Abstract
Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detec-tion systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an atten-tion blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predict-ing the damage affected/burned areas due to possible wild-fires and evaluating the multilateral interactions between the pertinent factors.The results show the impacts of CNN using attention blocks for feature extraction and to better understand how ecosystems are affected by meteorologi-cal factors.For selected meteorological data,RMSE 12.08 and MAE 7.45 values provide higher predictive power for selecting relevant and necessary features to provide optimal performance with less operational and computational costs.These findings show that the suggested strategy is reliable and effective for planning and managing fire-prone regions as well as for predicting forest fire damage.
维普
万方数据