Statistical modeling of extreme rainfall processes in British Columbia
Bibliographic record
Abstract
Millions of people are still exposed to unanticipated extreme rainfall events, and their devastating effects extend from communities to the surrounding environment. The impact extends across borders, to both developed and developing nations, causing massive casualties and financial loss. Accurate estimation of such events, however, requires an elaborated investigation covering different parameters, since precipitation patterns can be so diverse depending on the regional Topographical condition and even more so with progressive climate change. Prediction of extreme precipitations has been extensively studied and improved in recent years by various specialists from science and engineering. In particular, in current engineering practices for the estimation of extreme rainfall for design purposes, many probability models have been proposed for describing the distribution of this random variable. However, there is no general agreement as to which distribution should be used to provide the most accurate and most reliable design rainfall estimate. In view of the above-mentioned issues, the overall objective of the present research is therefore to propose a general procedure for assessing the descriptive and predictive abilities of ten probability distributions that have been used in extreme rainfall frequency analyses. The feasibility of the proposed procedure was tested using available 5-minute, 1-hour, and 24-hour annual maximum rainfall data from a network of 11 raingage stations located in the British Columbia region in Canada. Two commonly used methods, the maximum likelihood and L-moment methods, were used for estimating the parameters of the selected probability models. On the basis of the assessment of the descriptive and predictive abilities of each model, the GNO, PE3 and GEV models were found the best choice for the selected daily and sub-daily annual maximum rainfalls. Despite the popular use of GEV in Canada, the GNO distribution was found to have more robust and accurate descriptive and predictive ability from this study. However, no one distribution consistently outperformed the others among those distributions, and it is impossible to choose one distribution as the best to represent the versatile rainfall pattern of BC. The performance of the distribution models was not consistent with either the topographical or climatological condition of study stations. Yet it was evident that most distributions performed poorly with data sets with high skewness. However, it was difficult to define a pattern of skewness in data, as skewness can vary without relation to rainfall durations and climatological or Topographical condition. Using the proposed procedure for selecting the best distribution, the GNO, GEV and PE3 were found the best overall choice for its descriptive and predictive ability with annual maximum rainfall data in British Columbia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".