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Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 January 2019) | Viewed by 81465

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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

Prof. Dr. Francisco Martínez-Álvarez

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Guest Editor

Department of Computer Science, Pablo de Olavide University, ES-41013 Seville, Spain
Interests: big data; machine learning; time series; forecasting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards are extreme and unexpected threats resulting from natural processes of the Earth, such as landslides, floods, hurricanes, tornados, volcanoes, or any other natural phenomena that may cause harm to humans.

In this sense, this Special Issue encourages authors to share recent advances in natural hazard management, with a particular emphasis on issues addressed by means of advanced machine learning and big data analytics and remote sensing techniques.

Massive amounts of data are stored in almost all disciplines. Remote sensing is not an exception, since very large time series or high-resolution satellite and aerial images are sources of valuable information. How to extract useful information from these big data sources is not, by contrast, an easy task due to the computational and infrastructural costs involved.

Very powerful approaches have been developed in the context of advanced machine learning and big data analytics during the last few years. Such approaches deal with large datasets, considering all samples and measurements, as well as including many additional features. With them, advanced machine learning and big data methods for extracting relevant patterns, high performance computing or data visualization are being nowadays successfully applied to the field of remote sensing.

For all the aforementioned, we kindly invite the scientific community to contribute to this Special Issue by submitting novel and original research addressing at least one of the following topics, in the context of data science and big data:

Recent advances in information fusion for natural hazards management.
Recent advances in spatial modeling for natural hazards management.
Recent advances in temporal modeling for natural hazards management.
Real-world case study with findings with clear interest to the scientific community.

Finally, authors are encouraged to share codes and data so that their studies can be easily reproducible and serve as seed for future improvements.

Prof. Dr. Dieu Tien Bui
Prof. Dr. Francisco Martínez-Álvarez
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. Remote Sensing 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 2700 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

Data science and big data analytics
Remote sensing
GIS
Spatio-temporal analysis
Information fusion
Natural hazards management

Published Papers (11 papers)

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Editorial

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3 pages, 182 KiB

Open AccessEditorial

Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management

by Francisco Martínez-Álvarez and Dieu Tien Bui

Remote Sens. 2020, 12(2), 301; https://doi.org/10.3390/rs12020301 - 16 Jan 2020

Cited by 9 | Viewed by 2819

Abstract

This editorial summarizes the performance of the special issue entitled Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management, which was published at MDPI’s Remote Sensing journal. The special issue took place in years 2018 and 2019 and accepted a total of nine papers from authors of thirteen different countries. So far, these papers have dealt with 116 cites. Earthquakes, landslides, floods, wildfire and soil salinity were the topics analyzed. New methods were introduced, with applications of the utmost relevance. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

Research

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30 pages, 30403 KiB

Open AccessArticle

Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM

by Jie Dou, Ali P. Yunus, Dieu Tien Bui, Mehebub Sahana, Chi-Wen Chen, Zhongfan Zhu, Weidong Wang and Binh Thai Pham

Remote Sens. 2019, 11(6), 638; https://doi.org/10.3390/rs11060638 - 15 Mar 2019

Cited by 118 | Viewed by 7132

Abstract

Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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18 pages, 9596 KiB

Open AccessArticle

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

by Abolfazl Jaafari, Davood Mafi-Gholami, Binh Thai Pham and Dieu Tien Bui

Remote Sens. 2019, 11(6), 618; https://doi.org/10.3390/rs11060618 - 13 Mar 2019

Cited by 53 | Viewed by 6432

Abstract

Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape in the Zagros ecoregion, Iran. Dempster–Shafer-based evidential belief function (EBF) and the multivariate logistic regression (LR) were applied to a spatial dataset that represents 132 fire events from the period of 2007–2014 and twelve explanatory variables (altitude, aspect, slope degree, topographic wetness index (TWI), annual temperature, and rainfall, wind effect, land use, normalized difference vegetation index (NDVI), and distance to roads, rivers, and residential areas). While the EBF model successfully characterized each variable class by four probability mass functions in terms of wildfire probabilities, the LR model identified the variables that have a major impact on the probability of fire occurrence. Two distribution maps of wildfire probability were developed based upon the results of each model. In an ensemble modeling perspective, we combined the two probability maps. The results were verified and compared by the receiver operating characteristic (ROC) and the Wilcoxon Signed-Rank Test. The results showed that although an improved predictive accuracy (AUC = 0.864) can be achieved via an ensemble modeling of bivariate and multivariate statistics, the models fail to individually provide a satisfactory prediction of wildfire probability (EBF_AUC = 0.701; LR_AUC = 0.728). From these results, we recommend the employment of ensemble modeling approaches for different wildfire-prone landscapes. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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21 pages, 5587 KiB

Open AccessArticle

Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

by Pham Viet Hoa, Nguyen Vu Giang, Nguyen An Binh, Le Vu Hong Hai, Tien-Dat Pham, Mahdi Hasanlou and Dieu Tien Bui

Remote Sens. 2019, 11(2), 128; https://doi.org/10.3390/rs11020128 - 11 Jan 2019

Cited by 93 | Viewed by 11552

Abstract

Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical climates. This issue has become more severe and increasingly occurred in the Mekong River Delta of Vietnam. The main objective of this work is to map soil salinity intrusion in Ben Tre province located on the Mekong River Delta of Vietnam using the Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined with five state-of-the-art machine learning models, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Gaussian Processes (GP), Support Vector Regression (SVR), and Random Forests (RF). For this purpose, 63 soil samples were collected during the field survey conducted from 4–6 April 2018 corresponding to the Sentinel-1 SAR imagery. The performance of the five models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r). The results revealed that the GP model yielded the highest prediction performance (RMSE = 2.885, MAE = 1.897, and r = 0.808) and outperformed the other machine learning models. We conclude that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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31 pages, 5776 KiB

Open AccessArticle

A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China

by Yi Wang, Haoyuan Hong, Wei Chen, Shaojun Li, Dragan Pamučar, Ljubomir Gigović, Siniša Drobnjak, Dieu Tien Bui and Hexiang Duan

Remote Sens. 2019, 11(1), 62; https://doi.org/10.3390/rs11010062 - 30 Dec 2018

Cited by 115 | Viewed by 8370

Abstract

Floods are considered one of the most disastrous hazards all over the world and cause serious casualties and property damage. Therefore, the assessment and regionalization of flood disasters are becoming increasingly important and urgent. To predict the probability of a flood, an essential step is to map flood susceptibility. The main objective of this work is to investigate the use a novel hybrid technique by integrating multi-criteria decision analysis and geographic information system to evaluate flood susceptibility mapping (FSM), which is constructed by ensemble of decision making trial and evaluation laboratory (DEMATEL), analytic network process, weighted linear combinations (WLC) and interval rough numbers (IRN) techniques in the case study at Shangyou County, China. Specifically, we improve the DEMATEL method by applying IRN to determine connections in the network structure based on criteria and to accept imprecisions during collective decision making. The application of IRN can eliminate the necessity of additional information to define uncertain number intervals. Therefore, the quality of the existing data during collective decision making and experts’ perceptions that are expressed through an aggregation matrix can be retained. In this work, eleven conditioning factors associated with flooding were considered and historical flood locations were randomly divided into the training (70% of the total) and validation (30%) sets. The flood susceptibility map validates a satisfactory consistency between the flood-susceptible areas and the spatial distribution of the previous flood events. The accuracy of the map was evaluated by using objective measures of receiver operating characteristic (ROC) curve and area under the curve (AUC). The AUC values of the proposed method coupling with the WLC fuzzy technique for aggregation and flood susceptibility index are 0.988 and 0.964, respectively, which proves that the WLC fuzzy method is more effective for FSM in the study area. The proposed method can be helpful in predicting accurate flood occurrence locations with similar geographic environments and can be effectively used for flood management and prevention. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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20 pages, 5497 KiB

Open AccessArticle

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

by Min Ji, Lanfa Liu and Manfred Buchroithner

Remote Sens. 2018, 10(11), 1689; https://doi.org/10.3390/rs10111689 - 26 Oct 2018

Cited by 76 | Viewed by 8062

Abstract

Earthquake is one of the most devastating natural disasters that threaten human life. It is vital to retrieve the building damage status for planning rescue and reconstruction after an earthquake. In cases when the number of completely collapsed buildings is far less than intact or less-affected buildings (e.g., the 2010 Haiti earthquake), it is difficult for the classifier to learn the minority class samples, due to the imbalance learning problem. In this study, the convolutional neural network (CNN) was utilized to identify collapsed buildings from post-event satellite imagery with the proposed workflow. Producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and Kappa were used as evaluation metrics. To overcome the imbalance problem, random over-sampling, random under-sampling, and cost-sensitive methods were tested on selected test A and test B regions. The results demonstrated that the building collapsed information can be retrieved by using post-event imagery. SqueezeNet performed well in classifying collapsed and non-collapsed buildings, and achieved an average OA of 78.6% for the two test regions. After balancing steps, the average Kappa value was improved from 41.6% to 44.8% with the cost-sensitive approach. Moreover, the cost-sensitive method showed a better performance on discriminating collapsed buildings, with a PA value of 51.2% for test A and 61.1% for test B. Therefore, a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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27 pages, 5290 KiB

Open AccessArticle

A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides

by Dieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Chapi, Nhat-Duc Hoang, Binh Thai Pham, Quang-Thanh Bui, Chuyen-Trung Tran, Mahdi Panahi, Baharin Bin Ahmad and Lee Saro

Remote Sens. 2018, 10(10), 1538; https://doi.org/10.3390/rs10101538 - 25 Sep 2018

Cited by 90 | Viewed by 6460

Abstract

This research aims at proposing a new artificial intelligence approach (namely RVM-ICA) which is based on the Relevance Vector Machine (RVM) and the Imperialist Competitive Algorithm (ICA) optimization for landslide susceptibility modeling. A Geographic Information System (GIS) spatial database was generated from Lang Son city in Lang Son province (Vietnam). This GIS database includes a landslide inventory map and fourteen landslide conditioning factors. The suitability of these factors for landslide susceptibility modeling in the study area was verified by the Information Gain Ratio (IGR) technique. A landslide susceptibility prediction model based on RVM-ICA and the GIS database was established by training and prediction phases. The predictive capability of the new approach was evaluated by calculations of sensitivity, specificity, accuracy, and the area under the Receiver Operating Characteristic curve (AUC). In addition, to assess the applicability of the proposed model, two state-of-the-art soft computing techniques including the support vector machine (SVM) and logistic regression (LR) were used as benchmark methods. The results of this study show that RVM-ICA with AUC = 0.92 achieved a high goodness-of-fit based on both the training and testing datasets. The predictive capability of RVM-ICA outperformed those of SVM with AUC = 0.91 and LR with AUC = 0.87. The experimental results confirm that the newly proposed model is a very promising alternative to assist planners and decision makers in the task of managing landslide prone areas. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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32 pages, 10578 KiB

Open AccessArticle

Landslide Detection and Susceptibility Mapping by AIRSAR Data Using Support Vector Machine and Index of Entropy Models in Cameron Highlands, Malaysia

by Dieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Chapi, Mohsen Alizadeh, Wei Chen, Ayub Mohammadi, Baharin Bin Ahmad, Mahdi Panahi, Haoyuan Hong and Yingying Tian

Remote Sens. 2018, 10(10), 1527; https://doi.org/10.3390/rs10101527 - 23 Sep 2018

Cited by 134 | Viewed by 9796

Abstract

Since landslide detection using the combination of AIRSAR data and GIS-based susceptibility mapping has been rarely conducted in tropical environments, the aim of this study is to compare and validate support vector machine (SVM) and index of entropy (IOE) methods for landslide susceptibility assessment in Cameron Highlands area, Malaysia. For this purpose, ten conditioning factors and observed landslides were detected by AIRSAR data, WorldView-1 and SPOT 5 satellite images. A spatial database was generated including a total of 92 landslide locations encompassing the same number of observed and detected landslides, which was divided into training (80%; 74 landslide locations) and validation (20%; 18 landslide locations) datasets. Results of the difference between observed and detected landslides using root mean square error (RMSE) indicated that only 16.3% error exists, which is fairly acceptable. The validation process was performed using statistical-based measures and the area under the receiver operating characteristic (AUROC) curves. Results of validation process indicated that the SVM model has the highest values of sensitivity (88.9%), specificity (77.8%), accuracy (83.3%), Kappa (0.663) and AUROC (84.5%), followed by the IOE model. Overall, the SVM model applied to detected landslides is considered to be a promising technique that could be tested and utilized for landslide susceptibility assessment in tropical environments. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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34 pages, 16499 KiB

Open AccessArticle

A Hybrid Analytic Network Process and Artificial Neural Network (ANP-ANN) Model for Urban Earthquake Vulnerability Assessment

by Mohsen Alizadeh, Ibrahim Ngah, Mazlan Hashim, Biswajeet Pradhan and Amin Beiranvand Pour

Remote Sens. 2018, 10(6), 975; https://doi.org/10.3390/rs10060975 - 19 Jun 2018

Cited by 91 | Viewed by 9782

Abstract

Vulnerability assessment is one of the prerequisites for risk analysis in disaster management. Vulnerability to earthquakes, especially in urban areas, has increased over the years due to the presence of complex urban structures and rapid development. Urban vulnerability is a result of human behavior which describes the extent of susceptibility or resilience of social, economic, and physical assets to natural disasters. The main aim of this paper is to develop a new hybrid framework using Analytic Network Process (ANP) and Artificial Neural Network (ANN) models for constructing a composite social, economic, environmental, and physical vulnerability index. This index was then applied to Tabriz City, which is a seismic-prone province in the northwestern part of Iran with recurring devastating earthquakes and consequent heavy casualties and damages. A Geographical Information Systems (GIS) analysis was used to identify and evaluate quantitative vulnerability indicators for generating an earthquake vulnerability map. The classified and standardized indicators were subsequently weighed and ranked using an ANP model to construct the training database. Then, standardized maps coupled with the training site maps were presented as input to a Multilayer Perceptron (MLP) neural network for producing an Earthquake Vulnerability Map (EVM). Finally, an EVM was produced for Tabriz City and the level of vulnerability in various zones was obtained. South and southeast regions of Tabriz City indicate low to moderate vulnerability, while some zones of the northeastern tract are under critical vulnerability conditions. Furthermore, the impact of the vulnerability of Tabriz City on population during an earthquake was included in this analysis for risk estimation. A comparison of the result produced by EVM and the Population Vulnerability (PV) of Tabriz City corroborated the validity of the results obtained by ANP-ANN. The findings of this paper are useful for decision-makers and government authorities to obtain a better knowledge of a city’s vulnerability dimensions, and to adopt preparedness strategies in the future for Tabriz City. The developed hybrid framework of ANP and ANN Models can easily be replicated and applied to other urban regions around the world for sustainability and environmental management. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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22 pages, 10384 KiB

Open AccessArticle

Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment

by Xueyi Shang, Xibing Li, Antonio Morales-Esteban, Gualberto Asencio-Cortés and Zewei Wang

Remote Sens. 2018, 10(3), 461; https://doi.org/10.3390/rs10030461 - 15 Mar 2018

Cited by 21 | Viewed by 6499

Abstract

Microseismic sensing taking advantage of sensors can remotely monitor seismic activities and evaluate seismic hazard. Compared with experts’ seismic event clusters, clustering algorithms are more objective, and they can handle many seismic events. Many methods have been proposed for seismic event clustering and the K-means clustering technique has become the most famous one. However, K-means can be affected by noise events (large location error events) and initial cluster centers. In this paper, a data field-based K-means clustering methodology is proposed for seismicity analysis. The application of synthetic data and real seismic data have shown its effectiveness in removing noise events as well as finding good initial cluster centers. Furthermore, we introduced the time parameter into the K-means clustering process and applied it to seismic events obtained from the Chinese Yongshaba mine. The results show that the time-event location distance and data field-based K-means clustering can divide seismic events by both space and time, which provides a new insight for seismicity analysis compared with event location distance and data field-based K-means clustering. The Krzanowski-Lai (KL) index obtains a maximum value when the number of clusters is five: the energy index (EI) shows that clusters C1, C3 and C5 have very critical periods. In conclusion, the time-event location distance, and the data field-based K-means clustering can provide an effective methodology for seismicity analysis and hazard assessment. In addition, further study can be done by considering time-event location-magnitude distances. Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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2 pages, 149 KiB

Open AccessErratum

Erratum: Dieu, T.B. et al. A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides. Remote Sens. 2018, 10, 1538

by Dieu Tien Bui, Himan Shahabi, Ataollah Shirzadi, Kamran Kamran Chapi, Nhat-Duc Hoang, Binh Thai Pham, Quang-Thanh Bui, Chuyen-Trung Tran, Mahdi Panahi, Baharin Bin Ahmad and Lee Saro

Remote Sens. 2019, 11(1), 57; https://doi.org/10.3390/rs11010057 - 29 Dec 2018

Cited by 5 | Viewed by 2430

Abstract

The authors wish to make the following corrections to this paper [...] Full article

(This article belongs to the Special Issue Advanced Machine Learning and Big Data Analytics in Remote Sensing for Natural Hazards Management)

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