Climate Variability Forecasting Using Bat Algorithm Optimised Artificial Neural Network.
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
Zimbabwe Journal of Science & Technology
Abstract
This paper presents a summary and results of a study that was conducted in an attempt to forecast climate
variability in Zimbabwe using the BAT Algorithm optimised Artificial Neural Network (BAT-ANN) analysis
technique. Forecasts of climate ahead of time can potentially allow governments, farmers and other players
in private and/or public sectors to make decisions to reduce unwanted impacts or take advantage of expected
favourable climate. However, potential benefits of climate forecasts vary considerably because of many physical,
biological, economic, social, and political factors. In a developing country, like Zimbabwe where agriculture
is the base of the national economy, climate conditions play leading role for progressive and sustainable
development, therefore climate variability forecasts are very important. The BAT-ANN was adapted and tested
using the Zimbabwean meteorological dataset and results confirm that our proposed model has the potential
for reliable climate forecasting for a 25 year period. The mean percentage accuracy was used to evaluate the
performance of the trained climate forecasting neural network and proved sufficient. Therefore, in this paper,
we present a new technique to climate variability assessment namely; the BAT-ANN. In this study, the approach
employed to achieve objectives was; collecting quantitative data, adapting a BAT-ANN for analysis, and developing
a Java program that employs the BAT-ANN for forecasting. The objectives of the study were met.
Description
Journal article
Keywords
BAT Algorithm, Climate Variability, Artificial Neural Network, Network Optimisation, Forecasting.
Citation
Mzelikahle, K., Chilumani, K. R. and Kokera, N. 2015. Climate Variability Forecasting Using Bat Algorithm Optimised Artificial Neural Network. Zimbabwe Journal of Science & Technology, 10[2015]: 54 - 68