An effective ontology-based query response model for risk assessment in urban flood disaster management

Abstract

The drastic advancements in the field of Information Technology make it possible to analyze, manage and handle large-scale environment data and spatial information acquired from diverse sources. Nevertheless, this process is a more challenging task where the data accessibility has been performed in an unstructured, varied, and incomplete manner. The appropriate extraction of information from diverse data sources is crucial for evaluating natural disaster management. Therefore, an effective framework is required to acquire essential information in a structured and accessible manner. This research concentrates on modeling an efficient ontology-based evaluation framework to facilitate the queries based on the flood disaster location. It offers a reasoning framework with spatial and feature patterns to respond to the generated query. To be specific, the data is acquired from the urban flood disaster environmental condition to perform data analysis hierarchically and semantically. Finally, data evaluation can be accomplished by data visualization and correlation patterns to respond to higher-level queries. The proposed ontology-based evaluation framework has been simulated using the MATLAB environment. The result exposes that the proposed framework obtains superior significance over the existing frameworks with a lesser average query response time of 7 seconds.

The Role of Geological Methods in the Prevention and Control of Urban Flood Disaster Risk: A Case Study of Zhengzhou

“…They have put forward many suggestions based on ground conditions and theoretical methods that can be used effectively for flood control. Researchers and engineers based in China are constantly engaged in developing flood control strategies; they have also learned techniques practiced in foreign countries, which they successfully adapt for effective flood control [16][17][18][19][20][21][22].…”