Investigation of Carbon Dioxide Variations in Selected Points in Nigeria Using Neural Network Model

Atmospheric pollution due to carbon dioxide emission from different fossil fuels and deforestations are considered as a great and important international challenge to the societies. This study is to investigate carbon dioxide (CO2) distributions in selected points in Nigeria using neural network. Neural network model were used to estimate daily values of carbon dioxide, study spatial temporal variations of carbon dioxide, and study the annual variations of estimated and observed carbon dioxide in Nigeria. The study areas used in this work are thirty six (36) points location over Nigeria. The data used in this work is a satellite carbon dioxide () data were obtained from Global Monitoring for Environment and Security (GMES) under the programme of Monitoring Atmospheric Composition And Climate (MACC) www.gmes-atmosphere.eu/data between 2009-2014. The neural network architecture used comprises of three main layers; an input layer, a hidden layer and an output layer. Four input data were considered which include year, day of year (DOY) representing the time, latitude and longitude. Twenty hidden neurons were employed, while the output is the desired data of carbon dioxide. The results show that the increase in trend of CO2 in dry season in every part of the country is on yearly bases. In the wet season, the concentration of CO2 in Nigeria is not as much as in the dry season case, probably due to absorption of the gas by precipitation. The continuous annual increase of CO2 distribution suggests continuous increase of the greenhouse gas in Nigeria. This reveals continuous contribution of CO2 in Nigeria. The similarity in the estimated and observed signatures reveals that neural network model performance were excellent and efficient in determination of spatial distribution of CO2, thereby proving to be useful tool in modeling the greenhouse gases. The results show that neural network model has the capacity of investigating greenhouse gases variations in Nigeria.