An intelligent slope disaster prediction and monitoring system based on WSN and ANP

• This paper integrates wireless sensor network and analytic network process method to evaluate the weight of disaster factors.
• By using the proposed disaster prediction model, we design and implement the Portrait-based Disaster Alerting System.
• The proposed prediction model achieves more accurate disaster determination than the traditional method.

Taiwan generally has large-scale landslides and torrential rainfall during the typhoon season. As Wireless Sensor Networks (WSN) and mobile communication technologies advance rapidly, state-of-the-art technologies are adopted to build a model to reliably predict and monitor disasters, as well as accumulate environmental variation-related information. By integrating WSN and Analytic Network Process (ANP), this study evaluates the weight of disaster factors that adopt the consistency index of pair comparisons on hillslopes. The weight estimation and classification of disaster factors are based on the K-means model to build the hillslope prediction model. The Portrait-based Disaster Alerting System (PDAS) is designed and implemented using the proposed disaster prediction model. The PDAS adopts Web-GIS to visualize the environmental information. Evaluation results of the system indicate that the proposed prediction model achieves more accurate disaster determination than the conventional method.