Assessment of Risk of Rainfall Events with a Hybrid of ARFIMA-GARCH
Bibliographic record
Abstract
Hazardous situations related to rainfall events can be due to very intense rainfall, or to the persistence of rainfall over a long period of time. Such events may result to an exceedence of the capacity of drainage systems resulting in the heap of basements which may lead to landslides and flooding. This study assesses the persistence dependence of rainfall time series of Chui Chak, a station in Peninsular Malaysia that observed the highest rainfall event for the period 01/01/1975-31/12/2008. The persistence dependence of the rainfall time series was modelled via fractional ARIMA model augmented with the GARCH model. The Ljung-Box test for testing autocorrelation proves that the combined ARFIMA-GARCH model captures the temporal persistence behaviour in the Chui Chak rainfall time series data with persistence measure 0.839. This measure represents a relatively lasting persistence, that is, the process variability should return to the historical average after a relatively long period of time which may have a risk of extreme event.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".