Conflagration Prediction based on XGBoost
2017-03-25 21:40:38 0 举报
基于XGBoost的火灾预测是一种先进的机器学习技术,用于提前预警和控制火灾风险。通过收集大量历史数据,如气象条件、建筑物结构、消防设施等,XGBoost模型能够学习这些数据中的复杂模式和关联关系,从而对未来的火灾发生概率进行准确预测。这种预测方法不仅可以帮助政府和消防部门制定更有效的防火策略,还可以提高公众对火灾风险的认识和应对能力。总之,基于XGBoost的火灾预测为火灾防控提供了一种科学、高效的手段,有助于降低火灾事故的发生频率和损失程度。
作者其他创作
大纲/内容
Feature Selection Based On Association Rules
Conflagration Prediction Based on modefied XGBoost
Features:disaster-inducing successive factors and loss control successive factors selected by feature selection
Box-Cox Transformation
Features:disaster-inducing factors and loss control factors transformed by Box-Cox transformation and other discrete features selected by feature selection
Conflagration Prediction
DATA CLEANING
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