Prediction of Peat Forest Fires Using Wavelet and Backpropagation

Abstract

One of the causes of smog as well as climate damage, particularly in Palangka Raya, Center Kalimantan, are peat forest fires. There are a lot of losses inflicted by the smog including the increasing number of people who suffer respiratory infection (ARI) due to polluted air and any other related aspects. Peat fires are problematic to overcome because the locations of fires are difficult to be accessed. This paper focuses on building the system to predict the distribution of peat forest fire hotspots by utilizing satellite imagery. In designing the system for predicting the fire hotspots distribution, wavelet orthogonal was used as the initial processing of mapping the distribution of peat forest fire hotspots. Meanwhile, backpropagation method was used to identify the fire hotspot distribution patterns of peat forest fire in this system. From the result of the data tested which had been done for predicting the peat forest fire hotspots, the decomposition image obtained using Haar wavelet had the highest percentage of accuracy to recognize the fire hotspots, which is 90%. The recency of this system was its ability to predict the peat forest fire hotspots distribution which can be used as peat forest fires prevention, especially in Palangka Raya, Central Kalimantan.