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Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning

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

Deadline for manuscript submissions: closed (30 November 2020) | Viewed by 39017

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Prof. Dr. ByoungChul Ko

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Guest Editor

Department of Computer Engineering, Keimyung University, Shindang-Dong, Dalseo-Gu, Daegu 704-701,Republic of Korea
Interests: computer vision; pattern recognition; object detection tracking; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Sejin Park

E-Mail Website
Guest Editor

Dept. of Computer Engineering, Shindang-Dong, Dalseo-Gu, Daegu, Keimyung Univ. 704-701, Korea
Interests: ML-based system tuning; system software; intelligent storage system; blockchain; operating system

Special Issue Information

Dear Colleagues.

Along with global warming, various natural disasters such as typhoons, hurricanes, floods, droughts, fires, landslides, and marine (air) pollution are increasing. These natural disasters result in hundreds of thousands of victims and enormous damage to property each year. However, with the rapid development of information technology, natural disaster prevention is growing into a new field of research dealing with surveillance systems. The development of systems that analyze natural disasters using fusion technology of machine learning with various IoT sensors to predict and prevent damage from natural disasters has received extensive attention over the past decade.

The purpose of this Special Issue is to take this opportunity to publish the latest studies of several types of natural disasters and their warning systems that are based on fusion of machine learning, including deep learning with various sensor technologies.

In this Special Issue, you are invited to submit contributions of original research, advancements, developments, and experiments pertaining to machine learning combined with IoT sensors. Therefore, the Special Issue welcomes newly developed methods and ideas combining the data obtained from various sensors in the following fields (though not limited to them):

Fire monitoring system based on fusion of sensors and machine learning;
Floods or droughts monitoring system based on fusion of sensors and machine learning;
Typhoons or Hurricanes monitoring system based on fusion of sensors and machine learning;
Landslides monitoring system based on fusion of sensors and machine learning;
Monitoring of building collapse caused by natural disaster;
Marine (air) pollution monitoring system based on fusion of sensors and machine learning;
Various other natural disaster monitoring systems based on fusion of sensors and machine learning;
Deep network structure/learning algorithm for intelligent natural disaster monitoring;
Decision algorithms for intelligent natural disaster monitoring;
Fuzzy fusion of sensors, data, and information for intelligent natural disaster monitoring;
Machine learning for IoT and sensor research challenges intelligent natural disaster monitoring;
State-of-practice, research overview, experience reports, industrial experiments, and case studies in intelligent natural disaster monitoring.

Prof. Dr. ByoungChul Ko
Dr. Sejin Park
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.

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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 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.

Published Papers (7 papers)

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Research

17 pages, 1799 KiB

Open AccessArticle

Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)

by Miguel de Domingo, Nuria Ortigosa, Javier Sevilla and Sandra Roger

Sensors 2021, 21(3), 797; https://doi.org/10.3390/s21030797 - 25 Jan 2021

Cited by 3 | Viewed by 2227

Abstract

Forest fires are undesirable situations with tremendous impacts on wildlife and people’s lives. Reaching them quickly is essential to slowing down their expansion and putting them out in an effective manner. This work proposes an optimized distribution of fire stations in the province of Valencia (Spain) to minimize the impacts of forest fires. Using historical data about fires in the Valencia province, together with the location information about existing fire stations and municipalities, two different clustering techniques have been applied. Floyd–Warshall dynamic programming algorithm has been used to estimate the average times to reach fires among municipalities and fire stations in order to quantify the impacts of station relocation. The minimization was done approximately through k-means clustering. The outcomes with different numbers of clusters determined a predicted tradeoff between reducing the time and the cost of more stations. The results show that the proposed relocation of fire stations generally ensures faster arrival to the municipalities compared to the current disposition of fire stations. In addition, deployment costs associated with station relocation are also of paramount importance, so this factor was also taken into account in the proposed approach. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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21 pages, 5517 KiB

Open AccessArticle

Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection

by Mira Jeong, MinJi Park, Jaeyeal Nam and Byoung Chul Ko

Sensors 2020, 20(19), 5508; https://doi.org/10.3390/s20195508 - 25 Sep 2020

Cited by 25 | Viewed by 3970

Abstract

As the need for wildfire detection increases, research on wildfire smoke detection combining low-cost cameras and deep learning technology is increasing. Camera-based wildfire smoke detection is inexpensive, allowing for a quick detection, and allows a smoke to be checked by the naked eye. However, because a surveillance system must rely only on visual characteristics, it often erroneously detects fog and clouds as smoke. In this study, a combination of a You-Only-Look-Once detector and a long short-term memory (LSTM) classifier is applied to improve the performance of wildfire smoke detection by reflecting on the spatial and temporal characteristics of wildfire smoke. However, because it is necessary to lighten the heavy LSTM model for real-time smoke detection, in this paper, we propose a new method for applying the teacher–student framework to deep LSTM. Through this method, a shallow student LSTM is designed to reduce the number of layers and cells constituting the LSTM model while maintaining the original deep LSTM performance. As the experimental results indicate, our proposed method achieves up to an 8.4-fold decrease in the number of parameters and a faster processing time than the teacher LSTM while maintaining a similar detection performance as deep LSTM using several state-of-the-art methods on a wildfire benchmark dataset. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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27 pages, 8885 KiB

Open AccessArticle

A Hybrid Landslide Displacement Prediction Method Based on CEEMD and DTW-ACO-SVR—Cases Studied in the Three Gorges Reservoir Area

by Junrong Zhang, Huiming Tang, Tao Wen, Junwei Ma, Qinwen Tan, Ding Xia, Xiao Liu and Yongquan Zhang

Sensors 2020, 20(15), 4287; https://doi.org/10.3390/s20154287 - 31 Jul 2020

Cited by 34 | Viewed by 3593

Abstract

Accurately predicting the surface displacement of the landslide is important and necessary. However, most of the existing research has ignored the frequency component of inducing factors and how it affects the landslide deformation. Therefore, a hybrid displacement prediction model based on time series theory and various intelligent algorithms was proposed in this paper to study the effect of frequency components. Firstly, the monitoring displacement of landslide from the Three Gorges Reservoir area (TGRA) was decomposed into the trend and periodic components by complete ensemble empirical mode decomposition (CEEMD). The trend component can be predicted by the least square method. Then, time series of inducing factors like rainfall and reservoir level was reconstructed into high frequency components and low frequency components with CEEMD and t-test, respectively. The dominant factors were selected by the method of dynamic time warping (DTW) from the frequency components and other common factors (e.g., current monthly rainfall). Finally, the ant colony optimization-based support vector machine regression (ACO-SVR) is utilized for prediction purposes in the TGRA. The results demonstrate that after considering the frequency components of landslide-induced factors, the accuracy of the displacement prediction model based on ACO-SVR is better than that of other models based on SVR and GA-SVR. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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22 pages, 3064 KiB

Open AccessArticle

Forecasting the Concentration of Particulate Matter in the Seoul Metropolitan Area Using a Gaussian Process Model

by JoonHo Jang, Seungjae Shin, Hyunjin Lee and Il-Chul Moon

Sensors 2020, 20(14), 3845; https://doi.org/10.3390/s20143845 - 9 Jul 2020

Cited by 9 | Viewed by 2933

Abstract

Recently, the population of Seoul has been affected by particulate matter in the atmosphere. This problem can be addressed by developing an elaborate forecasting model to estimate the concentration of fine dust in the metropolitan area. We present a forecasting model of the fine dust concentration with an extended range of input variables, compared to existing models. The model takes inputs from holistic perspectives such as topographical features on the surface, chemical sources of the fine dusts, traffic and the human activities in sub-areas, and meteorological data such as wind, temperature, and humidity, of fine dust. Our model was evaluated by the index-of-agreement (IOA) and the root mean-squared error (RMSE) in predicting PM2.5 and PM10 over three subsequent days. Our model variations consist of linear regressions, ARIMA, and Gaussian process regressions (GPR). The GPR showed the best performance in terms of IOA that is over 0.6 in the three-day predictions. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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17 pages, 3571 KiB

Open AccessArticle

Two-Step Real-Time Night-Time Fire Detection in an Urban Environment Using Static ELASTIC-YOLOv3 and Temporal Fire-Tube

by MinJi Park and Byoung Chul Ko

Sensors 2020, 20(8), 2202; https://doi.org/10.3390/s20082202 - 13 Apr 2020

Cited by 49 | Viewed by 5883

Abstract

While the number of casualties and amount of property damage caused by fires in urban areas are increasing each year, studies on their automatic detection have not maintained pace with the scale of such fire damage. Camera-based fire detection systems have numerous advantages over conventional sensor-based methods, but most research in this area has been limited to daytime use. However, night-time fire detection in urban areas is more difficult to achieve than daytime detection owing to the presence of ambient lighting such as headlights, neon signs, and streetlights. Therefore, in this study, we propose an algorithm that can quickly detect a fire at night in urban areas by reflecting its night-time characteristics. It is termed ELASTIC-YOLOv3 (which is an improvement over the existing YOLOv3) to detect fire candidate areas quickly and accurately, regardless of the size of the fire during the pre-processing stage. To reflect the dynamic characteristics of a night-time flame, N frames are accumulated to create a temporal fire-tube, and a histogram of the optical flow of the flame is extracted from the fire-tube and converted into a bag-of-features (BoF) histogram. The BoF is then applied to a random forest classifier, which achieves a fast classification and high classification performance of the tabular features to verify a fire candidate. Based on a performance comparison against a few other state-of-the-art fire detection methods, the proposed method can increase the fire detection at night compared to deep neural network (DNN)-based methods and achieves a reduced processing time without any loss in accuracy. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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25 pages, 6344 KiB

Open AccessArticle

Machine Learning-Based Gully Erosion Susceptibility Mapping: A Case Study of Eastern India

by Sunil Saha, Jagabandhu Roy, Alireza Arabameri, Thomas Blaschke and Dieu Tien Bui

Sensors 2020, 20(5), 1313; https://doi.org/10.3390/s20051313 - 28 Feb 2020

Cited by 71 | Viewed by 4523

Abstract

Gully erosion is a form of natural disaster and one of the land loss mechanisms causing severe problems worldwide. This study aims to delineate the areas with the most severe gully erosion susceptibility (GES) using the machine learning techniques Random Forest (RF), Gradient Boosted Regression Tree (GBRT), Naïve Bayes Tree (NBT), and Tree Ensemble (TE). The gully inventory map (GIM) consists of 120 gullies. Of the 120 gullies, 84 gullies (70%) were used for training and 36 gullies (30%) were used to validate the models. Fourteen gully conditioning factors (GCFs) were used for GES modeling and the relationships between the GCFs and gully erosion was assessed using the weight-of-evidence (WofE) model. The GES maps were prepared using RF, GBRT, NBT, and TE and were validated using area under the receiver operating characteristic (AUROC) curve, the seed cell area index (SCAI) and five statistical measures including precision (PPV), false discovery rate (FDR), accuracy, mean absolute error (MAE), and root mean squared error (RMSE). Nearly 7% of the basin has high to very high susceptibility for gully erosion. Validation results proved the excellent ability of these models to predict the GES. Of the analyzed models, the RF (AUROC = 0.96, PPV = 1.00, FDR = 0.00, accuracy = 0.87, MAE = 0.11, RMSE = 0.19 for validation dataset) is accurate enough for modeling and better suited for GES modeling than the other models. Therefore, the RF model can be used to model the GES areas not only in this river basin but also in other areas with the same geo-environmental conditions. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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21 pages, 7954 KiB

Open AccessArticle

Earthquake Detection in a Static and Dynamic Environment Using Supervised Machine Learning and a Novel Feature Extraction Method

by Irshad Khan, Seonhwa Choi and Young-Woo Kwon

Sensors 2020, 20(3), 800; https://doi.org/10.3390/s20030800 - 1 Feb 2020

Cited by 37 | Viewed by 13205

Abstract

Detecting earthquakes using smartphones or IoT devices in real-time is an arduous and challenging task, not only because it is constrained with the hard real-time issue but also due to the similarity of earthquake signals and the non-earthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in real-time with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset. Full article

(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)

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