EVALUATIONOF SPECIFIC HUMIDITY OVER NIGERIA USING ARTIFICIAL NEURAL NETWORK

Abstract

In this research, neural network–based autoregressive moving average with exogenous inputs (NNARMAX) and autoregressive moving average with exogenous inputs (ARMAX) models were used to analyse specific humidity (q) data calculated for ten years (1999-2008) from daily meteorological data (temperature and relative humidity) obtained from the archives of Nigeria Meteorological Agency NIMET, Oshodi Lagos, Nigeria. The results showed that the two models captured and predicted specific humidity (q) accurately at 9 hours and 15 hours respectively over sixteen (16) selected stations in four (4) regions across Nigeria. The prediction of the training data and validation data of the two models showed that the NNARMAX model gave better prediction of q than ARMAX model. For example, over Coastal region in Lagos the NNARMAX model gave lower MSE values for training data of (0.0007 & 0.0004) for 9 hours and 15 hours respectively and for validation data of (0.0001 & 0.0003) compared to the ARMAX model MSE values for training data (0.2396 & 0.2492) and for validation data of (0.0242 & 0.0136) for 9 hours and 15 hours respectively while over Guinea Savannah region in Ibadan the NNARMAX model gave lower MSE values for training data of (0.0219 & 0.0001) and for validation data of (0.0008 & 0.0005) compared to the ARMAX model MSE values for training data (0.3478 & 0.4188) and for validation data of (0.0154 & 0.0198). Also, over Mid-land region in Abuja the NNARMAX model gave lower MSE values for training data of (0.0021 & 0.0008) and for validation data of (0.0011 & 0.0001) compared to the ARMAX model MSE values for training data (0.3465 & 0.3502) and for validation data of (0.0183 & 0.0205) while over Sahel region in Kaduna the NNARMAX model gave lower MSE values for training data of (0.0001 & 0.0003) and for validation data of (0.0001 & 0.0003) compared to the ARMAX model MSE values for training data (0.2944 & 0.3164) and for validation data of (0.0859 & 0.0907). This clearly showed that the NNARMAX model yielded better results than the ARMAX model with lower prediction errors. KEYWORDS: Weather Forecasting, Artificial Neural Networks, ARMAX model, time series.