Application of box-jenkins models for forecasting drought in north-western part of Bangladesh
Bibliographic record
Abstract
Recently, the research paradigm has shifted towards prediction, characterization and categorization of droughts for its global impacts on agriculture-based economy. This study aims to parsimoniously forecast the drought phenomena categorized by standardized precipitation index (SPI) for the north-western part of Bangladesh using autoregressive moving average (ARIMA) models. We considered four meteorological stations, namely Bogra, Dinajpur, Ishwardi and Rajshahi which were mostly affected by the droughts. Seasonal effects were most distinct for higher order SPI series with time scales of 12 months and needed to be seasonally differenced. Based on root mean square error (RMSE) and mean absolute error (MAE), the accuracy of the models increased as the order of the SPI series increased over time. There were approximately 60% decrease in RMSE and MAE values for SPI12 series compared to SPI3 series for selected stations. We found as the number of lead times increased the accuracy of the models decreased. A maximum of 6 months lead time was found for SPI12 series at Ishwardi where the fitted model accurately predicted the series. The present study concluded that the researcher should use short term prediction of drought using higher order SPI series for better prediction. Keywords: Accuracy measures, ARIMA, Forecasting, Parsimonious model, Standardized precipitation index
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".