Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an...Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires 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 meteorological 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.展开更多
文摘Prediction,prevention,and control of forest fires are crucial on at all scales.Developing effective fire detection systems can aid in their control.This study proposes a novel CNN(convolutional neural network)using an attention blocks module which combines an attention module with numerous input layers to enhance the performance of neural networks.The suggested model focuses on predicting the damage affected/burned areas due to possible wildfires 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 meteorological 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.
