Wildfire prediction for California using and comparing Spatio-Temporal Knowledge Graphs

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Martin Böckling, Heiko Paulheim, Sarah Detzler
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引用次数: 0

Abstract

Abstract The frequency of wildfires increases yearly and poses a constant threat to the environment and human beings. Different factors, for example surrounding infrastructure to an area (e.g., campfire sites or power lines) contribute to the occurrence of wildfires. In this paper, we propose using a Spatio-Temporal Knowledge Graph (STKG) based on OpenStreetMap (OSM) data for modeling such infrastructure. Based on that knowledge graph, we use the RDF2vec approach to create embeddings for predicting wildfires, and we align different vector spaces generated at each temporal step by partial rotation. In an experimental study, we determine the effect of the surrounding infrastructure by comparing different data composition strategies, which involve a prediction based on tabular data, a combination of tabular data and embeddings, and solely embeddings. We show that the incorporation of the STKG increases the prediction quality of wildfires.
基于时空知识图的加州野火预测与比较
摘要森林火灾的发生频率逐年增加,对环境和人类构成了持续的威胁。不同的因素,例如一个地区周围的基础设施(例如,营火点或电线)会导致野火的发生。在本文中,我们建议使用基于OpenStreetMap (OSM)数据的时空知识图(STKG)来建模此类基础设施。基于该知识图,我们使用RDF2vec方法创建用于预测野火的嵌入,并通过部分旋转对齐在每个时间步骤生成的不同向量空间。在一项实验研究中,我们通过比较不同的数据组合策略来确定周围基础设施的影响,这些策略包括基于表格数据的预测、表格数据和嵌入的组合以及单独嵌入。结果表明,STKG的加入提高了野火的预测质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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