Forecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeria

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dc.authoridMuhammad, Ahmad/0000-0003-3886-7956en_US
dc.authoridVarol, Asaf/0000-0003-1606-4079en_US
dc.authoridDanbatta, Salim Jibrin/0000-0002-8913-5766en_US
dc.contributor.authorDanbatta, Salim Jibrin
dc.contributor.authorMuhammad, Ahmad
dc.contributor.authorVarol, Asaf
dc.contributor.authorAbdurrahaman, Daha Tijjani
dc.date.accessioned2024-07-12T21:37:43Z
dc.date.available2024-07-12T21:37:43Z
dc.date.issued2024en_US
dc.department[Belirlenecek]en_US
dc.description.abstractNigeria's agricultural sector relies heavily on rainfall, but insecurity in various regions poses significant challenges. This study aims to address this issue by identifying secure, rain-rich areas in northern Nigeria to support sustainable agriculture. Two models, one integrating classical statistical methods (polynomial and Fourier series fittings) and another using a hybrid approach (artificial neural networks, polynomial, and Fourier series fittings), were employed to analyze historical rainfall data from 1981 to 2021 in the selected districts (Kano, Zaria, Bida, Nguru, and Yelwa) known for their rainfall levels and security stability. The study demonstrates that the machine learning-classical hybrid model outperforms existing models, including the classical-classical hybrid and benchmark models like Iwok's (2016) model, Fourier series, and SARIMA models. Multi-step ahead forecasting with this hybrid model reveals potential changes in rainfall patterns. Notably, Kano, Zaria, Bida, and Yelwa are expected to experience increased rainfall from 2022 to 2026, while Nguru may initially witness decreased rainfall, with improvement in the final year (2026). In conclusion, this study introduces an effective approach for rainfall modeling and forecasting, facilitating the identification of secure agricultural regions in northern Nigeria. These findings carry implications for crop production and agricultural development, contributing to climate resilience efforts and assisting stakeholders in strategic decision-making for regional agricultural investments.en_US
dc.description.sponsorshipUskudar Universityen_US
dc.description.sponsorshipWe express our gratitude to the Central Bank of Nigeria for providing open access to the data used in this research study.en_US
dc.identifier.doi10.1007/s10668-024-04516-6
dc.identifier.issn1387-585X
dc.identifier.issn1573-2975
dc.identifier.scopus2-s2.0-85184254964en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s10668-024-04516-6
dc.identifier.urihttps://hdl.handle.net/20.500.12415/6906
dc.identifier.wosWOS:001156358400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEnvironment Development And Sustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKY04248
dc.subjectHybrid Modelingen_US
dc.subjectAgricultural Sustainabilityen_US
dc.subjectRainfall Forecastingen_US
dc.subjectClimate Resilienceen_US
dc.titleForecasting monthly rainfall using hybrid time-series models and Monte Carlo simulation amidst security challenges: a case study of five districts from northern Nigeriaen_US
dc.typeArticle
dspace.entity.typePublication

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