比较不同数据不足条件下野火蔓延预测模型的准确性

Fire Pub Date : 2024-04-16 DOI:10.3390/fire7040141
Jiahao Zhou, Wenyu Jiang, Fei Wang, Yuming Qiao, Q. Meng
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引用次数: 0

摘要

野火是全球最严重的自然灾害之一,对自然生态、经济、健康和安全造成了深远的影响。精确预测野火的蔓延已成为一个重要的研究课题。目前的火灾蔓延预测模型依赖于各种地理和环境变量的输入。然而,与实验室模拟的理想条件不同,真实的野火场景中经常会出现数据缺口,给预测的准确性和稳健性带来挑战。有必要探索不同的缺失项对预测准确性的影响程度,从而为应急决策提供合理建议。在本文中,我们测试了不同条件下的数据缺失对现有野火蔓延模型预测精度的影响,并量化了相应的误差。最终的实验结果表明,由于尚未发现明显的行为模式,有必要根据研究区域的地理条件适当判断数据缺失的潜在影响。本研究旨在模拟真实场景中数据缺失对野火蔓延预测模型准确性的影响,从而使研究人员更好地理解不同环境变量对模型的优先级,并确定可接受的数据缺失程度和不可或缺的数据属性。它为开发适用于真实世界场景的蔓延预测模型和合理评估模型结果的有效性提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparing Accuracy of Wildfire Spread Prediction Models under Different Data Deficiency Conditions
Wildfire is one of the most severe natural disasters globally, profoundly affecting natural ecology, economy, and health and safety. Precisely predicting the spread of wildfires has become an important research topic. Current fire spread prediction models depend on inputs from a variety of geographical and environmental variables. However, unlike the ideal conditions simulated in the laboratory, data gaps often occur in real wildfire scenarios, posing challenges to the accuracy and robustness of predictions. It is necessary to explore the extent to which different missing items affect prediction accuracy, thereby providing rational suggestions for emergency decision-making. In this paper, we tested how different conditions of missing data affect the prediction accuracy of existing wildfire spread models and quantified the corresponding errors. The final experimental results suggest that it is necessary to judge the potential impact of data gaps based on the geographical conditions of the study area appropriately, as there is no significant pattern of behavior yet identified. This study aims to simulate the impact of data scarcity on the accuracy of wildfire spread prediction models in real scenarios, thereby enabling researchers to better understand the priority of different environmental variables for the model and identify the acceptable degree of missing data and the indispensable data attributes. It offers new insights for developing spread prediction models applicable to real-world scenarios and rational assessment of the effectiveness of model outcomes.
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