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Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management

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

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 27976

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Special Issue Editors

Prof. Dr. Dieu Tien Bui

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Guest Editor

Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Notodden, Norway
Interests: sensors; LiDAR; GIS and geospatial technology; geo-hazards; artificial intelligence; soil engineering; marine geology; environmental managements
Special Issues, Collections and Topics in MDPI journals

Dr. Hossein Moayedi

E-Mail Website
Guest Editor

Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Interests: sensors; geotechnical engineering; landslide assessment; natural hazards; meta-heuristic optimization; machine learning; field monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards, such as forest fire, earthquake, landslide, land subsidence, soil salinity, erosion, and floods, are still worldwide problems that have widespread impacts on people, infrastructure, and the environment; therefore, new and efficient solutions for improving the natural hazard mitigation, resilience, and managements are essential for sustainable societies.

This Special Issue would like to invite scholars to share recently-developed advances in sensors and intelligent techniques for natural hazard modeling, prediction, and management, with emphasis on the problems addressed by advanced geospatial artificial intelligence. This is an emerging scientific multidiscipline, which combines innovations in sensors, geospatial technology, remote sensing, UAV photogrammetry, advanced artificial intelligence techniques, data mining, hybrid and ensemble techniques, metaheuristic optimization, and high-performance computing to extract knowledge from geospatial data.

We kindly invite scientists to contribute novel and original research to this Special Issue, attributing at least one of the below topics:

Advances in sensors, intelligent techniques, and meta-heuristic optimizations for natural hazard modeling and management;
Recent advances in monitoring, early warning, and detection systems;
Real-world case studies of natural hazards (forest fire, earthquake, landslide, land subsidence, soil salinity, erosion, and floods) with findings of clear interest to the scientific community;

Finally, the authors are encouraged to share data and codes (if possible) for considering reproducibility of their works as well as future improvements of research.

Prof. Dr. Dieu Tien Bui
Dr. Hossein Moayedi
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

sensors
natural hazards
environmental problems
intelligent techniques
management

Published Papers (6 papers)

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Research

24 pages, 5683 KiB

Open AccessArticle

Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning

by Ratiranjan Jena, Biswajeet Pradhan, Abdullah Al-Amri, Chang Wook Lee and Hyuck-jin Park

Sensors 2020, 20(16), 4369; https://doi.org/10.3390/s20164369 - 05 Aug 2020

Cited by 19 | Viewed by 8577

Abstract

Earthquake prediction is a popular topic among earth scientists; however, this task is challenging and exhibits uncertainty therefore, probability assessment is indispensable in the current period. During the last decades, the volume of seismic data has increased exponentially, adding scalability issues to probability assessment models. Several machine learning methods, such as deep learning, have been applied to large-scale images, video, and text processing; however, they have been rarely utilized in earthquake probability assessment. Therefore, the present research leveraged advances in deep learning techniques to generate scalable earthquake probability mapping. To achieve this objective, this research used a convolutional neural network (CNN). Nine indicators, namely, proximity to faults, fault density, lithology with an amplification factor value, slope angle, elevation, magnitude density, epicenter density, distance from the epicenter, and peak ground acceleration (PGA) density, served as inputs. Meanwhile, 0 and 1 were used as outputs corresponding to non-earthquake and earthquake parameters, respectively. The proposed classification model was tested at the country level on datasets gathered to update the probability map for the Indian subcontinent using statistical measures, such as overall accuracy (OA), F1 score, recall, and precision. The OA values of the model based on the training and testing datasets were 96% and 92%, respectively. The proposed model also achieved precision, recall, and F1 score values of 0.88, 0.99, and 0.93, respectively, for the positive (earthquake) class based on the testing dataset. The model predicted two classes and observed very-high (712,375 km²) and high probability (591,240.5 km²) areas consisting of 19.8% and 16.43% of the abovementioned zones, respectively. Results indicated that the proposed model is superior to the traditional methods for earthquake probability assessment in terms of accuracy. Aside from facilitating the prediction of the pixel values for probability assessment, the proposed model can also help urban-planners and disaster managers make appropriate decisions regarding future plans and earthquake management. Full article

(This article belongs to the Special Issue Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management)

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24 pages, 8468 KiB

Open AccessArticle

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

by Mohammad Mehrabi, Biswajeet Pradhan, Hossein Moayedi and Abdullah Alamri

Sensors 2020, 20(6), 1723; https://doi.org/10.3390/s20061723 - 19 Mar 2020

Cited by 78 | Viewed by 3882

Abstract

Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area. Full article

(This article belongs to the Special Issue Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management)

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23 pages, 9619 KiB

Open AccessArticle

Low-Cost GNSS Solution for Continuous Monitoring of Slope Instabilities Applied to Madonna Del Sasso Sanctuary (NW Italy)

by Davide Notti, Alberto Cina, Ambrogio Manzino, Alessio Colombo, Iosif Horea Bendea, Paolo Mollo and Daniele Giordan

Sensors 2020, 20(1), 289; https://doi.org/10.3390/s20010289 - 04 Jan 2020

Cited by 32 | Viewed by 4477

Abstract

In recent years, the development of low-cost GNSS sensors allowed monitoring in a continuous way movement related to natural processes like landslides with increasing accuracy and limited efforts. In this work, we present the first results of an experimental low-cost GNSS continuous monitoring applied to an unstable slope affecting the Madonna del Sasso Sanctuary (NW Italy). The courtyard of Sanctuary is built on two unstable blocks delimited by a high cliff. Previous studies and non-continuous monitoring showed that blocks suffer a seasonal cycle of thermal expansion and a long-term trend to downslope a few millimeters (2/3) per year. The presence of a continuous monitoring solution could be an essential help to better understand the kinematics of unstable slope. Continuous monitoring could help to forecast a possible paroxysm phase that could end with a failure of the unstable area. The first year of experimental measurements shows a millimetric accuracy of low-cost GNSS, and the long-term trend is in agreement with other monitoring data. We also propose a methodological approach that considers the use of semi-automatized procedures for the identification of anomalous trends and a risk communication strategy. Pro and cons of the proposed methodology are also discussed. Full article

(This article belongs to the Special Issue Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management)

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12 pages, 4042 KiB

Open AccessArticle

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

by Junwei Ma, Xiaoxu Niu, Xiao Liu, Yankun Wang, Tao Wen and Junrong Zhang

Sensors 2020, 20(1), 219; https://doi.org/10.3390/s20010219 - 30 Dec 2019

Cited by 13 | Viewed by 3024

Abstract

A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change (

\bar{Δ T I R}

) can be selected as a precursor to landslide failure. Full article

(This article belongs to the Special Issue Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management)

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28 pages, 6620 KiB

Open AccessArticle

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

by Hossein Moayedi, Abdolreza Osouli, Dieu Tien Bui and Loke Kok Foong

Sensors 2019, 19(21), 4698; https://doi.org/10.3390/s19214698 - 29 Oct 2019

Cited by 32 | Viewed by 3418

Abstract

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models. Full article

(This article belongs to the Special Issue Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management)

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13 pages, 3774 KiB

Open AccessArticle

Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic

by Hossein Moayedi, Dieu Tien Bui and Loke Kok Foong

Sensors 2019, 19(21), 4636; https://doi.org/10.3390/s19214636 - 24 Oct 2019

Cited by 23 | Viewed by 3686

Abstract

By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameters. Full article

(This article belongs to the Special Issue Advances in Sensors and Intelligent Techniques for Natural Hazard Modeling and Management)

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