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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Upreti, Kamal; Hundekari, Sheela; Date, Saroj; Tiwari, Akhilesh; Radhakrishnan, Ganesh V.; Shankar, Uma Title A name given to the resource Spatiotemporal analysis and intensity prediction of forest fires using cuckoo search hybrid models Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.181-190 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14419/9pk3h895" target="_blank" rel="noreferrer noopener">https://doi.org/10.14419/9pk3h895</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105005538496?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105005538496?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Upreti K., Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India; Hundekari S., School of Computer Applications, Pimpri Chinchwad University, Pune, India; Date S., Department of Artificial Intelligence and Data Science, CSMSS Chh. Shahu College of Engineering, Chh. Sambhajinagar, Maharashtra, India; Tiwari A., Department of Business and Management, Christ University, Delhi NCR, Ghaziabad, India; Radhakrishnan G.V., Department of Economics and Finance, KIIT Univeristy, Bhubaneswar, India; Shankar U., Faculty of Management and Social Sciences, Qaiwan International University, Kurdistan, Sulaimanyah, Iraq Description An account of the resource Forest fire forecasting is a critical aspect of environmental conservation and ecological risk management, particularly in biodiversitysensitive areas like Uttara Kannada, India. In this research, this article suggests a new hybrid modeling ap-proach that combines Cuckoo Search Optimization (CSO) with ensemble machine learning techniques, namely Random Forest (RF) and XGBoost (XGB), for forecasting fire intensity levels. Known as CSORF and CS-XGB, the hybrid models were trained and validated against a spatiotemporally dense dataset from 2009 to 2024, with primary environmental, topographic, and anthropogenic predictors. Aside from classification modeling, spatiotemporal analyses such as Kernel Density Estimation (KDE), seasonal fire patterns, and influence studies on features were performed to determine high-risk seasons and areas. CSO was used to automate the hyperparameter tuning process for both classifiers, yielding a significant boost in performance. The CS-XGB model registered the top accuracy of 99.49%, better than CSORF's 98.99%. Feature importance testing confirmed ecological significance, and humidity, temperature, and rainfall were the top-ranked variables. The work adds a scalable and precise prediction model that can assist in early warning systems and forest manage-ment practices. Kamal Upreti et al. Subject The topic of the resource Fire Intensity Classification; Forest Fire Prediction; Kernel Density Estimation; Spatiotemporal Analysis; Uttara Kannada Publisher An entity responsible for making the resource available Science Publishing Corporation Inc. Relation A related resource ISSN: 22275053; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online