Statistical modeling of extreme rainfall processes in British Columbia
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».