall, avoiding outcome-driven selection bias. The data were discretized into three categories (bins) with training-set bin edges to avoid data leakage. Fisher's exact test, Wilson score confidence intervals, and block permutation testing were used for statistical inference. In the five reliable districts (cells n ≥ 3), the prediction accuracy was significantly greater when the model disagreed with the data than when it agreed (0.83 vs. 0.52; Fisher's exact p = 0.022; block permutation p = 0.038). However, the Mansa district (dis.: 0.25; agree: 0.33) had a reverse pattern, indicating that the relationship was not pervasive. The findings are empirically consistent with the heuristic intuition of ensemble diversity theory but are not derivable from known theorems. The disagreement between ARIMA and RF can be used as a realistic uncertainty indicator for real-time rainfall monitoring. The findings are exploratory and hypothesis-generating, as there are small test sets per district.

Keywords:

Rainfall Classification; Model Disagreement; ARIMA; Random Forest (RF); Ensemble Diversity; Punjab Hydrology; Coefficient of Variation; Southwest Monsoon (SWM)

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How to Cite

Arora, V., Khan, M. S., Azad, I., Dar, A. A., Sinha, A. K., & Hisam, M. W. (2026). Model Agreement and Disagreement in Rainfall Classification: A Region-Dependent Comparative Analysis of ARIMA and Random Forest across Punjab Districts, India. Research in Ecology, 8(4), 200–214. https://doi.org/10.30564/re.v8i4.13458

Issue

Vol.8, Iss.4 (August 2026): In Progress

Article Type

Article

License

Copyright © 2026 Vanshita Arora, Mohammad Shahfaraz Khan, Imran Azad, Amir Ahmad Dar, Amit Kishore Sinha, Mohammed Wamique Hisam

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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