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• W4306249917 abstract "Forest fires are a very common in India, especially in the hilly regions of the western and northeastern Himalayas, which puts adverse impacts on the environment and society. Himachal Pradesh and Uttarakhand are the states most prone to forest fires; therefore, this research is intended to map forest fire occurrence in both states using geospatial techniques and machine learning algorithms (MLAs). To fulfill this objective, we used the meteorological data such as evapotranspiration, precipitation, temperature and wind speed data regarding aridity, elevation, slope, aspect, curvature and land use/land cover (LULC) data; and three MLAs: support vector machine (SVM), random forest (RF), and logistic regression (LR) along with an ensemble learning model were used for the modeling of susceptibility of forest fires. This produced forest fire susceptible maps that were finally validated using the global fire atlas of the NASA Earth DATA. The result showed that more than 50% of the area of Uttarakhand and Himachal Pradesh states lies under the moderate, high, and very high forest fire susceptible zones. Further, most of the low hilly and foothill regions of both the states lie under the high and very high forest fire susceptible zones. The validation of the models used was completed using the ROC curve, which showed that the area under curve (AUC) of the ROC (experimental and binormal) was highest for ensemble model, i.e. 0.923 and 0.989, respectively followed by SVM (0.89 and 0.97), RF (0.887 and 0.96), and LR (0.85 and 0.92). This indicates that, although all the models produced satisfactory results, the accuracy of the ensemble model was highest. The output of this research can be utilized for the effective mitigation of forest fires, as well as forest management in the region. Further, similar approaches may be used in other parts of the world for forest fire susceptibility mapping using additional parameters." @default.
• W4306249917 created "2022-10-15" @default.
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• W4306249917 date "2022-10-14" @default.
• W4306249917 modified "2023-10-05" @default.
• W4306249917 title "Forest Fire Susceptibility Mapping by Integrating Remote Sensing and Machine Learning Algorithms" @default.
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• W4306249917 doi "https://doi.org/10.1002/9781119788157.ch9" @default.
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