ON THE PERFORMANCE OF SARIMA AND SARIMAX MODEL IN FORECASTING MONTHLY AVERAGE RAINFALL IN KOGI STATE, NIGERIA
DOI:
https://doi.org/10.33003/fjs-2023-0706-2095Keywords:
SARIMA, SARIMAX, exogenous, ACF, PACF, Rainfall, Temperature, HumidityAbstract
Forecasting monthly rainfall is very important in Kogi state for better approach to flood management and also plays a pivotal role in agriculture which remains a significant factor in Nigeria’s economy. Advanced time series univariate models such as Seasonal Autoregressive Integrated Moving Average (SARIMA) models are usually employed in modelling and forecasting rainfall in Nigeria due to their non-linear pattern and spatiotemporal variation. Few studies have attempted to investigate the influence of other climatic factors in modelling and prediction of rainfall pattern. This study examines the performance of a univariate seasonal ARIMA and seasonal ARIMA which uses monthly temperature and relative humidity as exogenous factors otherwise known as SARIMAX model in forecasting monthly average rainfall in Lokoja, the capital of Kogi state. The study uses monthly data on rainfall, temperature and relative humidity spanning from 2010 to 2022 obtained from Nigeria Meteorological Agency NiMet, Lokoja station. The series were appropriately differenced to attain stationarity. The plots of autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to select some tentative models whose parameters would be estimated. The most suitable SARIMA model [SARIMA was chosen based on maximum Coefficient of Determination , and the minimum Akaike information criterion (AIC). However, SARIMAX model outperformed SARIMA model based on the criteria earlier highlighted. SARIMAX model was therefore recommended for modelling and forecasting monthly average rainfall in Kogi state.
References
Bailey, T. L., Boden, M., Buske, F. A., Frith, M., Grant, C. E., Clementi, L., et al. (2009). MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37(Web Server): 202-8.
Bhandari, S., Poudel, D. K., Marahatha, R., Dawadi, S., Khadayat, K., Phuyal, S., ... & Parajuli, N. (2021). "Microbial Enzymes Used in Bioremediation", Journal of Chemistry. https://doi.org/10.1155/2021/8849512.
Bo H., Jinpu J., An-Yuan G., He Z, Jingchu L. and Ge G. (2015). GSDS 2.0: an upgraded gene features visualization server. Bioinformatics, 31(8):1296- 1297.
Budddolla, V., Bandi R., Avilala J., Arthala P.K. and Golla N. (2014). Fungal Laccases and the Application in Bioremediation. Enzyme Research 201:1-21
Chadha S., Mahetre S.T., Bansal R., Kuo A., Aerts A., Grigoriev I.V., Druzhinina S.I., Mukherjee PK. (2018). Genome-wide analysis of cytochrome P450 of Trichoderma spp.: annotation and evolutionary relationships. Fungal Biol Biotechnol 5(12): 11-12
Chen W., Lee M., Jefcoate C., Kim S, Chen F., Yu J.H., (2014). Fungal Cytochrome P450 Monooxygenases: Their Distribution, Structure, Functions, Family Expansion, and Evolutionary Origin. Genome Biol. Evol. 6(7):1620-1634
Cheng, X., Xiao, X., & Chou, K. C. (2018). pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC. Genomics, 110 (1), 50-58.
Dauda WP, Peter GW, Abraham P, Adetunji CO, Glen E, Daji M, Ogra IO, Shittu EA., Azameti MK, Ghazanfar S, Osemwegie OO, Olaniyan OT, and Anyakudo MMC (2022c). Bioinformatics Based Structural Analysis of Cytochrome P450 genes in Candida tropicalis. Nigerian Journal of Parasitology 43(2): 379-390.
Dauda WP, Abraham P, Fasogbon IV, Adetunji CO, Banwo OO, Kashina BD, Alegbejo MD (2021). Cassava mosaic virus in Africa: Functional analysis of virus coat proteins based on evolutionary processes and protein structure. Gene Reports, p.101239.
Dauda WP, Abraham P, Glen E, Adetunji CO, Ghazanfar S, Ali S, Al-Zahrani M, Azameti MK, Alao SEL, Zarafi AB, et al. (2022a). Robust Profiling of Cytochrome P450s (P450ome) in Notable Aspergillus spp. Life.12, 451.https://doi.org/10.3390/life12030451.
Dauda WP, Morumda D, Abraham P, Adetunji CO, Ghazanfar S, Glen E, Abraham SE, Peter GW, Ogra IO, Ifeanyi UJ, Musa H, Azameti MK, Paray BA, Gulnaz A (2022b). Genome-Wide Analysis of Cytochrome P450s of Alternaria Species: Evolutionary Origin, Family Expansion and Putative Functions. J. Fungi (Basel) 8 (4): 324. doi: 10.3390/jof8040324.
Dauda, W.P., Alao, S.E.L., Zarafi, A.B. and Alabi, O., 2018. First Report of die-back disease of onion (Allium cepa L.) induced by Fusarium equiseti (Mart) Sacc in Nigeria. Inter J Plant Soil Sci, 21, pp.1-8.
Deng J.X., Carbone I., Dean R.A. (2007). The Evolutionary History of Cytochrome P450 Genes in Four Filamentous Ascomycetes. BMC Evol Biol. 7:30
Felsenstein J. (1985). Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39:783-791.
Gao, S., Zeng, R., Xu, L., Song, Z., Gao, P., & Dai, F. (2020). Genome sequence and spore germination-associated transcriptome analysis of Corynespora cassiicola from cucumber. BMC microbiology, 20 (1), 1-20.
Jasu, A., Lahiri, D., Nag, M., & Ray, R. R. (2021). Fungi in bioremediation of soil organic pollutants. In Fungi Bio-Prospects in Sustainable Agriculture, Environment and Nano technology (pp. 381-405). Academic Press.
Jiu, S., Xu, Y., Wang, J., Wang, L., Liu, X., Sun, W., ... & Zhang, C. (2020). The Cytochrome P450 Monooxygenase Inventory of Grapevine (Vitis vinifera L.): Genome-Wide Identification, Evolutionary Characterization and Expression Analysis. Frontiers in genetics, 11, 44.
Keller, N. P., Turner, G., & Bennett, J. W. (2005). Fungal secondary metabolism—from biochemistry to genomics. Nature Reviews Microbiology, 3(12), 937-947.
Kelly D.E., Krasevec N., Mullins J., Nelson D.R. (2009). The Cypone (Cytochrome P450 complement) of Aspergillus nidulans. Fungal Genetics and Biology 46:53-61.
Kevin, D. H., Jianchu X., Sylvie, R., Rajesh, J., Saisamorn, L., Allen G. T. N., Pranami, D.A.… & Marc, S. (2019). The amazing potential of fungi: 50 ways we can exploit fungi industrially. Fungal Diversity 97:1–136
Kumar S., Stecher G., Li M., Knyaz C., and Tamura K. (2018). MEGA X: Molecular Evolutionary Genetics Analysis across computing platforms. Molecular Biology and Evolution 35:1547-1549.
Kuo-Chen C. and Hong-Bin S. (2010). Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms, Natural Science, 2: 1090-1103.
Li Z., Jlang Y., Guengerich F.P., Ma L., Shengying L. and Zhang W. (2020). Engineering cytochrome P450 enzymes systems for biomedical and biotechnological applications.
Li, F., Di, L., Liu, Y., Xiao, Q., Zhang, X., Ma, F., & Yu, H. (2019). Carbaryl biodegradation by Xylaria sp. BNL1 and its metabolic pathway. Ecotoxicology and environmental safety, 167, 331-337.
Matowane, R. G., Wieteska, L., Bamal, H. D., Kgosiemang, I. K. R., Van Wyk, M., Manume, N. A., ... & Syed, K. (2018). In silico analysis of cytochrome P450 monooxygenases in chronic granulomatous infectious fungus Sporothrix schenckii: Special focus on CYP51. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 1866(1), 166-177.
Mockali V., Park J., Fedorova N.D., Park B., Choi J., Lee Y.H., and Kang S., (2012). Systematic and searchable classification of cytochrome P450 proteins encoded by fungal and oomycete genomes. BMC Genomics 13,525
Nelson D.R. (2006). Cytochrome P450 nomenclature, 2004. Methods in Molecular Biology (Clifton, N.J.) 320:1-10
Nelson, D. R., Goldstone, J. V., & Stegeman, J. J. (2013). The cytochrome P450 genesis locus: the origin and evolution of animal cytochrome 450s. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 368(1612), 20120474.
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