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Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters...
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Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters Managements

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 10984

Special Issue Editors

Dr. Antonino D'Alessandro

E-Mail Website
Guest Editor
National Earthquake Observatory, Istituto Nazionale di Geofisica e Vulcanologia, 00143 Rome, Italy
Interests: earthquake seismology; seismic sensors and networks; smart technology for earthquake monitoring; seismogram analysis; advanced methods of seismological data processing
Dr. Salvatore Scudero

E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Nazionale Terremoti, 00143 Rome, Italy
Interests: earthquake seismology; seismic sensors and networks; smart technology for earthquake monitoring; seismogram analysis; advanced methods of seismological data processing

Special Issue Information

Dear Colleagues,

The more and more frequent occurrence of natural disasters has contributed to increasing general attention to the study and mitigation of natural hazards. The climate changes, the profound alterations of the territory, and the increasingly widespread urbanization have significantly influenced the impact of some types of extreme natural events (e.g., earthquakes, volcanic eruptions, tsunamis, extreme weather events, ciclones, landslides, floods, etc.) leading to an increase in the risks associated and, as a consequence, of the often devastating impact on human life and on infrastructures.

In the last few decades, smart sensors and novely technological systems have been developed to face these issues. The advent of various smart technologies has paved the way for the realization of new infrastructures and applications for smart cities. Due to their cost effectiveness and rapid deployment, smart sensors and networks can be used for securing smart cities by providing remote monitoring and sensing for many critical scenarios.

This Special Issue aims to collect contributions on smart sensors and monitoring networks, as well as novel technologies and approaches in early warning, surveillance, and post-event damage assessment in case of extreme events or natural disasters. Contributions presenting new devices and their applications in smart city for the prevention and mitigation of natural hazards will be welcome. Contributions concerning innovative algorithms (e.g., signal processing, big data analysis, machine learning, etc.) for the real-time analysis of signals recorded by smart networks will also be considered.

Dr. Antonino D'Alessandro
Dr. Salvatore Scudero
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart sensors, devices and technologies
  • mobile devices and Internet of Things
  • smart network for smart cities
  • extreme natural events
  • natural disasters mitigation
  • structural health monitoring
  • early warning
  • damage assessment
  • innovative algorithms

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Published Papers (4 papers)

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Research

21 pages, 5460 KiB  
Article
Towards Managing Uncertain Geo-Information for Drilling Disasters Using Event Tracking Sensitivity Analysis
by Siamak Tavakoli, Stefan Poslad, Rudolf Fruhwirth, Martin Winter and Herwig Zeiner
Sensors 2023, 23(9), 4292; https://doi.org/10.3390/s23094292 - 26 Apr 2023
Viewed by 1645
Abstract
In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, [...] Read more.
In sub-surface drilling rigs, one key critical crisis is unwanted influx into the borehole as a result of increasing the influx rate while drilling deeper into a high-pressure gas formation. Although established risk assessments in drilling rigs provide a high degree of protection, uncertainty arises due to the behavior of the formation being drilled into, which may cause crucial situations at the rig. To overcome such uncertainties, real-time sensor measurements are used to predict, and thus prevent, such crises. In addition, new understandings of the effective events were derived from raw data. In order to avoid the computational overhead of input feature analysis that hinders time-critical prediction, EventTracker sensitivity analysis, an incremental method that can support dimensionality reduction, was applied to real-world data from 1600 features per each of the 4 wells as input and 6 time series per each of the 4 wells as output. The resulting significant input series were then introduced to two classification methods: Random Forest Classifier and Neural Networks. Performance of the EventTracker method was understood correlated with a conventional manual method that incorporated expert knowledge. More importantly, the outcome of a Neural Network Classifier was improved by reducing the number of inputs according to the results of the EventTracker feature selection. Most important of all, the generation of results of the EventTracker method took fractions of milliseconds that left plenty of time before the next bunch of data samples. Full article
(This article belongs to the Special Issue Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters Managements)
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10 pages, 1872 KiB  
Article
Statistical Picking of Multivariate Waveforms
by Nicoletta D’Angelo, Giada Adelfio, Marcello Chiodi and Antonino D’Alessandro
Sensors 2022, 22(24), 9636; https://doi.org/10.3390/s22249636 - 8 Dec 2022
Cited by 1 | Viewed by 1114
Abstract
In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this [...] Read more.
In this paper, we propose a new approach based on the fitting of a generalized linear regression model in order to detect points of change in the variance of a multivariate-covariance Gaussian variable, where the variance function is piecewise constant. By applying this new approach to multivariate waveforms, our method provides simultaneous detection of change points in functional time series. The proposed approach can be used as a new picking algorithm in order to automatically identify the arrival times of P- and S-waves in different seismograms that are recording the same seismic event. A seismogram is a record of ground motion at a measuring station as a function of time, and it typically records motions along three orthogonal axes (X, Y, and Z), with the Z-axis being perpendicular to the Earth’s surface and the X- and Y-axes being parallel to the surface and generally oriented in North–South and East–West directions, respectively. The proposed method was tested on a dataset of simulated waveforms in order to capture changes in the performance according to the waveform characteristics. In an application to real seismic data, our results demonstrated the ability of the multivariate algorithm to pick the arrival times in quite noisy waveforms coming from seismic events with low magnitudes. Full article
(This article belongs to the Special Issue Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters Managements)
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14 pages, 4262 KiB  
Article
Fundamentals of Fast Tsunami Wave Parameter Determination Technology for Hazard Mitigation
by Mikhail Lavrentiev, Konstantin Lysakov, Andrey Marchuk and Konstantin Oblaukhov
Sensors 2022, 22(19), 7630; https://doi.org/10.3390/s22197630 - 8 Oct 2022
Cited by 3 | Viewed by 1922
Abstract
This paper describes two basic elements of the smart technology, allowing us to bring to a new level the problem of early warning and mitigation of tsunami hazards for the so-called near zone events (when a destructive tsunami wave reaches the nearest coast [...] Read more.
This paper describes two basic elements of the smart technology, allowing us to bring to a new level the problem of early warning and mitigation of tsunami hazards for the so-called near zone events (when a destructive tsunami wave reaches the nearest coast in tens of minutes after the earthquake). The sensors system, installed in a reasonable way (to detect a wave as early as possible), is capable of transmitting the necessary raw data (measured wave profile) in a real time mode to a processing center. The smart (based on mathematical theory) algorithm can reconstruct an actual source shape within a few seconds using just a part of the measured wave record. Using modern computer architectures (Graphic Processing Units or Field Programmable Gates Array) allows computing tsunami wave propagation from the source to shoreline in 1–2 min, which is comparable to the performance of a supercomputer. As is observed, the inundation zone could be evaluated reasonably correctly as the coastal area below two thirds of the tsunami wave height at a particular location. In total, the achieved performance of the two above mentioned algorithms makes it possible to evaluate timely the tsunami wave heights along the coastline to approximate the expected inundation zone, and therefore, to suggest (in case of necessity) evacuation measures to save lives. Full article
(This article belongs to the Special Issue Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters Managements)
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24 pages, 11672 KiB  
Article
The Use of Soil Moisture and Pore-Water Pressure Sensors for the Interpretation of Landslide Behavior in Small-Scale Physical Models
by Josip Peranić, Nina Čeh and Željko Arbanas
Sensors 2022, 22(19), 7337; https://doi.org/10.3390/s22197337 - 27 Sep 2022
Cited by 25 | Viewed by 5127
Abstract
This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation [...] Read more.
This paper presents some of the results and experiences in monitoring the hydraulic response of downscaled slope models under simulated rainfall in 1 g. The downscaled slope model platform was developed as part of a four-year research project, “Physical modeling of landslide remediation constructions’ behavior under static and seismic actions”, and its main components are briefly described with the particular focus on the sensor network that allows monitoring changes in soil moisture and pore-water pressure (pwp). The technical characteristics of the sensors and the measurement methods used to provide the metrics are described in detail. Some data on the hydraulic and mechanical responses obtained from the conducted tests on slope models built from different soil types under different test conditions are presented and interpreted in the context of rainfall-induced landslides. The results show that the sensor network used is suitable for monitoring changes in the soil moisture and pwp in the model, both in terms of the transient rainfall infiltration through partially saturated soil and in terms of the rise in the water table and pwp build-up under fully saturated conditions. It is shown how simultaneous monitoring of soil moisture and pwp can be used to reconstruct stress paths that the monitored points undergo during different test phases. Finally, some peculiarities related to hydraulic hysteresis and surface erosion that were observed in some of tests are discussed, as well as possible difficulties in achieving and maintaining the targeted initial moisture distribution in slope models. Full article
(This article belongs to the Special Issue Smart Sensors and Technologies for Natural Hazards Mitigation and Disasters Managements)
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