Spatiotemporal analysis and intensity prediction of forest fires using cuckoo search hybrid models
subject: Fire Intensity Classification; Forest Fire Prediction; Kernel Density Estimation; Spatiotemporal Analysis; Uttara Kannada
description: 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.
creator: Upreti, Kamal; Hundekari, Sheela; Date, Saroj; Tiwari, Akhilesh; Radhakrishnan, Ganesh V.; Shankar, Uma
source: International Journal of Basic and Applied Sciences;Volume;14;Issue;1;pp.181-190
publisher: Science Publishing Corporation Inc.
date: 01-01-2025
rights: All Open Access; Gold Open Access; Green Open Access
relation: ISSN: 22275053;
format: online
language: English
type: Article
coverage: 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