Journal Article

Disaster Prediction Knowledge Graph Based on Multi-Source Spatio-Temporal Information

Chen, Jiahui, Li, Weichao, Zhang, Wenyue, Ge, Xingtong, Peng, Ling, Huang, Zhisheng, Yang, Yi

2022

Overview

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

• Publisher: MDPI AG
• Subject: Artificial intelligence / Decision making / disaster dynamic prediction / disaster prediction knowledge graph / Disasters / Earthquakes / Environmental risk / expert opinion / Forest & brush fires / forest fire risk prediction / Forest fires / geological landslide risk prediction / Geology / Knowledge representation / Landslides / Landslides & mudslides / Methods / multi-source data fusion / Natural disasters / Ontology / prediction / Prediction models / Remote sensing / risk / Seismic engineering / Semantics / spatial data / spatio-temporal