Prediction and data mining of burned areas of forest fires: Optimized data matching and mining algorithm provides valuable insight

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
David A. Wood
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引用次数: 13

Abstract

An optimized data-matching machine learning algorithm is developed to provide high-prediction accuracy of total burned areas for specific wildfire incidents. It is applied to a well-studied forest-fire dataset from Portugal Montesinho Natural Park considering 13 input variables. The total burned area distribution of the 517 burn events in that dataset is highly positively skewed. The model is transparent and avoids regressions and hidden layers. This increases its detailed data mining capabilities. It matches the highest burned-area prediction accuracy achieved for this dataset with a wide range of traditional machine learning algorithms. The two-stage prediction process provides informative feature selection that establishes the relative influences of the input variables on burned-area predictions. Optimizing with mean absolute error (MAE) and root mean square error (RMSE) as separate objective functions provides complementary information with which to data mine each total burned-area incident. Such insight offers potential agricultural, ecological, environmental and forestry benefits by improving the understanding of the key influences associated with each burn event. Data mining the differential trends of cumulative absolute error and squared error also provides detailed insight with which to determine the suitability of each optimized solution to accurately predict burned-areas events of specific types. Such prediction accuracy and insight leads to confidence in how each prediction is derived. It provides knowledge to make appropriate responses and mitigate specific burn incidents, as they occur. Such informed responses should lead to short-term and long-term multi-faceted benefits by helping to prevent certain types of burn incidents being repeated or spread.

森林火灾烧毁区域预测和数据挖掘:优化的数据匹配和挖掘算法提供了有价值的见解
开发了一种优化的数据匹配机器学习算法,为特定野火事件提供高准确度的总烧伤面积预测。它被应用于葡萄牙蒙特西尼奥自然公园的一个经过充分研究的森林火灾数据集,考虑了13个输入变量。该数据集中517个烧伤事件的总烧伤面积分布是高度正倾斜的。模型是透明的,避免了回归和隐藏层。这增加了详细的数据挖掘能力。它与广泛的传统机器学习算法在该数据集上实现的最高烧伤面积预测精度相匹配。两阶段预测过程提供了信息特征选择,建立了输入变量对烧伤面积预测的相对影响。以平均绝对误差(MAE)和均方根误差(RMSE)作为单独的目标函数进行优化,为数据挖掘每个总烧伤面积事件提供了补充信息。这种见解通过提高对与每次燃烧事件相关的关键影响的理解,提供了潜在的农业、生态、环境和林业效益。数据挖掘累积绝对误差和平方误差的差异趋势也提供了详细的见解,以确定每个优化解决方案的适用性,以准确预测特定类型的烧毁区域事件。这种预测的准确性和洞察力使我们对每个预测的推导方式充满信心。它提供了知识,以作出适当的反应,减轻特定的烧伤事件,当他们发生。这种明智的应对措施应有助于防止某些类型的烧伤事件再次发生或蔓延,从而带来短期和长期多方面的好处。
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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