Special Issue "Mapping and Monitoring of Geohazards"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 30 November 2019.

Special Issue Editors

Guest Editor
Dr. Stephen Grebby Website E-Mail
Nottingham Geospatial Institute, University of Nottingham, UK
Interests: earth observation; geohazards; mineral exploration; geological remote sensing; ground deformation; InSAR; LiDAR; hyperspectral; geophysics
Guest Editor
Dr. Alessandro Novellino Website E-Mail
British Geological Survey, Keyworth, Nottingham NG12 5GG, UK
Interests: remote sensing; SAR; InSAR; earth surface processes; landscape evolution; geohazards; landslides; land subsidence; cultural heritage

Special Issue Information

Dear Colleagues,

Geohazards affected approximately 3.5 million people in 2018, according to the Centre for Research on the Epidemiology of Disasters Emergency Events Database (EM-DAT), and continue to impose a significant financial burden on global nations in responding to such phenomena. The impact of geohazards can be reduced with a clearer understanding of the risks they pose. One way of achieving this is through enhanced knowledge of both where and when potential geohazards are likely to occur.

There is a now wide array of Earth Observation (EO) spaceborne, airborne and ground-based sensors, encompassing different spatial–temporal resolutions and characteristics of the phenomena, to support scientists and engineers in the mapping and monitoring of geohazards. However, the increasing trend in the quantity and accessibility of data acquired using these sensors has also generated new challenges with regards to their exploitation. These include overcoming issues related to the transfer, storage and processing demands of taking full advantage of the large archives of multi-sensor EO data.

This Special Issue encourages submissions that showcase the broad range of applications of EO sensors and processing techniques to the mapping and monitoring of geohazards, including, but not limited to, those associated with:

  • Volcanoes
  • Landslides
  • Earthquakes
  • Ground subsidence
  • Sinkholes
  • Tsunamis
  • Induced seismicity

Dr. Stephen Grebby
Dr. Alessandro Novellino
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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 1500 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

  • Geohazards
  • landslides
  • volcanoes
  • earthquakes
  • ground deformation
  • subsidence
  • induced seismicity
  • Earth Observation
  • remote sensing
  • big data

Published Papers (5 papers)

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Research

Open AccessArticle
Tertiary Waves Measured during 2017 Pohang Earthquake Using an Underwater Glider
Appl. Sci. 2019, 9(18), 3860; https://doi.org/10.3390/app9183860 - 14 Sep 2019
Abstract
An underwater glider equipped with a hydrophone observed the acoustic sounds of an earthquake that occurred on 15 November 2017 05:29:32 (UTC) in the Pohang area. The underwater glider observed the earthquake sounds after 19 s (05:29:51) at approximately 140 km from the [...] Read more.
An underwater glider equipped with a hydrophone observed the acoustic sounds of an earthquake that occurred on 15 November 2017 05:29:32 (UTC) in the Pohang area. The underwater glider observed the earthquake sounds after 19 s (05:29:51) at approximately 140 km from the Pohang epicenter. In order to distinguish the earthquake sound from the glider’s operation noise, the noise sources and Sound Pressure Level (SPL) of the underwater glider were analyzed and measured at laboratory tank and sea. The earthquake acoustic signal was distinguished from glider’s self-noises of fin, pumped Conductivity-Temperature-Depth profiler (CTD) and altimeter which exist over 100 Hz. The dominant frequencies of the earthquake acoustic signals due to the earthquake were 10 Hz. Frequencies at which the spectra had dropped 60 dB were 50 Hz. By analysis of time correlation with seismic waves detected by five seismic land stations and the earthquake acoustic signal, it is clearly shown that the seismic waves converted to Tertiary waves and then detected by the underwater glider. The results allow constraining the acoustic sound level of the earthquake and suggest that the glider provides an effective platform for enhancing the earth seismic observation systems and monitoring natural and anthropogenic ocean sounds. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessArticle
Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm
Appl. Sci. 2019, 9(18), 3819; https://doi.org/10.3390/app9183819 - 11 Sep 2019
Abstract
In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term [...] Read more.
In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
Open AccessArticle
Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models
Appl. Sci. 2019, 9(18), 3664; https://doi.org/10.3390/app9183664 - 04 Sep 2019
Abstract
Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for [...] Read more.
Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessArticle
Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network
Appl. Sci. 2019, 9(15), 2951; https://doi.org/10.3390/app9152951 - 24 Jul 2019
Abstract
In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network [...] Read more.
In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessArticle
Facing Missing Observations in Data—A New Approach for Estimating Strength of Earthquakes on the Pacific Coast of Southern Mexico Using Random Censoring
Appl. Sci. 2019, 9(14), 2863; https://doi.org/10.3390/app9142863 - 18 Jul 2019
Abstract
We introduced a novel spatial model based on the distribution of generalized extreme values (GEV) to analyze the maximum intensity levels of earthquakes with incomplete data (randomly censored) on the Pacific coast of southern Mexico using a random censorship approach. Spatiotemporal trends were [...] Read more.
We introduced a novel spatial model based on the distribution of generalized extreme values (GEV) to analyze the maximum intensity levels of earthquakes with incomplete data (randomly censored) on the Pacific coast of southern Mexico using a random censorship approach. Spatiotemporal trends were modeled through a non-stationary GEV model. We used a multivariate smoothing function as a linear predictor of GEV parameters to approximate nonlinear trends. The model was fitted using a flexible semi-parametric Bayesian approach and the parameters are estimated via Markov chain Monte-Carlo (MCMC). Through a rigorous simulation study, we showed the robustness of both the model and the estimation method used. Maps of the location parameter on the spatial plane for different periods of time show the existence of local variations in the extreme values of seismicity in the study area. The results indicate strong evidence of an increase in the magnitude of earthquakes over time. A spatial map of risk with maximum intensity of earthquakes in a period of 25 years was elaborated. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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