GIS-based evolutionary optimized Gradient Boosted Decision Trees for forest fire susceptibility mapping

Shruti Sachdeva (), Tarunpreet Bhatia and A. K. Verma
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Shruti Sachdeva: Thapar University
Tarunpreet Bhatia: Thapar University
A. K. Verma: Thapar University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2018, vol. 92, issue 3, No 7, 1399-1418

Abstract: Abstract Rampant pasture burning has lead to various forest fires taking their toll over the health of many forests. Nanda Devi Biosphere Reserve, located in the northern part of India, witnessed a majority of these incidents in the recent past, though, it remains comprehensively untouched from research studies. The scale of these wildfires has led to an immense requirement of preventive measures to be taken for recuperating from such events. This requires for an in-depth analysis of the study area, its history of wildfires and their causes. These efforts would assist in laying a blueprint for a contingency plan in the event of a wildfire. This work proposes an evolutionary optimized gradient boosted decision trees for preparing wildfire susceptibility maps for the study area that would aid in the government’s forest preservation and disaster management activities. The study took 18 ignition factors of elevation, slope, aspect, plan curvature, topographic position index, topographic water index, normalized difference vegetation index, soil texture, temperature, rainfall, aridity index, potential evapotranspiration, relative humidity, wind speed, land cover and distance from roads, rivers and habitations into consideration. The study revealed that approximately 1432.025 km2 of area was very highly susceptible to forest fires while 1202.356 km2 was highly susceptible to forest fires. The proposed model was compared against various machine learning models such as random forest, neural networks and support vector machines, and it outperformed them by achieving an overall accuracy of 95.5%. The proposed model demonstrated good prospects for application in the field of hazard susceptibility mappings.

Keywords: Wildfires; Forest fire susceptibility mapping; Gradient Boosted Decision Trees; Evolutionary Optimization; Machine learning; Geographic Information System (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (13)

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DOI: 10.1007/s11069-018-3256-5

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