| Title |
Web-Based Manhole Overflow Prediction System Using Ultrasonic Level Sensors And Expert System |
| ID_Doc |
61593 |
| Authors |
Jerry Daniel J.; Byju C.; Rakesh G.; Lekshmi G. |
| Year |
2022 |
| Published |
2022 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing, COM-IT-CON 2022 |
| DOI |
http://dx.doi.org/10.1109/COM-IT-CON54601.2022.9850733 |
| Abstract |
The implementation of smart cities is becoming more evident with the continuous increase in the urban population. Difficulty in waste management, pollution, traffic congestions scarcity of resources, human health concerns, and inadequate, deteriorating and aging infrastructures are among the more basic technical, physical, and material problems. The integration of AI techniques in traditional real-time systems appears to be a promising approach to deal with the growing complexity of real-world applications. Real-time expert systems are online knowledge-based systems that are used to monitor the complex industrial process and combine analytical process models with conventional process control and heuristics to gauge and interpret sensory data, while reasoning about the past, present, and future to assess ongoing developments and plan appropriate actions in future. This paper describes the Real-Time Expert System Shell is an integrated software tool which can be used to monitor Sewer Network Monitoring System and prevent manhole overflow. The sewer level data measured by Ultrasonic level sensors is delivered wirelessly via GSM and in real-time to the citizens or the appropriate authorities. The expert system interfaces with the local SCADA System which acquires online sewer data from ultrasonic level sensors and identifies the possibility of manhole overflow in the future. A web-enabled, user-friendly GUI is designed to build the Knowledge Base for a particular domain as well as display overflow details. It also publishes the data to Cloud for taking necessary action by the appropriate authorities © 2022 IEEE. |
| Author Keywords |
Expert System Shell; Fault Diagnosis; Inference Engine; Manholes; Real-time overflow prediction; Ultrasonic level sensor |
