Assessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics

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dc.authoridSatti, Samina/0009-0000-9749-0074
dc.authoridKartal, Veysi/0000-0003-4671-1281
dc.contributor.authorBatool, Aamina
dc.contributor.authorAli, Zulfiqar
dc.contributor.authorMohsin, Muhammad
dc.contributor.authorMasmoudi, Atef
dc.contributor.authorKartal, Veysi
dc.contributor.authorSatti, Samina
dc.date.accessioned2024-12-24T19:24:25Z
dc.date.available2024-12-24T19:24:25Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractClimate changes and global warming increase risk of recurrent extreme and complex climatic features. It necessitates accurate modeling and forecasting of climate phenomena for sustainable development goals. However, machine learning algorithms and advanced statistical models are extensively employed to analyze complex data and make predictions related to climate phenomena. It is important to have comprehensive knowledge to use these models and consider their potential implications. This study aims to evaluate and compare some popular machine learning and probabilistic methods by analyzing various time series indices associated with precipitation and temperature. For application, time series data of Standardized Precipitation Temperature Index (SPTI), Standardized Temperature Index (STI), Standardized Compound Drought and Heat Index (SCDHI), and Biased Diminished Weighted Regional Drought Index (BDWRDI) are used from various meteorological regions of Pakistan. The performance of each algorithm is compared using Residual Mean Square Error (RMSE) and Mean Average Error (MAE). The outcomes associated with this research indicate a higher preference of neural networks over machine learning methods in the training sets. However, the efficiency varies from model to model, indicator to indicator, time scale to time scale, and location to location during the testing phase. The most appropriate models are found by considering a list of candidates forecasting models and investigating the performance of each model.
dc.description.sponsorshipDeanship of Scientific Research at King Khalid University [RGP2/171/44]
dc.description.sponsorshipThe authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through large group Research Project under grant number RGP2/171/44.
dc.identifier.doi10.1007/s00477-024-02721-3
dc.identifier.endpage2947
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85190528487
dc.identifier.scopusqualityQ1
dc.identifier.startpage2927
dc.identifier.urihttps://doi.org/10.1007/s00477-024-02721-3
dc.identifier.urihttps://hdl.handle.net/20.500.12604/5983
dc.identifier.volume38
dc.identifier.wosWOS:001285774900001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofStochastic Environmental Research and Risk Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectClimate change
dc.subjectMachine learning algorithms
dc.subjectAdvanced statistical models
dc.subjectEfficiency
dc.titleAssessing the generalization of forecasting ability of machine learning and probabilistic models for complex climate characteristics
dc.typeArticle

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