Abstract
Modelling and predicting rainfall in research are essential because the inferences from the study will assist decision-makers, planners, and policymakers in mitigating the effects of drought or flooding in the environment. This chapter aims to fit time series models to rainfall data from seven states in the Nigerian north-central region. The data used for this research was obtained from NIMET (Jan 1989–Dec 2019). The rainfall data set was modelled and predicted using the conventional seasonal Autoregressive Integrated Moving Average (ARIMA) and Neural Network Times Series Autoregressive (NNETAR) models. The time plot sequence shows the time series data is stationary, and the Augmented Dick Fuller (ADF) test did not suggest otherwise. Furthermore, the Hegy and Canova-Hansen tests indicate seasonality in the data with order 1. When the ARIMA and NNETAR models were applied to the rainfall data set, the analysis revealed that the NNETAR model outperformed the ARIMA model in modelling and predicting the Ilorin, Jos, Lafia, Lokoja, and Minna rainfall data sets. In contrast, the ARIMA model outperformed the NNETAR model for predicting rainfall in Abuja and Makurdi. The fitted models were used to predict monthly rainfall in the north-central region for the next five years. The forecast suggests an expected increase in rainfall in Lafia, Abuja, and Minna. At the same time, an expected decrease in rainfall in Ilorin, Lokoja, Jos, and Makurdi states in the north-central region of Nigeria.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Akinbobola A, Okogbue EC, Ayansola AK (2018) Statistical modeling of monthly rainfall in selected stations in forest and savannah eco-climatic regions of Nigeria. J Climatol Weather For 2018(6), S1. https://doi.org/10.4172/2332-2594.1000226
Bjornlund V, Bjornlund H, Van Rooyen AF (2020) Why agricultural production in sub-Saharan Africa remains low compared to the rest of the world–a historical perspective. Int J Water Resour Dev 36(sup1):S20–S53. https://doi.org/10.1080/07900627.2020.1739512
Box G, Jenkins G (1970) Time series analysis: forecasting and control
Darji M (2019) Rainfall forecasting using neural networks
Di C, Yang X, Wang X (2014) Hybrid neural network models for hydrologic time series forecasting. Plus One 9:e104663
Dwivedi DK, Kelaiya JH, Sharma GR (2019) Forecasting monthly rainfall using autoregressive integrated moving average model (ARIMA) and artificial neural network (ANN) model: a case study of Junagadh, Gujarat, India. J Appl Natl Sci 11(1), 35–41. https://doi.org/10.31018/jans.v11i1.1951
Grigonyte E, Butkeviciuye E (2016) Short-term wind speed foresting using ARIMA model. Energetika, 62
Ikot (2021) The environment and social and economic development nigerian aluminium smelter company. https://alscon.net/ikot-abasi-smelter/78-the-environment-and-socio-economic-development.html
Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput 7:585–592
Kajuru J, Abdulkarim K, Muhammed M (2019) Forecasting performance of arima and arima models on monthly average temperature of zaria, Nigeria. ATBU J Sci Technol Educ 7(3):205–212. https://www.atbuftejoste.com/index.php/joste/article/view/823
Li Y, Dzombak DA (2020) Use of the autoregressive integrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather For 35(3):959–976
Momani PE (2009) Time series analysis model for rainfall data in Jordan: case study for using time series analysis. Am J Environ Sci 5https://doi.org/10.3844/ajessp.2009.599.604
Mushtaq, Rizwan (August 17, 2011) Augmented Dickey-Fuller Test. SSRN: https://ssrn.com/abstract=1911068 or https://doi.org/10.2139/ssrn.1911068
Nyong A, Adesina F, Osman Elasha B (2007) The value of indigenous knowledge in climate change mitigation and adaptation strategies in the African Sahel. Mitig Adapt Strategy Glob Change 12:787 797
Somvanshi VK et al (2006) Modelling and prediction of rainfall using artificial neural network and ARIMA techniques. J Ind Geophys Union 10(2):141–151
Pal S, Mazumdar D (2019) Forecasting monthly rainfall using an artificial neural network 3:65–73
Ray S, Das SS, Mishra P, Al Khatib AMG (2021) Time series ARIMA modeling and forecasting of monthly rainfall and temperature in the south Asian countries. Earth Syst Environ 5(3):531–546.
Shad M, Sharma YD, Singh A (2022). Forecasting of monthly relative humidity in Delhi, Indian using ARIMA and ANN models. Modeling Earth Systems and Environment. https://doi.org/10.1007/s.40808-022-01385-8
Sunil S, Acharya S, Jogi AK (2019) Application of hybrid model for forecasting prices of jasmine flower in Bangalore, India. Int J Sci Technol Res 8(11)
Tadesse D (2010) The impact of climate change in Africa. Inst Secur Stud Ser 220(November):20. http://www.issafrica.org/uploads/Paper220.pdf %5Cn
Tuğrul KM (2019) Soil management in sustainable agriculture, sustainable crop production, Mirza Hasanuzzaman, Marcelo Carvalho Minhoto Teixeira Filho, Masayuki Fujita and Thiago Assis Rodrigues Nogueira, IntechOpen, https://doi.org/10.5772/intechopen.88319. https://www.intechopen.com/chapters/68683
Wang W, Van Gelder, Vrijling J, Ma J (2006) Forecasting daily sstreamflow using hybrid ANN models. J Hydrol 324:383–399
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Chukwueloka, E.H., Nwosu, A.O. (2023). Modelling and Prediction of Rainfall in the North-Central Region of Nigeria Using ARIMA and NNETAR Model. In: Egbueri, J.C., Ighalo, J.O., Pande, C.B. (eds) Climate Change Impacts on Nigeria. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-031-21007-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-21007-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21006-8
Online ISBN: 978-3-031-21007-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)