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As the access to this document is restricted, you may want to search for a different version of it. Taylor, Full references including those not matched with items on IDEAS Most related items These are the items that most often cite the same works as this one and are cited by the same works as this one. Huiyun Zhu, Sonja I. Josephine D. Sumera, Janice J. Statistics Access and download statistics Corrections All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcsosc:vyid See general information about how to correct material in RePEc. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about. If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form. If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the 'citations' tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation. For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing email available below. Please note that corrections may take a couple of weeks to filter through the various RePEc services. Economic literature: papers , articles , software , chapters , books. My bibliography Save this article. Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting. The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling. Handle: RePEc:spr:jcsosc:vyid Most related items These are the items that most often cite the same works as this one and are cited by the same works as this one. Statistics Access and download statistics. Corrections All material on this site has been provided by the respective publishers and authors. Help us Corrections Found an error or omission? RePEc uses bibliographic data supplied by the respective publishers.
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