Seasonal Autoregressive Integrated Moving Average Modelling and Forecasting of Monthly Rainfall in Selected African Stations
Notice bibliographique
Résumé
Africa as a continent is blessed with arable land suitable for crop production but this cannot be fully harnessed without proper understanding of the rainfall pattern.Modelling and forecasting rainfall in Africa is even more important now considering the climate change that has brought a new narrative into the rainfall pattern globally, Africa inclusive.This study applied Seasonal Integrated Moving Average (SARIMA) models in modelling and forecasting rainfall across five selected African stations with one station each from the five African regions: West (Abuja, Nigeria), East (Nairobi, Kenya), South (Pretoria, South-Africa), North (Cairo, Egypt) and Central Africa (Yaoundé , Cameroon).Monthly rainfall data for these stations between 1980 and 2022 (42 years) were obtained from the MERRA-2 satellite.However, the data for this study were obtained from the solar radiation data archive website (www.soda-pro.com).The Soda service provides time series of solar radiation data derived from satellites.Furthermore, Modern-Era Retrospective Analysis for Research and Application-2 (MERRA-2) data were extracted from the satellite, which included meteorological and atmospheric data.Since January 1980, the data has been available in hourly, daily, and monthly increments.However, missing data values were checked and removed before implementing the analysis in this study.The determination of the specific SARIMA parameters orders for each city was carried by manual tuning after observing the plots of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF).Descriptive analysis revealed that Abuja had the highest variance in the amount of rainfall compared with other major cities in Africa while rainfall in Yaoundé between March and June was higher than that of other stations.Monthly rainfall was stationary in all the stations as shown by the result of Augmented Dickey (p<.05) except for Yaoundé which was stationary after the first differencing.Based on the result of outof-sample forecast performance, different SARIMA models were found to be suitable for rainfall in each of the stations with ARIMA (0,0,1) (1,0,1)12 for Abuja (RMSE=70.7044)and Nairobi (RMSE=92.8925),ARIMA (1,0,1) (1,0,1)12 for Cairo (RMSE=9.9279),ARIMA (2,0,0) (1,0,1)12 for Pretoria (RMSE=42.05462)and ARIMA (1,1,1) (1,0,1)12 for Yaoundé (RMSE=79.42084).The findings show that the seasonal terms were statistically significant in all models which justified the use of seasonal ARIMA models in modelling rainfall in these selected stations in Africa.This also underscored the significant role of the season in the rainfall pattern in the selected African stations.Findings also revealed that the previous month's rainfall has a positive influence on the present month's rainfall in some of the stations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».