| Title |
A Study on the development of big data-based AI water meter freeze and burst risk information service |
| Authors |
Lee, Jinuk ; Kim, Sunghoon ; Lee, Mnjae |
| DOI |
https://dx.doi.org/10.6106/KJCEM.2023.24.3.042 |
| Keywords |
AI; SWM; Freeze and burst; IoT sensor; Temperature prediction; Big data |
| Abstract |
Freeze and burst water meter in winter causes many social costs, such as meter replacement cost, inability of water use, and secondary damage by freezing water. The government is making efforts to modernize local waterworks, and in particular, is promoting SWM(Smart Water Management) project nationwide. In this study suggests a new freeze risk notification information service based on the temperature by IoT sensor inside the water meter box rather than outside temperature. In addition, in order to overcome the quantitative and regional limitation of IoT temperature sensors installed nationwide, and AI based temperature prediction model was developed that predicts the temperature inside water meter boxes based on data acquired from IoT temperature sensors and other information. Through the prediction model optimization process, a nationwide water meter freezing risk information service was convinced. |