Next Article in Journal
An Efficient Representation-Based Subspace Clustering Framework for Polarized Hyperspectral Images
Next Article in Special Issue
Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest, Extreme Gradient Boosting, and Deep Neural Network
Previous Article in Journal
Fusion of Change Vector Analysis in Posterior Probability Space and Postclassification Comparison for Change Detection from Multispectral Remote Sensing Data
Previous Article in Special Issue
Machine Learning Approaches for Detecting Tropical Cyclone Formation Using Satellite Data
Open AccessArticle

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

1
National Metrology Institute of Japan (NMIJ), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-0045, Japan
2
Department of Electronics and Manufacturing, The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8564, Japan
3
Sabo Department, National Institute for Land and Infrastructure Management(NILIM), Tsukuba 305-0804, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(13), 1512; https://doi.org/10.3390/rs11131512
Received: 30 April 2019 / Revised: 20 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
(This article belongs to the Special Issue Advances in Remote Sensing-based Disaster Monitoring and Assessment)
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. View Full-Text
Keywords: disaster monitoring; wireless sensor network; debris flow; anomaly detection; machine learning; deep learning; accelerometer sensor disaster monitoring; wireless sensor network; debris flow; anomaly detection; machine learning; deep learning; accelerometer sensor
Show Figures

Figure 1

MDPI and ACS Style

Ye, J.; Kurashima, Y.; Kobayashi, T.; Tsuda, H.; Takahara, T.; Sakurai, W. An Efficient In-Situ Debris Flow Monitoring System over a Wireless Accelerometer Network. Remote Sens. 2019, 11, 1512.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop