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doi:10.3808/jei.201800404
Copyright © 2017 ISEIS. All rights reserved

A Hybrid Intelligence System Based on Relevance Vector Machines and Imperialist Competitive Optimization for Modelling Forest Fire Danger Using GIS

H. V. Le1, Q. T. Bui2, D. Tien Bui3,4,*, H. H. Tran5, and N. D. Hoang6

  1. Faculty of Information Technology, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam
  2. Faculty of Geography, VNU University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi, Vietnam
  3. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  4. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam
  5. Faculty of Geomatics and Land Administration, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi, Vietnam
  6. Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809 - K7/25 Quang Trung, Danang, Vietnam

*Corresponding author. Tel.: +47 35952724; fax: +47 35952601. E-mail address: buitiendieu@tdtu.edu.vn (D. Tien Bui).

Abstract


This article proposes and verifies a novel intelligence approach for modelling forest fire danger, namely ICA-RVM, developed based on Relevance Vector Machine (RVM) and Imperialist Competitive Algorithm (ICA), state-of-the art machine learning techniques that have not been investigated for forest fire danger modeling. RVM is used to establish a prediction model that computes probability of fire danger, whereas ICA is adopted to optimize the prediction model. The tropical forest at Gia Lai province, Central Highland (Vietnam), was used as a case study. Area under the curve (AUC) and statistical measures were used to assess the model performance. The result showed that the proposed model achieves high performances; AUC is 0.842 and 0.793 on the training dataset and the validation dataset, respectively. Compared to two benchmarks, Random Forests and Support Vector Machine, the proposed model performs better. Therefore, the propose ICA-RVM is a valid alternative system for forest fire danger modeling.

Keywords: forest fire; Imperialist Competitive Algorithm; Relevance Vector Machines; GIS; Gia Lai; Vietnam


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