Monthly rainfall prediction using artificial neural network: A case study of Kano, Nigeria

Monthly rainfall prediction using artificial neural network: A case study of Kano, Nigeria

Ahmad A. BelloMustapha B. Mamman 

Nigerian Meteorological Agency, National Weather Forecasting and Climate Research Center, Abuja 900421, Nigeria

Corresponding Author Email:
21 April 2018
8 June 2018
30 June 2018
| Citation



Rainfall continues to be the major source of moisture for agricultural activities over Nigeria, therefore accurate and timely rainfall prediction is essential for food availability and improved water resources management over this region. In this study, Artificial Neural Network (ANN) was applied to predict monthly rainfall over Kano, Nigeria. Three months lagged climate indices for monitoring El Niño–Southern Oscillation (ENSO) namely; Southern Oscillation Index (SOI), Niño1+2, Niño3, Niño3.4 and Niño4 monthly values for 37 years were used as predictors. A Linear Model (LM) was first developed to serve as a yardstick. The ANN was trained using neuralnet package in R statistical software, 25 years data (1981-2005) was used for model training while the remaining 12 years data (2006-2017) was used for model evaluation. Results indicated that both ANN and LM replicated the actual pattern of monthly rainfall, although with some disparities. ANN has a correlation coefficient value of 0.73 which is higher than 0.70 recorded by LM, a lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also observed for ANN as compared with LM. Therefore indicating ANN is more preferable and could confidently be used with ENSO indices for subsequent monthly rainfall prediction over Kano, Nigeria.


artificial neural network, monthly rainfall, climate indices, El niño–southern oscillation

1. Introduction
2. Study Area
3. Materials and Methods
4. Results and Discussion
5. Conclusions

[1] National Bureau of Statistics. (2017). Nigerian Gross Domestic Product Report.

[2] Abbot J, Marohasy J. (2015). Improving monthly rainfall forecasts using artificial neural networks and single-month optimisation: A case study of the Brisbane catchment, Queensland, Australia. Water Resources Management VIII: WIT Transactions on Ecology and the Environment, 3–13. 

[3] Abbot J, Marohasy J. (2017). Skilful rainfall forecasts from artificial neural networks with long duration series and single-month optimisation. Atmospheric Research 197: 289-299.

[4] Shukla RP, Tripathi KC, Pandey AC, Das IML. (2011). Prediction of Indian summer monsoon rainfall using Niño indices: A neural network approach. Atmospheric Research 102: 99–109.

[5] Diattaa S, Fink HA. (2014). Statistical relationship between remote climate indices and West African monsoon variability. Int. J. Climatol 34: 3348–3367.

[6] Salau OR, Fasuba A, Aduloju KA, Adesakin GE, Fatigun AT. (2016). Effects of Changes in ENSO on Seasonal Mean Temperature and Rainfall in Nigeria. Climate 4: 5.

[7] 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 Forecasting 6: 226.

[8] Egbuawa OI, Anyanwu JC, Amaku GE, Onuoha IC. (2017). Assessment of the teleconnection between El Nino southern oscillation (ENSO) and West African rainfall. AFRREV 11(4).

[9] Adeniyi MO, Dilau KA. (2015). Seasonal prediction of precipitation over Nigeria. Journal of Science and Technology 35(1): 102-113.

[10] Ewona IO, Osang JE, Uquetan UI, Inah EO, Udo SO. (2016). Rainfall prediction in Nigeria using artificial neural networks. International Journal of Scientific & Engineering Research 7(1).

[11] Abdulkadir TS, Salami AW, Aremu AS, Ayanshola AM, Oyejobi DO. (2017). Assement of neural networks performance in modelling rainfall amounts. Journal of Research in Forestry, Wildlife & Environment 9(1).