Wildfire Predictions: Determining Reliable Models using Fused Dataset

Authors

  • Hariharan Naganathan

  • Sudarshan P Seshasayee

  • Jonghoon Kim

  • Jui-Sheng Chou

  • Wai K Chong

Keywords:

support vector machines, k-nearest neighbor, k-fold cross-validation, decision tree stumps, forest fire, binary and multiclass classifiers

Abstract

Wildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U S While predictive models are faster and accurate it is still important to identify the right model for the data type analyzed The paper aims at understanding the reliability of three predictive methods using fused dataset Performances of these methods Support Vector Machine K-Nearest Neighbors and decision tree models are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity Data extracted from meteorological database and U S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused dataset

How to Cite

Hariharan Naganathan, Sudarshan P Seshasayee, Jonghoon Kim, Jui-Sheng Chou, & Wai K Chong. (2016). Wildfire Predictions: Determining Reliable Models using Fused Dataset. Global Journal of Computer Science and Technology, 16(C4), 29–40. Retrieved from https://computerresearch.org/index.php/computer/article/view/1437

Wildfire Predictions: Determining Reliable Models using Fused Dataset

Published

2016-07-15