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Toward Energy-Efficient Deep Neural Networks For Forest Fire Detection In An Image, Yali Wang, Chuulabat Purev, Hunter Barndt, Henry Toal, Jason Kim, Luke Underwood, Luis Avalo, Arghya Kusum Das
Toward Energy-Efficient Deep Neural Networks For Forest Fire Detection In An Image, Yali Wang, Chuulabat Purev, Hunter Barndt, Henry Toal, Jason Kim, Luke Underwood, Luis Avalo, Arghya Kusum Das
The Geographical Bulletin
Forest fires cause huge losses and are a serious problem facing many countries worldwide, including the USA, Canada, Brazil, Siberia, and Indonesia, to name a few. Automatic identification of forest fires in an image is thus an important field to research in order to minimize disasters while also helping in mitigation planning and designing rescue tactics. Artificial Intelligence technologies, especially deep neural networks, have emerged recently with promises to detect fires with better accuracy from an image. However, the massive energy consumption of deep neural networks thwarts their widespread adoption, especially when it comes to onsite detection of fire utilizing …
Predicting Cloud Fraction With Instantaneous Direct And Diffuse Shortwave Solar Irradiance, Jamison Lerma, Emily K. Blackaby, Maosi Chen, Sasha Madronich, Wei Gao
Predicting Cloud Fraction With Instantaneous Direct And Diffuse Shortwave Solar Irradiance, Jamison Lerma, Emily K. Blackaby, Maosi Chen, Sasha Madronich, Wei Gao
The Geographical Bulletin
The purpose of this study is to compare the different performances of three commonly used models (i.e., linear regression, random forest regression, and deep neural network (DNN)) to predict cloud fraction (CF) using ground-based shortwave solar radiation measurements and analyze the importance of the input features. The CF data are obtained from the Surface Radiation Budget (SURFRAD) and the Atmospheric Radiation Measurement (ARM) and the irradiance data from the USDA UV-B Monitoring and Research Program. The study finds that CF of opaque and total clouds can be best predicted using both Random Forest Regression and DNN with the validation R2 …