Spatiotemporal analysis and intensity prediction of forest ‎fires using cuckoo search hybrid models

Authors

  • Kamal Upreti Department of Computer Science, Christ University, Delhi NCR, Ghaziabad, India
  • Sheela Hundekari School of Computer Applications, Pimpri Chinchwad University, Pune, India
  • Saroj Date Department of Artificial Intelligence and Data Science,‎ CSMSS Chh. Shahu College of Engineering, Chh. Sambhajinagar, Maharashtra, India
  • Akhilesh Tiwari Department of Business and Management , Christ University, Delhi NCR, Ghaziabad, India
  • Ganesh V. Radhakrishnan Department of Economics and Finance, KIIT Univeristy, Bhubaneswar, India
  • Uma Shankar Faculty of Management and Social Sciences, Qaiwan International University Sulaimanyah, Kurdistan, Iraq

How to Cite

Upreti, K., Hundekari , S. ., Date , S. ., Tiwari , A. ., Radhakrishnan, G. V. . . ., & Shankar, U. . (2025). Spatiotemporal analysis and intensity prediction of forest ‎fires using cuckoo search hybrid models. International Journal of Basic and Applied Sciences, 14(1), 181-190. https://doi.org/10.14419/9pk3h895

Received date: April 4, 2025

Accepted date: April 30, 2025

Published date: May 5, 2025

DOI:

https://doi.org/10.14419/9pk3h895

Keywords:

Forest Fire Prediction; Uttara Kannada; Fire Intensity Classification; Spatiotemporal Analysis; Kernel Density Estimation

Abstract

Forest fire forecasting is a critical aspect of environmental conservation and ecological risk management, particularly in ‎biodiversity-sensitive 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 spatio-temporally 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.

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How to Cite

Upreti, K., Hundekari , S. ., Date , S. ., Tiwari , A. ., Radhakrishnan, G. V. . . ., & Shankar, U. . (2025). Spatiotemporal analysis and intensity prediction of forest ‎fires using cuckoo search hybrid models. International Journal of Basic and Applied Sciences, 14(1), 181-190. https://doi.org/10.14419/9pk3h895

Received date: April 4, 2025

Accepted date: April 30, 2025

Published date: May 5, 2025