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• W4328100434 abstract "In the hilly region of the Western Himalayas, forest fires play a crucial role in forest destruction and biodiversity loss. Therefore, addressing the problem of forest fires is an immediate priority to mitigate the impact of forest fires. Therefore, the objective of this research is to assess and map the susceptibility zones of forest fire in the hilly state of Uttarakhand using geospatial techniques and machine learning algorithms (MLA) and deep Learning based sensitivity and uncertainty analysis of the forest fire ignition parameters. Thirteen forest fire ignition parameters were used to map forest fire susceptibility. Further, we used five distinct MLAs, including artificial neural networks (ANN), random forest (RF), logistic regression (LR), support vector machine (SVM) and an ensemble machine learning model. The generated models were validated using the receiver operating characteristic (ROC). The result shows that the ''very high'' and ''high'' zones for forest fires cover almost 55% of the total area of Uttarakhand. Validation of the models used indicated that all models produced most significant results. The ensemble machine learning had the highest accuracy (AUC = 0.977), followed by RF (0.973) and ANN (0.972). In addition, a Deep Neural Networks (DNN) based sensitivity approach is proposed to perform sensitivity and uncertainty analysis of the ignition parameters. For this, we have selected the best predictive model (Ensemble Machine Learning) to evaluate the sensitivity and uncertainty analysis. The sensitivity analysis shows that evapotranspiration has the highly influencing parameter to forest fires, followed by annual rainfall, wind speed, distance to agriculture, distance to tourist spots, distance to built-up and distance to road. However, aspect, temperature and distance to religious centers have less influence on susceptibility to forest fires in this study area. This study is unique because it provides new insights into the construction of an ensemble machine learning model and the integration of DNN-based sensitivity and uncertainty for robust and scientific modelling of forest fire susceptibility. The contribution of parameters demonstrates a novel approach in understanding the impact of a single parameter sample on forest fire susceptibility, which will help in developing prevention strategies. A similar approach may be used in other parts of the world with similar natural and anthropogenic conditions for mapping forest fire susceptibility." @default.
• W4328100434 created "2023-03-22" @default.
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• W4328100434 date "2023-07-01" @default.
• W4328100434 modified "2023-09-26" @default.
• W4328100434 title "Forest fire susceptibility mapping with sensitivity and uncertainty analysis using machine learning and deep learning algorithms" @default.
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• W4328100434 cites W2036063587 @default.
• W4328100434 cites W2046841239 @default.
• W4328100434 cites W2071068479 @default.
• W4328100434 cites W2088730795 @default.
• W4328100434 cites W2090191602 @default.
• W4328100434 cites W2117350110 @default.
• W4328100434 cites W2139086914 @default.
• W4328100434 cites W2168894097 @default.
• W4328100434 cites W219911742 @default.
• W4328100434 cites W2223806373 @default.
• W4328100434 cites W2334806815 @default.
• W4328100434 cites W2336394836 @default.
• W4328100434 cites W2481142482 @default.
• W4328100434 cites W2557002900 @default.
• W4328100434 cites W2591825958 @default.
• W4328100434 cites W2761085791 @default.
• W4328100434 cites W2769668329 @default.
• W4328100434 cites W2789262548 @default.
• W4328100434 cites W2789712950 @default.
• W4328100434 cites W2789751949 @default.
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• W4328100434 cites W2902772599 @default.
• W4328100434 cites W2911424673 @default.
• W4328100434 cites W2911964244 @default.
• W4328100434 cites W2938393691 @default.
• W4328100434 cites W2944366268 @default.
• W4328100434 cites W2954436711 @default.
• W4328100434 cites W2972175198 @default.
• W4328100434 cites W2972972597 @default.
• W4328100434 cites W2973710071 @default.
• W4328100434 cites W2994660704 @default.
• W4328100434 cites W2996089053 @default.
• W4328100434 cites W3001555788 @default.
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• W4328100434 cites W3173406979 @default.
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• W4328100434 cites W3178248440 @default.
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• W4328100434 cites W3216925752 @default.
• W4328100434 cites W4211049415 @default.
• W4328100434 cites W4211066654 @default.
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• W4328100434 doi "https://doi.org/10.1016/j.asr.2023.03.026" @default.
• W4328100434 hasPublicationYear "2023" @default.
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