Submit to IJERPH Review for IJERPH Propose a Special Issue

Journal Menu

Journal Browser

Landslide Risk Assessment and Mitigation

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 73750

Share This Special Issue

Special Issue Editors

Prof. Dr. Sabatino Cuomo

E-Mail Website
Guest Editor

Department of Civil Engineering, University of Salerno, 84084 Salerno, Italy
Interests: landslide mechanisms; solid-fluid transition; landslide dynamics; regional slope stability; slope erosion; geosynthetics reinforcement; laboratory testing; constitutive modeling; unsaturated soils

Dr. Anika Braun

E-Mail Website
Guest Editor

Engineering Geology Department, Institute of Applied Geosciences, Technische Universität Berlin, 10623 Berlin, Germany
Interests: landslide susceptibility; geographic information systems (GIS); machine learning; drone remote sensing and 3D mapping; digital rock characterization; earthquake environmental effects

Dr. Josip Peranic

E-Mail Website
Guest Editor

Faculty of Civil Engineering, University of Rijeka, 51000 Rijeka, Croatia
Interests: slope stability; rainfall-induced landslides; unsaturated soils; geotechnical monitoring; laboratory testing

Special Issue Information

Dear Colleagues,

Landslides are climatically or seismically triggered processes that cause significant loss of life, damage to infrastructure, and economic losses in inhabited mountainous environments. Landslide risk is defined as a function of the hazard or probability of occurrence of landslides, the elements at risk, and their vulnerability. Two developments are currently making this equation explode. The intensification of climate actions, characterized by heavy rainfall events, storms, wildfires, and thawing of permafrost and glaciers, is amplifying the hazard component. At the same time, through increasing migration from rural to urban areas almost everywhere in the world, more and more people are putting themselves at risk, a development that is also reinforcing the hazard through intensified land use in urban areas. On the plus side is the fact that compared with other natural hazards, such as earthquakes or storms, landslides are usually spatially restricted processes, making it possible to manage and mitigate the risk through holistic urban planning, engineering solutions, and early warning.

In this Special Issue, we want to explore recent advances in the fields of landslide risk management and mitigation. We would like to invite contributions about advances in procedures in the fields of data acquisition, monitoring, modeling, and mapping of landslide hazard and risk, and we particularly want to encourage contributions considering the implications of climate change for landslide risk. Papers documenting examples of implementations in landslide risk management and mitigation, e.g., through structural works or early warning systems, are welcome, as well as papers about advances in the investigation of landslide mechanisms through laboratory experiments, field investigations, and physical models.

Prof. Dr. Sabatino Cuomo
Dr. Anika Braun
Dr. Josip Peranic
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. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly 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 2500 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

soil
susceptibility
modeling
monitoring
vulnerability
perception
inventory

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (12 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

26 pages, 1165 KiB

Open AccessArticle

A Framework for Identification, Assessment and Prioritization of Climate Change Adaptation Measures for Roads and Railways

by Yvonne Andersson-Sköld, Lina Nordin, Erik Nyberg and Mikael Johannesson

Int. J. Environ. Res. Public Health 2021, 18(23), 12314; https://doi.org/10.3390/ijerph182312314 - 23 Nov 2021

Cited by 2 | Viewed by 2847

Abstract

Severe accidents and high costs associated with weather-related events already occur in today’s climate. Unless preventive measures are taken, the costs are expected to increase in future due to ongoing climate change. However, the risk reduction measures are costly as well and may result in unwanted impacts. Therefore, it is important to identify, assess and prioritize which measures are necessary to undertake, as well as where and when these are to be undertaken. To be able to make such evaluations, robust (scientifically based), transparent and systematic assessments and valuations are required. This article describes a framework to assess the cause-and-effect relationships and how to estimate the costs and benefits as a basis to assess and prioritize measures for climate adaptation of roads and railways. The framework includes hazard identification, risk analysis and risk assessment, identification, monetary and non-monetary evaluation of possible risk reduction measures and a step regarding distribution-, goal- and sensitivity analyses. The results from applying the framework shall be used to prioritize among potential risk reduction measures as well as when to undertake them. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

21 pages, 15639 KiB

Open AccessArticle

Integration of Vulnerability and Hazard Factors for Landslide Risk Assessment

by Patricia Arrogante-Funes, Adrián G. Bruzón, Fátima Arrogante-Funes, Rocío N. Ramos-Bernal and René Vázquez-Jiménez

Int. J. Environ. Res. Public Health 2021, 18(22), 11987; https://doi.org/10.3390/ijerph182211987 - 15 Nov 2021

Cited by 20 | Viewed by 5277

Abstract

Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socio-economic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

26 pages, 22059 KiB

Open AccessArticle

Investigation of Critical Geotechnical, Petrological and Mineralogical Parameters for Landslides in Deeply Weathered Dunite Rock (Medellín, Colombia)

by Tamara Breuninger, Bettina Menschik, Agnes Demharter, Moritz Gamperl and Kurosch Thuro

Int. J. Environ. Res. Public Health 2021, 18(21), 11141; https://doi.org/10.3390/ijerph182111141 - 23 Oct 2021

Cited by 6 | Viewed by 2275

Abstract

The current study site of the project Inform@Risk is located at a landslide prone area at the eastern slopes of the city of Medellín, Colombia, which are composed of the deeply weathered Medellín Dunite, an ultramafic Triassic rock. The dunite rock mass can be characterized by small-scale changes, which influence the landslide exposition to a major extent. Due to the main aim of the project, to establish a low-cost landslide early warning system (EWS) in this area, detailed field studies, drillings, laboratory and mineralogical tests were conducted. The results suggest that the dunite rock mass shows a high degree of serpentinization and is heavily weathered up to 50 m depth. The rock is permeated by pseudokarst, which was already found in other regions of this unit. Within the actual project, a hypothesis has for the first time been established, explaining the generation of the pseudokarst features caused by weathering and dissolution processes. These parameters result in a highly inhomogeneous rock mass and nearly no direct correlation of weathering with depth. In addition, the theory of a secondary, weathering serpentinization was established, explaining the solution weathering creating the pseudokarst structures. This contribution aims to emphasize the role of detailed geological data evaluation in the context of hazard analysis as an indispensable data basis for landslide early warning systems. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

20 pages, 7589 KiB

Open AccessArticle

Landslide Susceptibility Assessment Using an AutoML Framework

by Adrián G. Bruzón, Patricia Arrogante-Funes, Fátima Arrogante-Funes, Fidel Martín-González, Carlos J. Novillo, Rubén R. Fernández, René Vázquez-Jiménez, Antonio Alarcón-Paredes, Gustavo A. Alonso-Silverio, Claudia A. Cantu-Ramirez and Rocío N. Ramos-Bernal

Int. J. Environ. Res. Public Health 2021, 18(20), 10971; https://doi.org/10.3390/ijerph182010971 - 19 Oct 2021

Cited by 22 | Viewed by 3851

Abstract

The risks associated with landslides are increasing the personal losses and material damages in more and more areas of the world. These natural disasters are related to geological and extreme meteorological phenomena (e.g., earthquakes, hurricanes) occurring in regions that have already suffered similar previous natural catastrophes. Therefore, to effectively mitigate the landslide risks, new methodologies must better identify and understand all these landslide hazards through proper management. Within these methodologies, those based on assessing the landslide susceptibility increase the predictability of the areas where one of these disasters is most likely to occur. In the last years, much research has used machine learning algorithms to assess susceptibility using different sources of information, such as remote sensing data, spatial databases, or geological catalogues. This study presents the first attempt to develop a methodology based on an automatic machine learning (AutoML) framework. These frameworks are intended to facilitate the development of machine learning models, with the aim to enable researchers focus on data analysis. The area to test/validate this study is the center and southern region of Guerrero (Mexico), where we compare the performance of 16 machine learning algorithms. The best result achieved is the extra trees with an area under the curve (AUC) of 0.983. This methodology yields better results than other similar methods because using an AutoML framework allows to focus on the treatment of the data, to better understand input variables and to acquire greater knowledge about the processes involved in the landslides. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

20 pages, 67168 KiB

Open AccessArticle

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

by Xiaoting Zhou, Weicheng Wu, Ziyu Lin, Guiliang Zhang, Renxiang Chen, Yong Song, Zhiling Wang, Tao Lang, Yaozu Qin, Penghui Ou, Wenchao Huangfu, Yang Zhang, Lifeng Xie, Xiaolan Huang, Xiao Fu, Jie Li, Jingheng Jiang, Ming Zhang, Yixuan Liu, Shanling Peng, Chongjian Shao, Yonghui Bai, Xiaofeng Zhang, Xiangtong Liu and Wenheng Liu Show full author list Hide full author list

Int. J. Environ. Res. Public Health 2021, 18(11), 5906; https://doi.org/10.3390/ijerph18115906 - 31 May 2021

Cited by 20 | Viewed by 7901

Abstract

Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

17 pages, 6468 KiB

Open AccessArticle

Integration of Spatial Probability and Size in Slope-Unit-Based Landslide Susceptibility Assessment: A Case Study

by Langping Li and Hengxing Lan

Int. J. Environ. Res. Public Health 2020, 17(21), 8055; https://doi.org/10.3390/ijerph17218055 - 1 Nov 2020

Cited by 12 | Viewed by 3108

Abstract

Landslide spatial probability and size are two essential components of landslide susceptibility. However, in existing slope-unit-based landslide susceptibility assessment methods, landslide size has not been explicitly considered. This paper developed a novel slope-unit based approach for landslide susceptibility assessment that explicitly incorporates landslide size. This novel approach integrates the predicted occurrence probability (spatial probability) of landslides and predicted size (area) of potential landslides for a slope-unit to obtain a landslide susceptibility value for that slope-unit. The results of a case study showed that, from a quantitative point of view, integrating spatial probability and size in slope-unit-based landslide susceptibility assessment can bring remarkable increases of AUC (Area under the ROC curve) values. For slope-unit-based scenarios using the logistic regression method and the neural network method, the average increase of AUC brought by incorporating landslide size is up to 0.0627 and 0.0606, respectively. Slope-unit-based landslide susceptibility models incorporating landslide size had utilized the spatial extent information of historical landslides, which was dropped in models not incorporating landslide size, and therefore can make potential improvements. Nevertheless, additional case studies are still needed to further evaluate the applicability of the proposed approach. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

10 pages, 23727 KiB

Open AccessArticle

Assessment of Landslide Susceptibility Based on Multiresolution Image Segmentation and Geological Factor Ratings

by GongHao Duan, JunChi Zhang and Shuiping Zhang

Int. J. Environ. Res. Public Health 2020, 17(21), 7863; https://doi.org/10.3390/ijerph17217863 - 27 Oct 2020

Cited by 9 | Viewed by 2109

Abstract

Evaluating the susceptibility of regional landslides is one of the core steps in spatial landslide prediction. Starting from multiresolution image segmentation and object-oriented classification theory, this paper uses the four parameters of entropy, energy, correlation, and contrast from remote-sensing images in the Zigui–Badong section of Three Gorges Reservoir as image texture factors; the original image data for the study area were divided into 2279 objects after segmentation. According to the various indicators of the existing historical landslide database in the Three Gorges Reservoir area, combined with the classification processing steps for different types of multistructured data, the relevant geological evaluation factors, including the slope gradient, slope structure, and engineering rock group, were rated based on expert experience. From the perspective of the object-oriented segmentation of multiresolution images and geological factor rating classification, the C5.0 decision tree susceptibility classification model was constructed for the prediction of four types of landslide susceptibility units in the Zigui–Badong section. The mapping results show that the engineering rock group of a high-susceptibility unit usually develops in soft rock or soft–hard interphase rock groups, and the slope is between 15°–30°. The model results show that the average accuracy is 91.64%, and the kappa coefficients are 0.84 and 0.51, indicating that the C5.0 decision tree algorithm provides good accuracy and can clearly divide landslide susceptibility levels for a specific area, respectively. This landslide susceptibility classification, based on multiresolution image segmentation and geological factor classification, has potential applicability. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

27 pages, 7910 KiB

Open AccessArticle

Effect of Soil Geomechanical Properties and Geo-Environmental Factors on Landslide Predisposition at Mount Oku, Cameroon

by Wamba Danny Love Djukem, Anika Braun, Armand Sylvain Ludovic Wouatong, Christian Guedjeo, Katrin Dohmen, Pierre Wotchoko, Tomas Manuel Fernandez-Steeger and Hans-Balder Havenith

Int. J. Environ. Res. Public Health 2020, 17(18), 6795; https://doi.org/10.3390/ijerph17186795 - 17 Sep 2020

Cited by 22 | Viewed by 4477

Abstract

In this work, we explored a novel approach to integrate both geo-environmental and soil geomechanical parameters in a landslide susceptibility model. A total of 179 shallow to deep landslides were identified using Google Earth images and field observations. Moreover, soil geomechanical properties of 11 representative soil samples were analyzed. The relationship between soil properties was evaluated using the Pearson correlation coefficient and geotechnical diagrams. Membership values were assigned to each soil property class, using the fuzzy membership method. The information value method allowed computing the weight value of geo-environmental factor classes. From the soil geomechanical membership values and the geo-environmental factor weights, three landslide predisposition models were produced, two separate models and one combined model. The results of the soil testing allowed classifying the soils in the study area as highly plastic clays, with high water content, swelling, and shrinkage potential. Some geo-environmental factor classes revealed their landslide prediction ability by displaying high weight values. While the model with only soil properties tended to underrate unstable and stable areas, the model combining soil properties and geo-environmental factors allowed a more precise identification of stability conditions. The geo-environmental factors model and the model combining geo-environmental factors and soil properties displayed predictive powers of 80 and 93%, respectively. It can be concluded that the spatial analysis of soil geomechanical properties can play a major role in the detection of landslide prone areas, which is of great interest for site selection and planning with respect to sustainable development at Mount Oku. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

23 pages, 6747 KiB

Open AccessArticle

Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment

by Viet-Ha Nhu, Ayub Mohammadi, Himan Shahabi, Baharin Bin Ahmad, Nadhir Al-Ansari, Ataollah Shirzadi, John J. Clague, Abolfazl Jaafari, Wei Chen and Hoang Nguyen

Int. J. Environ. Res. Public Health 2020, 17(14), 4933; https://doi.org/10.3390/ijerph17144933 - 8 Jul 2020

Cited by 107 | Viewed by 7647

Abstract

We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

20 pages, 7983 KiB

Open AccessArticle

Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda

by Martin Kuradusenge, Santhi Kumaran and Marco Zennaro

Int. J. Environ. Res. Public Health 2020, 17(11), 4147; https://doi.org/10.3390/ijerph17114147 - 10 Jun 2020

Cited by 47 | Viewed by 7705

Abstract

Landslides fall under natural, unpredictable and most distractive disasters. Hence, early warning systems of such disasters can alert people and save lives. Some of the recent early warning models make use of Internet of Things to monitor the environmental parameters to predict the disasters. Some other models use machine learning techniques (MLT) to analyse rainfall data along with some internal parameters to predict these hazards. The prediction capability of the existing models and systems are limited in terms of their accuracy. In this research paper, two prediction modelling approaches, namely random forest (RF) and logistic regression (LR), are proposed. These approaches use rainfall datasets as well as various other internal and external parameters for landslide prediction and hence improve the accuracy. Moreover, the prediction performance of these approaches is further improved using antecedent cumulative rainfall data. These models are evaluated using the receiver operating characteristics, area under the curve (ROC-AUC) and false negative rate (FNR) to measure the landslide cases that were not reported. When antecedent rainfall data is included in the prediction, both models (RF and LR) performed better with an AUC of 0.995 and 0.997, respectively. The results proved that there is a good correlation between antecedent precipitation and landslide occurrence rather than between one-day rainfall and landslide occurrence. In terms of incorrect predictions, RF and LR improved FNR to 10.58% and 5.77% respectively. It is also noted that among the various internal factors used for prediction, slope angle has the highest impact than other factors. Comparing both the models, LR model’s performance is better in terms of FNR and it could be preferred for landslide prediction and early warning. LR model’s incorrect prediction rate FNR = 9.61% without including antecedent precipitation data and 3.84% including antecedent precipitation data. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

30 pages, 9739 KiB

Open AccessArticle

Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

by Viet-Ha Nhu, Ataollah Shirzadi, Himan Shahabi, Sushant K. Singh, Nadhir Al-Ansari, John J. Clague, Abolfazl Jaafari, Wei Chen, Shaghayegh Miraki, Jie Dou, Chinh Luu, Krzysztof Górski, Binh Thai Pham, Huu Duy Nguyen and Baharin Bin Ahmad

Int. J. Environ. Res. Public Health 2020, 17(8), 2749; https://doi.org/10.3390/ijerph17082749 - 16 Apr 2020

Cited by 180 | Viewed by 12267

Abstract

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Figure 1

Review

Jump to: Research

24 pages, 37221 KiB

Open AccessReview

Worldwide Research Trends in Landslide Science

by Paúl Carrión-Mero, Néstor Montalván-Burbano, Fernando Morante-Carballo, Adolfo Quesada-Román and Boris Apolo-Masache

Int. J. Environ. Res. Public Health 2021, 18(18), 9445; https://doi.org/10.3390/ijerph18189445 - 7 Sep 2021

Cited by 65 | Viewed by 11811

Abstract

Landslides are generated by natural causes and by human action, causing various geomorphological changes as well as physical and socioeconomic loss of the environment and human life. The study, characterization and implementation of techniques are essential to reduce land vulnerability, different socioeconomic sector susceptibility and actions to guarantee better slope stability with a significant positive impact on society. The aim of this work is the bibliometric analysis of the different types of landslides that the United States Geological Survey (USGS) emphasizes, through the SCOPUS database and the VOSviewer software version 1.6.17, for the analysis of their structure, scientific production, and the close relationship with several scientific fields and its trends. The methodology focuses on: (i) search criteria; (ii) data extraction and cleaning; (iii) generation of graphs and bibliometric mapping; and (iv) analysis of results and possible trends. The study and analysis of landslides are in a period of exponential growth, focusing mainly on techniques and solutions for the stabilization, prevention, and categorization of the most susceptible hillslope sectors. Therefore, this research field has the full collaboration of various authors and places a significant focus on the conceptual evolution of the landslide science. Full article

(This article belongs to the Special Issue Landslide Risk Assessment and Mitigation)

► Show Figures

Journal Menu

Journal Browser

Landslide Risk Assessment and Mitigation

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (12 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI