and k-Fold Cross-Validation
Authors: Nina Kurnia Hikmawati; Yudi Ramdhani; Doni Purnama Alamsyah
DOI: 10.1109/ICIC64337.2024.10956756
Abstract
Preventing Forest fires by identifying events based on specific criteria using the Neural Network (NN) algorithm. To ensure result stability, test and training data are divided using k-fold cross-validation with variations k-5, k-10, and k-20. The NN algorithm performed significantly better after being optimized with Particle Swarm Optimization (PSO), with the average accuracy value going from 97% to 98.78% and the AUC value increasing from 0.997 to 0.999. A weight study of PSO reveals variability in values for each attribute, with Day and Month having no significant effect on classification. The results of the Paired Two-sample t-test for Means show that using PSO improves accuracy significantly. The merging of PSO with Neural Networks has a considerable positive influence on forest fire prediction and is suggested for applications requiring high accuracy. © 2024 IEEE.
Author keywords
Forest Fires; Machine Learning; Neural Networks; Particle Swarm Optimization
Indexed keywords
Engineering controlled terms
Stability criteria
Engineering uncontrolled terms
Forest fire detection; Forest fires; K fold cross validations; Machine-learning; Neural networks algorithms; Neural-networks; Particle swarm; Particle swarm optimization; Particle swarm optimization-neural networks; Swarm optimization
Engineering main heading
Particle swarm optimization (PSO)
This article can be accessed at https://www.scopus.com/pages/publications/105004579202







