| dc.description.abstract | Tidal flooding is a serious threat to coastal areas, potentially causing infrastructure
damage, property loss, and health disturbances. This study aims to develop an early
warning system for tidal flooding based on the Internet of Things (IoT) and Computer
Vision to provide early alerts about potential flooding, allowing communities to take
preventive measures. The system has two alert statuses: warning, triggered when wind
speed reached or exceeded the threshold and is directed toward land, and danger,
triggered when tidal water levels exceed the safe limit. Anemometer, Wind Vane,
DHT11, and a USB camera with the You Only Look Once (YOLO) algorithm are used
to detect these conditions, with Raspberry Pi 5 as the main processing unit. Testing
results indicate that the IoT devices, consisting of an Anemometer, Wind Vane, and
DHT11, successfully collected data, despite minor delays in data acquisition, which
remained within acceptable limits (±1s). Data from the three sensors were also
successfully transmitted via serial communication for further processing. The YOLOv8
model demonstrated excellent performance in detecting tidal water objects, with
Precision of 1.000, Recall of 1.000, mAP50 of 0.995, and mAP50-95 of 0.984,
accurately detecting all test data. Furthermore, testing of the early warning system
showed that it successfully sent a warning alert to the telegram channel when wind
speed reached or exceeded the threshold and wind direction was toward land. The
system also successfully sent a danger alert when tidal water levels exceeded the
predetermined safe threshold. | en_US |
