Comparative Machine Learning Framework for Rainfall Forecasting and Agricultural Loss Estimation
Bibliographic record
Abstract
Because of the growing unpredictability of the weather, which can affect food security and productivity, the consequences of climate change are no longer speculative; every farmer in South Asia is starting to suffer the ramifications in their fields. In the two most susceptible districts of Bangladesh, Rajshahi and Ishwardi, the comparative machine learning method presented in this study aims to predict rainfall and identify regions at risk of agricultural impacts due to climate change. We examine the performance of four models: the Prophet model, ARIMA, Random Forest, and XGBoost, using 48 years of historical rainfall data (1976–2024). With an R-squared value of 0.89, Random Forest displayed the best accuracy, exceeding both standard time series and boosting-based approaches while efficiently capturing non-seasonal trends. On the other hand, XGBoost performed poorly, possibly due to the difficulty in fitting noisy, small-scale meteorological data. To classify years as droughts or floods, we apply a conventional anomaly detection technique that utilizes z-scores (1.5 standard deviations) in conjunction with predictive modeling. It is feasible to identify problematic years and regions by using these characteristics, which are linked to historical periods of agricultural displacement. The findings are more accessible and helpful when simplified visual maps of climate-induced risk validate the relationship between the projected anomalies and previous crop failures. The suggested method would provide a scientifically informed tool for climate resilience planning, agricultural planning, and early warning systems. The objective of preserving vulnerable livelihoods during a climate transition is achieved by integrating the three aspects of this architecture, namely translating the long-term records of the meteorological system into risk information that agronomists, policymakers, and humanitarian actors can utilize.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".