Internet of Things-Based Landslide Threshold Detection and Monitoring System on Ambon Island, Maluku Province, Indonesia

Abstract

Landslides frequently occur on Ambon Island, causing extensive damage each year, including debris avalanches and slope failures. Landslide mass movements are unpredictable, and investigations are usually only conducted after they occur. Therefore, direct landslide investigations that can be continuously monitored, anytime and anywhere, are crucial for establishing an early warning system. This study aims to detect and monitor slope landslide thresholds using IoT technology integration through cloud communication, as well as determine the maximum speed and total time of landslide runoff until deposition.. This research method is an innovation in the development of smart IoT devices by involving smart sensors (GesIC), and also a landslide physics approach. The research results obtained are smart IoT device products that detect and monitor landslides in real time so that slope landslides can be detected at a critical threshold angle of 32.5 on September 29, 2025 at 13:11:55 EIT, with a soil temperature of 30.8 C, soil moisture of 23.2%, soil conductivity of 29.0 S/cm, and rainfall of 215 mm. Then, a subsequent landslide occurred on October 7, 2025, with a threshold angle of 32.5 at 12:43:00 EIT, and a soil temperature of 26.8 C, soil moisture of 21.0%, soil conductivity of 17.0 mS/cm, and rainfall of 203 mm. The estimated maximum speed of the landslide run was 20.4 m/s with a maximum landslide range of 2.24 seconds at a slope angle of 85.0. Meanwhile, for a slope angle of 41.2, the maximum landslide speed is 7.3 m/s with a maximum landslide reach time of 17.9 seconds. These findings reveal that climate change can intensify landslide disasters during the rainy season. Therefore, a strategy is needed to design an adaptive landslide monitoring system, including increasing communication network capacity and optimizing an IoT-based EWS to be developed in the Maluku Islands region.