Forecasting wildfires in major forest types of India

Author:

Kale Manish P.,Mishra Asima,Pardeshi Satish,Ghosh Suddhasheel,Pai D. S.,Roy Parth Sarathi

Abstract

Severity of wildfires witnessed in different parts of the world in the recent times has posed a significant challenge to fire control authorities. Even when the different fire early warning systems have been developed to provide the quickest warnings about the possible wildfire location, severity, and danger, often it is difficult to deploy the resources quickly to contain the wildfire at a short notice. Response time is further delayed when the terrain is complex. Early warning systems based on physics-based models, such as WRF-FIRE/SFIRE, are computationally intensive and require high performance computing resources and significant data related to fuel properties and climate to generate forecasts at short intervals of time (i.e., hourly basis). It is therefore that when the objective is to develop monthly and yearly forecasts, time series models seem to be useful as they require lesser computation power and limited data (as compared to physics-based models). Long duration forecasts are useful in preparing an efficient fire management plan for optimal deployment of resources in the event of forest fire. The present research is aimed at forecasting the number of fires in different forest types of India on a monthly basis using “Autoregressive Integrated Moving Average” time series models (both univariate and with regressors) at 25 km × 25 km spatial resolution (grid) and developing the fire susceptibility maps using Geographical Information System. The performance of models was validated based on the autocorrelation function (ACF), partial ACF, cumulative periodogram, and Portmanteau (L-Jung Box) test. Both the univariate- and regressor-based models performed equally well; however, the univariate model was preferred due to parsimony. The R software package was used to run and test the model. The forecasted active fire counts were tested against the original 3 years monthly forecasts from 2015 to 2017. The variation in coefficient of determination from 0.94 (for year 1 forecast) to 0.64 (when all the 3-year forecasts were considered together) was observed for tropical dry deciduous forests. These values varied from 0.98 to 0.89 for tropical moist deciduous forest and from 0.97 to 0.88 for the tropical evergreen forests. The forecasted active fire counts were used to estimate the future forest fire frequency ratio, which has been used as an indicator of forest fire susceptibility.

Publisher

Frontiers Media SA

Subject

Nature and Landscape Conservation,Environmental Science (miscellaneous),Ecology,Global and Planetary Change,Forestry

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3