Implementation of Modified Cuckoo Search Algorithm on Functional Link Neural Network for Climate Change Prediction via Temperature and Ozone Data

Abstract

The effect of climate change presents a huge impact on the development of a country. Furthermore, it is one of the causes in determining planning activities for the advancement of a country. Also, this change will have an adverse effect on the environment such as flooding, drought, acid rain and extreme temperature changes. To be able to avert these dangerous and hazardous developments, early predictions regarding changes in temperature and ozone is of utmost importance. Thus, neural network algorithm namely the Multilayer Perceptron (MLP) which applies Back Propagation algorithm (BP) as their supervised learning method, was adopted for use based on its success in predicting various meteorological jobs. Nevertheless, the convergence velocity still faces problem of multi layering of the network architecture. As consequence, this paper proposed a Functional Link Neural Network (FLNN) model which only has a single layer of tunable weight trained with the Modified Cuckoo Search algorithm (MCS) and it is called FLNN-MCS. The FLNN-MCS is used to predict the daily temperatures and ozone. Comprehensive simulation results have been compared with standard MLP and FLNN trained with the BP. Based on the extensive output, FLNN-MCS was proven to be effective compared to other network models by reducing prediction error and fast convergence rate.