Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Forest Fire Prediction Using Machine Learning and Deep Learning Techniques Subject The topic of the resource Deep learning; Fire; Flask implementation; Forests; Machine learning; Prediction Description An account of the resource Forests are considered synonyms for abundance on our planet. They uphold the lifecycle of a diversity of creatures, including mankind. Destruction of such forests due to environmental hazards like forest fires is disastrous and leads to loss of economy, wildlife, property, and people. It endangers everything in its vicinity. Sadly, the presence of flora and fauna only increase the fire spread capability and speed. Early detection of these forest fires can help control the spread and protect the nearby areas from the damage caused. This research paper aims at predicting the occurrence of forest fires using machine learning and deep learning techniques. The idea is to apply multiple algorithms to the data and perform comparative analysis to find the best-performing model. The best performance is obtained by the decision tree model for this work. It gave an accuracy of 79.6% and a recall score of 0.90. This model was then implemented on front-end WebUI using the flask and pickle modules in Python. The front-end Website returns the probability that a forest fire occurs for a set of inputs given by the user. This implementation is done using the PyCharm IDE. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. Creator An entity primarily responsible for making the resource Shreya M.; Rai R.; Shukla S. Source A related resource from which the described resource is derived Lecture Notes on Data Engineering and Communications Technologies, Vol-141, pp. 683-694. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-19-3035-5_51" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-19-3035-5_51</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140262300&amp;doi=10.1007%2F978-981-19-3035-5_51&amp;partnerID=40&amp;md5=6a89df26826aa8206e943869c8f4f467" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140262300&amp;doi=10.1007%2f978-981-19-3035-5_51&amp;partnerID=40&amp;md5=6a89df26826aa8206e943869c8f4f467</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 23674512 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shreya M., Department of Data Science, Christ University, Bangalore, India; Rai R., Department of Data Science, Christ University, Bangalore, India; Shukla S., Department of Data Science, Christ University, Bangalore, India