Main Article Content
Deliberating the importance of rainfall in determining process such as agriculture, flood and water management, these study aim at evaluation of non-linear techniques on seasonal rainfall prediction (SRP). One of the non-linear method widely used is the Artificial Neural Networks (ANN) approach which has the ability of mapping between input and output patterns. The complexity of the atmospheric processes that generate rainfall makes quantitative forecasting of rainfall an extremely, difficult task. The research goal is to train/develop Artificial Neural Network model using backward propagation algorithm to predict seasonal Rainfall. Using some meteorological variables like, sea surface temperature (SST), U-wind at (surface, 700, 850 and 1000), air temperature, specific humidity, ITD and relative humidity. The study adopt monthly June-October (JJASO) rainfall data and January-May (JFMAM) monthly data of SST, U-wind at (surface, 700, 850 and 1000), air temperature, specific humidity and relative humidity for a period of 31 years (1986-2017) over Ikeja. The proposed ANN model architecture (9-4-1) in training the network using back-propagation algorithm indicated that the statistical performance of the model for predicting 2013 to 2017 (JJASO) rainfall amount indicated as follows; MSE, RMSE, and MAE were 7174, 84.7 and 18.6 respectively with a high statistical coefficient of variation of 94% when the ANN model prediction is validated with the observed rainfall. The result indicated that the propose ANN built network is reliable in prediction of seasonal rainfall amount in Ikeja with a minimal error.
Kumar DN, Sathish T. Forecasting hydrologic time series using artificial neural networks. The Math Works, Inc 1984-2009; 2008.
Mekanik F. Rainfall time series modeling for a mountainous region in west Iran. Master Thesis, Universitiy Putra Malaysia; 2011.
Mekanik F, Lee TS, Shitan M. Time series modelling of rainfall in Hamedan. Iran. Proceedings of the Sains Matematik Ke-17, Melaka, Malaysia; 2009.
Ewona IO, Osang JE, Uquetan UI, Inah EO, Udo SO. Rainfall prediction in Nigeria using artificial neural networks. International Journal of Scientific & Engineering Research. 2016;7(1):1157–1169.
French MN, Cuykendall RR, Rainfall forecasting in space and time using a neural network. Journal of Hydrology. 1992;1-31.
Hung NQ, Babel MS, Weesakul S, Tripathi. An artificial neural network model for Rainfall forecasting in Bangkok. Thailand Journal of Hydrology and Earth System Sciences. 2008; 13:1413-14.
Mishra A, Desai V. Drought forecasting using stochastic models. Stochastic Environmental Research and Risk Assessment. 2005;19(5):326-339.
Nourani V, Alami MT, Aminfar MH. A combined neural-wavelet model for prediction of Ligvanchai watershed precipitation. Engineering Applications of Artificial Intelligence. 2009;22(3):466- 472.
Oguntunde PG, Abiodun BJ, lischeid G. Rainfall trends in Nigeria, 1901-2000. J. Hydrol. 2011;411:207-218.
Oladipo EO. A comprehensive approach to drought and desertification in Northern Nigeria. Natural Hazards. 1993;8(3):235-261.
Omotosho JA, Abiodun BJ. A numerical study of moisture build-up and rainfall over West Africa. Meteorol. Appl. 2007;14:209-225.
Olaniran OJ. The problems in the measurement of rainfall: An experiment at Ilorin, Nigeria, Weather. 2002;37(7):201–204.
Esiefarienrhe Bukohwo Michael, Ofikwu Ene Patience. Flood prediction in Nigeria using artificial neural network. American Journal of Engineering Research. 2018;7(9):15-21.
Jackson GA. Public efficiency and private choice in higher education. Educational Evaluation and Policy Analysis. 1982;4(2):237-247.
Oguntoyinbo JS, Areola OO, Filani M. A geography of Nigeria development; 1983.
Shamseldin AY. Application of a neural network technique to rainfall-runoff modeling. J. of Hydrol. 1997;199:272– 294.
Smith J, Eli RN. Neural network models of rainfall-runoff process. Journal of Water Resources Planning and Management. 1995;499-508.
Kunstmann H, Jung G. Impact of regional climate change on water availability in the Volta basin of West Africa. IAHS Publication. 2005;295:75-85.
Shamseldin AY. Application of neural network technique to rainfall-runoff modeling. Journal of Hydrology. 1997;199:272–294.
Sivakumar B, Jayawardena AW, Fernando TMKG. River flow forecasting: Use of phase space reconstruction and artificial neural networks approaches. Journal of Hydrology. 2002;265:225-245.
Odekunle TO. Determining rainy season onset and retreat over Nigeria from precipitation amount and number of rainy days. Theoretical and Applied Climatology. 2006;83(1-4):193-201.
Guhathakurta P. Long lead monsoon rainfall prediction for meteorological sub-divisions of India using deterministic artificial neural network model; 2008.