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: closed (30 November 2019).

Special Issue Editors

Dr. Stephen Grebby
Website
Guest Editor
Nottingham Geospatial Institute, University of Nottingham, Nottingham NG7 2TU, UK
Interests: earth observation; geohazards; mineral exploration; geological remote sensing; ground deformation; InSAR; LiDAR; hyperspectral; geophysics
Special Issues and Collections in MDPI journals
Dr. Alessandro Novellino
Website
Guest Editor
British Geological Survey, Keyworth, Nottingham NG12 5GG, UK
Interests: natural hazards; landslides; Synthetic Aperture Radar; InSAR; geodesy

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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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 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 (13 papers)

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Editorial

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Open AccessEditorial
Special Issue on “Mapping and Monitoring of Geohazards”
Appl. Sci. 2020, 10(13), 4609; https://doi.org/10.3390/app10134609 - 03 Jul 2020
Abstract
According to the Emergency Events Database (https://public [...] Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Research

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Open AccessArticle
A Shear-Wave Velocity Model in the City of Oued-Fodda (Northern Algeria) from Rayleigh Wave Ellipticity Inversion
Appl. Sci. 2020, 10(5), 1717; https://doi.org/10.3390/app10051717 - 03 Mar 2020
Cited by 1
Abstract
The city of Oued-Fodda is located in north-central Algeria on the margins of the Middle-Cheliff Basin. This region has suffered several destructive earthquakes. The strongest was the 1980 El-Asnam earthquake (Ms7.3), whose causative fault was located about 1 km north of the city [...] Read more.
The city of Oued-Fodda is located in north-central Algeria on the margins of the Middle-Cheliff Basin. This region has suffered several destructive earthquakes. The strongest was the 1980 El-Asnam earthquake (Ms7.3), whose causative fault was located about 1 km north of the city of Oued-Fodda. Therefore, a good knowledge of the soil characteristics in this city may allow a better evaluation of the seismic risk and help to minimize damages in the future. With this objective, a detailed microzonation study of Oued-Fodda has been carried out in this study. For that, the horizontal-to-vertical spectral ratio (HVSR) method has been applied on 102 sites along the city, estimating the soil fundamental frequencies and their corresponding amplitudes. Besides, the Rayleigh wave ellipticity inversion has been accomplished in order to estimate the corresponding Vs profiles and provide two cross-sections of the geology under the city. In the central part of the city, high-frequency peaks are observed, between 12.5 and 15 Hz, which correspond to impedance contrasts at shallow depth (<20 m). In the surrounding plain, two clear peaks are identified in the ranges 1.8–3.5 Hz (fundamental frequencies) and 6.5–15 Hz (secondary peaks). Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessFeature PaperArticle
Mapping and Analyzing the Evolution of the Butangbunasi Landslide Using Landsat Time Series with Respect to Heavy Rainfall Events during Typhoons
Appl. Sci. 2020, 10(2), 630; https://doi.org/10.3390/app10020630 - 15 Jan 2020
Cited by 3
Abstract
Large rainfall-induced landslides are among the most dangerous natural hazards in Taiwan, posing a risk for people and infrastructure. Thus, better knowledge about the evolution of landslides and their impact on the downstream area is of high importance for disaster mitigation. The aim [...] Read more.
Large rainfall-induced landslides are among the most dangerous natural hazards in Taiwan, posing a risk for people and infrastructure. Thus, better knowledge about the evolution of landslides and their impact on the downstream area is of high importance for disaster mitigation. The aim of this study is twofold: (1) to semi-automatically map the evolution of the Butangbunasi landslide in south-central Taiwan using satellite remote sensing data, and (2) to investigate the potential correlation between changes in landslide area and heavy rainfall during typhoon events. Landslide area, as well as temporary landslide-dammed lakes, were semi-automatically identified using object-based image analysis (OBIA), based on 20 Landsat images from 1984 to 2018. Hourly rainfall data from the Taiwan Central Weather Bureau (CWB) was complemented with rainfall data from Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) to examine the potential relationship between landslide area changes and rainfall as a triggering factor. The OBIA mapping results revealed that the most significant landslide extension happened after typhoon Morakot in 2009. We found a moderate positive relationship between the landslide area change and the duration of the heavy rainfall event, whereas daily precipitation, cumulative rainfall and mean intensity did not present strong significant correlations. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessFeature PaperArticle
Mapping Recent Shoreline Changes Spanning the Lateral Collapse of Anak Krakatau Volcano, Indonesia
Appl. Sci. 2020, 10(2), 536; https://doi.org/10.3390/app10020536 - 10 Jan 2020
Cited by 3
Abstract
We use satellite imagery to investigate the shoreline changes associated with volcanic activity in 2018–2019 at Anak Krakatau, Indonesia, spanning a major lateral collapse and period of regrowth through explosive activity. The shoreline changes have been analyzed and validated through the adaptation of [...] Read more.
We use satellite imagery to investigate the shoreline changes associated with volcanic activity in 2018–2019 at Anak Krakatau, Indonesia, spanning a major lateral collapse and period of regrowth through explosive activity. The shoreline changes have been analyzed and validated through the adaptation of an existing methodology based on Sentinel-2 multispectral imagery and developed on Google Earth Engine. This work tests the results of this method in a highly dynamic volcanic environment and validates them with manually digitized shorelines. The analysis shows that the size of the Anak Krakatau Island increased from 2.84 km2 to 3.19 km2 during 15 May 2018–1 November 2019 despite the loss of area in the 22 December 2018 lateral collapse. The lateral collapse reduced the island area to ~1.5 km2 but this was followed by a rapid increase in area in the first two months of 2019, reaching up to 3.27 km2. This was followed by a period of little change as volcanic activity declined and then by a net decrease from May 2019 to 1 November 2019 that resulted from erosion on the SW side of the island. This history of post-collapse eruptive regrowth and coastal erosion derived from the shoreline changes illuminates the potential for satellite-based automated shoreline mapping to provide databases for monitoring remote island volcanoes. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessArticle
Rockfall Investigation and Hazard Assessment from Nang County to Jiacha County in Tibet
Appl. Sci. 2020, 10(1), 247; https://doi.org/10.3390/app10010247 - 28 Dec 2019
Cited by 1
Abstract
The influences of rockfall on human engineering have been increasing in Tibet with the rapid development of the western region of China. This study proposed a multi-approach to carry out rockfall investigation and hazard assessment. As a case study, the rockfall hazard from [...] Read more.
The influences of rockfall on human engineering have been increasing in Tibet with the rapid development of the western region of China. This study proposed a multi-approach to carry out rockfall investigation and hazard assessment. As a case study, the rockfall hazard from Nang County to Jiacha County in Tibet was assessed. Firstly, we summarized the characteristics of spatial distributions of typical rockfall sources using Digital Elevation Model (DEM) and unmanned aerial vehicle (UAV) aerial images with resolution of 10 m. According to the thresholds of slope angle, slope aspect and elevation distribution of typical rockfall sources, we obtained all of the rockfall source areas in study area semi-automatically in ArcGIS platform. Secondly, we improved the efficiency and accuracy of detailed field investigation by using a three-dimensional (3D) point cloud model and rock mass structure extraction software. According to the analysis result, the dominant joint set was J1, whose orientation was basically consistent with the Yarlung Tsangpo Fault. The combination of J1, J2 and J4 cut the rock mass into blocks of wedge with J1 as potential sliding planes. It was indicated that the stability of the rock mass in study area was mainly controlled by the characters of joint sets. Finally, we applied the improved reclassification criteria of the Rockfall Hazard Vector (RHV) method in rockfall hazard assessment according to protection capabilities of the current protection facilities, making the result more valuable for geohazards prevention work. Based on this multi-approach, we obtained that 10.92% of the 306 provincial highway and 9.38% of the power line were threatened by potential rockfall hazards in study area. The hazard assessment results of study area were also of certain guiding value to the linear project planning and geohazards prevention work. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessFeature PaperArticle
Integrated Procedure for Monitoring and Assessment of Linear Infrastructures Safety (I-Pro MONALISA) Affected by Slope Instability
Appl. Sci. 2019, 9(24), 5535; https://doi.org/10.3390/app9245535 - 16 Dec 2019
Cited by 4
Abstract
The occurrence of geological events such as landslides is one of the main causes of damage along linear infrastructures: Damage to transport infrastructures, as roads, bridges, and railways, can restrict their optimal functions and contribute to traffic accidents. The frequent and accurate monitoring [...] Read more.
The occurrence of geological events such as landslides is one of the main causes of damage along linear infrastructures: Damage to transport infrastructures, as roads, bridges, and railways, can restrict their optimal functions and contribute to traffic accidents. The frequent and accurate monitoring of slope instability phenomena and of their interaction with existing man-made infrastructures plays a key role in risk prevention and mitigation activities. In this way, the use of high-resolution X-band synthetic aperture radar (SAR) data, characterized by short revisiting times, has demonstrated to be a powerful tool for a periodical noninvasive monitoring of ground motion and superstructure stability, aimed at improving the efficiency of inspection, repairing, and rehabilitation efforts. In the present work, we suggest a semiautomatic GIS approach, which, by using satellite radar interferometry data and results of geomorphological field survey integrated in a qualitative vulnerability matrix, allows to identify sections with different levels of damage susceptibility, where detailed conventional in situ measurements are required for further analysis. The procedure has been tested to investigate landslide-induced effects on a linear infrastructure in Campania Region (Italy), the Provincial Road “P.R. 264”, which is affected, along its linear development, by several slope instabilities. COSMO-SkyMed interferometric products, as indicator of ground kinematics, and results of in situ damage survey, as indicator of consequences, have been merged in a qualitative 4 × 4 matrix, thus obtaining a vulnerability zoning map along a linear infrastructure in January 2015. Furthermore, an updating of landslide inventory map is provided: In addition to 24 official landslides pre-mapped in 2012, 30 new events have been identified, and corresponding intensity and state of activity has been detected. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessArticle
Landslide Susceptibility Mapping for Austria Using Geons and Optimization with the Dempster-Shafer Theory
Appl. Sci. 2019, 9(24), 5393; https://doi.org/10.3390/app9245393 - 10 Dec 2019
Cited by 8
Abstract
Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation [...] Read more.
Landslide susceptibility mapping (LSM) can serve as a basis for analyzing and assessing the degree of landslide susceptibility in a region. This study uses the object-based geons aggregation model to map landslide susceptibility for all of Austria and evaluates whether an additional implementation of the Dempster–Shafer theory (DST) could improve the results. For the whole of Austria, we used nine conditioning factors: elevation, slope, aspect, land cover, rainfall, distance to drainage, distance to faults, distance to roads, and lithology, and assessed the performance and accuracy of the model using the area under the curve (AUC) for the receiver operating characteristics (ROC). We used three scale parameters for the geons model to evaluate the impact of the scale parameter on the performance of LSM. The results were similar for the three scale parameters. Applying the Dempster–Shafer theory could significantly improve the results of the object-based geons model. The accuracy of the DST-derived LSM for Austria improved and the respective AUC value increased from 0.84 to 0.93. The resulting LSMs from the geons model provide meaningful units independent of administrative boundaries, which can be beneficial to planners and policymakers. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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Open AccessArticle
Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines: A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China
Appl. Sci. 2019, 9(22), 4756; https://doi.org/10.3390/app9224756 - 07 Nov 2019
Cited by 3
Abstract
Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the [...] Read more.
Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA. The Wushan segment of TGRA was selected as a case study. At first, 165 landslides were identified and a total of 14 landslide causal factors were constructed from different data sources. Multicollinearity analysis and information gain ratio (IGR) model were applied to select landslide causal factors. Subsequently, the landslide susceptibility mapping using the calculated results of four models, namely, support vector machines (SVM), artificial neural networks (ANN), classification and regression tree (CART), and logistic regression (LR). The accuracy of these four maps were evaluated using the receive operating characteristic (ROC) and the accuracy statistic. Results revealed that eliminating the inconsequential factors can perhaps improve the accuracy of landslide susceptibility modelling, and the SVM model had the best performance in this study, providing strong technical support for landslide susceptibility modelling in TGRA. Full article
(This article belongs to the Special Issue Mapping and Monitoring of Geohazards)
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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
Cited by 1
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
Cited by 1
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)
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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
Cited by 15
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
Cited by 4
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
Cited by 1
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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