An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network

Abstract

Debris flow disasters pose a serious threat to public safety in many areas all over the world, and it may cause severe consequences, including losses, injuries, and fatalities. With the emergence of deep learning and increased computation powers, nowadays, machine learning methods are being broadly acknowledged as a feasible solution to tackle the massive data generated from geo-informatics and sensing platforms to distill adequate information in the context of disaster monitoring. Aiming at detection of debris flow occurrences in a mountainous area of Sakurajima, Japan, this study demonstrates an efficient in-situ monitoring system which employs state-of-the-art machine learning techniques to exploit continuous monitoring data collected by a wireless accelerometer sensor network. Concretely, a two-stage data analysis process had been adopted, which consists of anomaly detection and debris flow event identification. The system had been validated with real data and generated favorable detection precision. Compared to other debris flow monitoring system, the proposed solution renders a batch of substantive merits, such as low-cost, high accuracy, and fewer maintenance efforts. Moreover, the presented data investigation scheme can be readily extended to deal with multi-modal data for more accurate debris monitoring, and we expect to expend addition sensory measurements shortly.

A Pilot Experiment on Infrasonic Lahar Detection at Mount Adams, Cascades: Ambient Infrasound and Wind-Noise Characterization at a Quiescent Stratovolcano

“…The limited detection of known mass movements to date implies that improved SNR equipment design (e.g., Pankow, 2019, 2020) and preprocessing noise reduction strategies (e.g., Williams et al, 2020) would be of use for deployments in these more exposed areas, and where source amplitudes could be relatively low. Given permitting issues, however, potentially desirous solutions like low-cost and low-power telemetered systems that reduce preparation and installation time (e.g., Schimmel et al, 2018;Ye et al, 2019), may prove challenging to implement. We find that when ASR is correctly operating, some small mass movement events are clearly detectable seismically (Fig.…”

“…Amplitudes for such events can vary considerably, with reduced pressure observations of 1 Pa at 1 km for small rockfalls and ice avalanches (e.g., Havens et al, 2014;Johnson and Ronan, 2015), to 110 Pa at 1 km for very large avalanches (Allstadt et al, 2017). Machinelearning-based classification and location schemes are increasingly being applied to mass wasting monitoring applications (e.g., Allstadt et al, 2018, and references therein;Ye et al, 2019;Liu et al, 2020;Wenner et al, 2020).…”

Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts

“…Huang et al [135] proposed a scheme for debris-flow monitoring that combines a 3D WebGIS-based platform with the WSN, as well as automatic continuous monitoring and early warning of debris flow. Ye et al [136] demonstrated an effective on-site monitoring technique that captures continuous monitoring data through the use of wireless accelerometer sensor networks and cutting-edge machine-learning technologies. Ma [137] employed Bluetooth technology to create a mountain debris-flow health monitoring system, analyse and repair mountain monitoring parts, and build data collecting and processing methods.…”

Advances in Remote Sensing-Based Disaster Monitoring and Assessment

“…Ye et al [10] proposed an original monitoring system for detecting debris flow by building a wireless accelerometer network and evaluated it over a mountainous area in Japan. Defining the phenomena of debris flow is challenging because of its drastic ignition and difficult access.…”