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 Articles Article Faculty Publications -Articles 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 Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model Subject The topic of the resource forest fire; long short term memory; modified firefly algorithm; variational autoencoder; waterwheel plant optimization Description An account of the resource A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license. Creator An entity primarily responsible for making the resource Aluri Y.K.; Devi V.S.A.; Chinthakunta M.; Manur M.; Jayapal P.K.; Balan G.; Lella K.K. Source A related resource from which the described resource is derived International Journal of Safety and Security Engineering, Vol-14, No. 2, pp. 329-340. Publisher An entity responsible for making the resource available International Information and Engineering Technology Association Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.18280/ijsse.140202" target="_blank" rel="noreferrer noopener">https://doi.org/10.18280/ijsse.140202</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191979067&amp;doi=10.18280%2Fijsse.140202&amp;partnerID=40&amp;md5=da5ab8e8345813967a68be7e906ffac2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191979067&amp;doi=10.18280%2fijsse.140202&amp;partnerID=40&amp;md5=da5ab8e8345813967a68be7e906ffac2</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 20419031 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 Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Aluri Y.K., Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, 520007, India; Devi V.S.A., Department of Chemistry, New Horizon College of Engineering, Bengaluru, 560103, India; Chinthakunta M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Jayapal P.K., DiSTAP, Singapore-MIT Alliance for Research and Technology, Singapore, 138602, Singapore; Balan G., Department of Electrical and Electronics Engineering, New Horizon College of Engineering, Bengaluru, 560103, India; Lella K.K., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India