Artificial neural network (ANN) pricing model for natural rubber products based on climate dependencies

Abstract

International Rubber Study Group report in [1] points out that the world natural rubber consumption continues to increase at an average of 9 per cent per year. Especially, the demands of natural rubber tire industry in developed countries such as the USA, Germany, China and Japan have increased steadily. Tropical countries, such as Indonesia, Malaysia, Thailand and Vietnam, members of the Association of Natural Rubber Producing Countries (ANRPC) accounted for about 92 per cent of the global production of natural rubber in 2010. The market price of natural rubber fluctuates reflecting the variations in supply capacity of these production countries. Therefore, knowledge on the natural rubber supply from these countries is significant in order to have an accurate pricing model of natural rubbers. Moreover, the supply of natural rubber is determined by the climatic conditions in these countries. Rubber trees grow and produce best in warm with an ideal temperature between 21–35 °C, an annual rainfall of 200-300 cm and moistly conditions. In this context, the chapter looks at the dependencies of natural rubber market price especially, the climatic conditions in the production countries, and derives at a natural rubber pricing model to provide farmer information regarding the prediction of market price using an artificial neural network (ANN) based prediction approach.