Integrating Geographical Information Systems and Artificial Neural Networks to improve spatial decision making

Abstract

GIS has been used in Veterinary Science for a couple of year and the application thereof has been growing rapidly. A number of GIS models have been developed to predict the occurrences of certain types of insect species including the Culicoides species (spp), the insect vectors responsible for the transmission of the African horse sickness (AHS) virus. AHS is endemic to sub-Saharan Africa and is carried by two midges called Culicoides Imicola and Culicoides Bolitinos. The disease causes severe illness in horses and has significant economic impact if not dealt with timeously. Although these models had some success in the prediction of possible abundance of the Culicoides spp. the complicated nature and high number of variables influencing the abundance of Culicoides spp. posed some challenges to these GIS models. This informs the need for models that can accurately predict potential abundance of Culicoides spp to prevent unnecessary horse deaths. This lead the study to the use of a combination of a GIS and an artificial neural networks (ANN) to develop a model that can predict the abundance of C. Imicola and C. Bolitinos. ANNs are models designed to imitate the human brain and have the ability to learn through examples. ANNs can therefore model extremely complex features. In addition, using GIS maps to visualise the predictions will make the models more accessible to a wider range of practitioners.