Hybrid neural network extreme learning machine and flower pollination algorithm to predict fire extensions on Kalimantan Island

Abstract

The forest and land fires are one of the environmental problems that often occur in several regions of Indonesia, one of which is the Kalimantan Island. Ecologically, forest fires result in loss of nutrients, low soil infiltration, and high erosion. Therefore, rehabilitation needs to be done to improve and re-increase land productivity after a forest fire. One effort that can be done in preparing for the rehabilitation process is to predict the extent of forest fires. The purpose of this study is to predict the extent of fires on the island of Kalimantan using a hybrid artificial neural network method of extreme learning machine (ELM) and flower pollination algorithm (FPA). The flower pollination algorithm is one of the algorithms used in optimization problems. In the Extreme Learning Machine training process, this algorithm plays a role in optimizing the weight so that the optimal weight is obtained. The optimal weight used to predict is the best interest value (GBest) flower pollination algorithm. The stages of predicting the area of fire on the island of Kalimantan use the flower pollination algorithm and extreme learning machine, including data normalization, training process, validation test process, data denormalization, and error value calculation using mean square error (MSE). Based on the training process, obtained the smallest MSE of 0.016753377 with the MSE validation test of 0.017989928.