Cao Qi, Marifel Grace C. Kummer
ABSTRACT
This study aimed to design and develop an urban pipeline safety monitoring and early warning system for Maoming City using decision support and artificial intelligence. To achieve this, the researchers employed a combination of descriptive and developmental research designs to gain an in-depth understanding of existing conditions and to guide system development based on platform and user-specific requirements. The study involved 55 participants, including users, super administrators, administrators, IT specialists, and pipeline management personnel, who were interviewed to gather insights into their experiences and needs. Additionally, a questionnaire based on the ISO/IEC 25010 standard was administered to evaluate the system across key quality dimensions, including suitability, efficiency, compatibility, usability, reliability, security, maintainability, and portability. Quantitative data were analyzed using descriptive statistics and weighted averages to assess the system’s level of compliance with the standard. The findings revealed that the developed system complied with the ISO/IEC 25010 software quality standards to a very great extent, indicating strong overall performance and effectiveness. These results suggest that the system successfully meets established software quality requirements and is a viable solution for enhancing urban pipeline safety monitoring and early warning capabilities.
Keywords: Artificial intelligence, decision support system, pipeline monitoring, smart infrastructure, urban pipeline safety
https://doi.org/10.57180/uatz6726