Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience

Topic Information

Dear Colleague,

Countries worldwide are subjected to new and complex challenges related to the intensification of the frequency and severity of natural disasters because of the impact of climate change, rapid demographic growth, and intense urbanization. These challenges have a significant socio-economic impact because of the large-scale damage due to natural disasters. Indeed, natural disasters generally cover large areas, causing substantial human losses, severe environmental damage, and destruction of infrastructures that support social and economic activity.

The latest advances in monitoring using IoT, crowdsourcing, satellites, and drones provide new opportunities to collect large amounts of data related to natural disasters.

The use of machine learning and big data enables the development of effective solutions that improve urban systems' resilience to natural disasters, including a better understanding of the response of complex socio-technical systems to natural disasters, the development of early warning systems, rapid scanning of damage, optimization of emergency actions, use of automation to reduce and protect critical infrastructures, and the adaptation of infrastructures to the new level of natural hazards.

The objective of this Topic is to share the latest developments in this area with a focus on the following questions:

• What are the new scientific challenges related to the intensification of natural disasters (floods, earthquakes, storms, heat waves, disasters, wildfire and landslides)?
• How could digital technology (IoT, crowdsourcing, and satellite) enhance natural disaster monitoring?
• How could ML and BigData empower real-time analysis of data related to natural disasters?
• How could ML and BigData improve the efficiency of early warning systems?
• How could ML and BigData help adaptation strategies to natural disasters?
• How could ML and BigData help reduce damage related to natural disasters?

Prof. Dr. Isam Shahrour
Dr. Marwan Alheib
Dr. Anna Brdulak
Prof. Dr. Fadi Comair
Dr. Carlo Giglio
Prof. Dr. Xiongyao Xie
Prof. Dr. Yasin Fahjan
Dr. Salah Zidi
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Earth earth	2.1	3.3	2020	21.7 Days	CHF 1200	Submit
GeoHazards geohazards	-	2.6	2020	20.4 Days	CHF 1000	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	36.2 Days	CHF 1700	Submit
Land land	3.2	4.9	2012	17.8 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
Smart Cities smartcities	7.0	11.2	2018	25.8 Days	CHF 2000	Submit
Infrastructures infrastructures	2.7	5.2	2016	16.8 Days	CHF 1800	Submit
Automation automation	-	2.9	2020	20.6 Days	CHF 1000	Submit

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Published Papers (4 papers)

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All Earth GeoHazards IJGI Land Remote Sensing Smart Cities Infrastructures Automation

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33 pages, 629 KiB  

Open AccessArticle

Enhancing Smart City Connectivity: A Multi-Metric CNN-LSTM Beamforming Based Approach to Optimize Dynamic Source Routing in 6G Networks for MANETs and VANETs

by Vincenzo Inzillo, David Garompolo and Carlo Giglio

Smart Cities 2024, 7(5), 3022-3054; https://doi.org/10.3390/smartcities7050118 (registering DOI) - 17 Oct 2024

Abstract

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel [...] Read more.

The advent of Sixth Generation (6G) wireless technologies introduces challenges and opportunities for Mobile Ad Hoc Networks (MANETs) and Vehicular Ad Hoc Networks (VANETs), necessitating a reevaluation of traditional routing protocols. This paper introduces the Multi-Metric Scoring Dynamic Source Routing (MMS-DSR), a novel enhancement of the Dynamic Source Routing (DSR) protocol, designed to meet the demands of 6G-enabled MANETs and the dynamic environments of VANETs. MMS-DSR integrates advanced technologies and methodologies to enhance routing performance in dynamic scenarios. Key among these is the use of a CNN-LSTM-based beamforming algorithm, which optimizes beamforming vectors dynamically, exploiting spatial-temporal variations characteristic of 6G channels. This enables MMS-DSR to adapt beam directions in real time based on evolving network conditions, improving link reliability and throughput. Furthermore, MMS-DSR incorporates a multi-metric scoring mechanism that evaluates routes based on multiple QoS parameters, including latency, bandwidth, and reliability, enhanced by the capabilities of Massive MIMO and the IEEE 802.11ax standard. This ensures route selection is context-aware and adaptive to changing dynamics, making it effective in urban settings where vehicular and mobile nodes coexist. Additionally, the protocol uses machine learning techniques to predict future route performance, enabling proactive adjustments in routing decisions. The integration of dynamic beamforming and machine learning allows MMS-DSR to effectively handle the high mobility and variability of 6G networks, offering a robust solution for future wireless communications, particularly in smart cities. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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Figure 1

Figure 1
<p>Flowchart for MMS-DSR Architecture.</p> Full article ">Figure 2
<p>Network topology diagram illustrating the routes from Node A to Node J with respective metrics.</p> Full article ">Figure 3
<p>Network topology diagram illustrating the routes from Node A to Node J with respective metrics.</p> Full article ">Figure 4
<p>CNN-LSTM model architecture for MMS-DSR.</p> Full article ">Figure 5
<p>Network topology diagram illustrating the routes from Node A to Node J with respective metrics.</p> Full article ">Figure 6
<p>Network topology diagram illustrating the routes from Node A to Node J with respective metrics.</p> Full article ">Figure 7
<p>Throughput comparison across varying numbers of vehicles.</p> Full article ">Figure 8
<p>Throughput comparison across varying vehicle speeds.</p> Full article ">Figure 9
<p>Latency comparison in function of vehicle density.</p> Full article ">Figure 10
<p>Latency comparison in function on vehicle speed.</p> Full article ">Figure 11
<p>Route discovery time vs. vehicle density.</p> Full article ">Figure 12
<p>Route discovery time vs. vehicle speed.</p> Full article ">Figure 13
<p>Routing overhead comparison in function of vehicle density.</p> Full article ">Figure 14
<p>Routing overhead comparison in function of vehicle speed.</p> Full article ">Figure 15
<p>Scalability comparison performance across increasing vehicle density.</p> Full article ">

21 pages, 1921 KiB  

Open AccessArticle

Utilizing Machine Learning and Multi-Station Observations to Investigate the Visibility of Sea Fog in the Beibu Gulf

by Qin Huang, Peng Zeng, Xiaowei Guo and Jingjing Lyu

Remote Sens. 2024, 16(18), 3392; https://doi.org/10.3390/rs16183392 - 12 Sep 2024

Viewed by 483

Abstract

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations [...] Read more.

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ($T_{\mathrm{a}}-T_{\mathrm{d}}$), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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Figure 1

Figure 1
<p>Location map of the meteorological observation stations in the Beibu Gulf.</p> Full article ">Figure 2
<p>Six-year average frequency of sea fog occurrence per hour for observation stations: (<b>a</b>) QZ, (<b>b</b>) FC, (<b>c</b>) FCG, (<b>d</b>) DX, (<b>e</b>) BH, (<b>f</b>) WZ, and (<b>g</b>) HP.</p> Full article ">Figure 3
<p>Hourly probability histograms of different fog categories at Beibu Gulf observation stations in terms of (<b>a</b>) Light Fog, (<b>b</b>) Heavy fog, (<b>c</b>) Dense fog, and (<b>d</b>) Severe dense fog.</p> Full article ">Figure 4
<p>Six-year average diurnal variation in air temperature on (<b>a</b>) fog and (<b>b</b>) non-fog days at observation stations in the Beibu Gulf.</p> Full article ">Figure 5
<p>Six-year average diurnal variation in specific humidity on (<b>a</b>) fog and (<b>b</b>) non-fog days at observation stations in the Beibu Gulf.</p> Full article ">Figure 6
<p>Six-year average diurnal variation in wind speeds on (<b>a</b>) fog and (<b>b</b>) non-fog days at observation stations in the Beibu Gulf.</p> Full article ">Figure 7
<p>Wind direction frequency for different wind speeds during fog hours at observation stations in the Beibu Gulf.</p> Full article ">Figure 8
<p>Performance comparison of predictive visibility and observed visibility of general (<b>a</b>) RF, (<b>b</b>) XGBoost, and (<b>c</b>) CatBoost models.</p> Full article ">Figure 9
<p>Performance of General RF models: predicted visibility vs. observed visibility for (<b>a</b>) QZ, (<b>b</b>) FC, (<b>c</b>) FCG, (<b>d</b>) DX, (<b>e</b>) BH, (<b>f</b>) WZ, and (<b>g</b>) HP.</p> Full article ">Figure 10
<p>Performance of the general XGBoost model: predicted visibility vs. observed visibility for (<b>a</b>) QZ, (<b>b</b>) FC, (<b>c</b>) FCG, (<b>d</b>) DX, (<b>e</b>) BH, (<b>f</b>) WZ, and (<b>g</b>) HP.</p> Full article ">Figure 11
<p>Performance of CatBoost models: predicted visibility vs. observed visibility for (<b>a</b>) QZ, (<b>b</b>) FC, (<b>c</b>) FCG, (<b>d</b>) DX, (<b>e</b>) BH, (<b>f</b>) WZ, and (<b>g</b>) HP.</p> Full article ">

19 pages, 6613 KiB  

Open AccessArticle

Multi-Type Structural Damage Image Segmentation via Dual-Stage Optimization-Based Few-Shot Learning

by Jiwei Zhong, Yunlei Fan, Xungang Zhao, Qiang Zhou and Yang Xu

Smart Cities 2024, 7(4), 1888-1906; https://doi.org/10.3390/smartcities7040074 - 22 Jul 2024

Viewed by 674

Abstract

The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have [...] Read more.

The timely and accurate recognition of multi-type structural surface damage (e.g., cracks, spalling, corrosion, etc.) is vital for ensuring the structural safety and service performance of civil infrastructure and for accomplishing the intelligent maintenance of smart cities. Deep learning and computer vision have made profound impacts on automatic structural damage recognition using nondestructive test techniques, especially non-contact vision-based algorithms. However, the recognition accuracy highly depends on the training data volume and damage completeness in the conventional supervised learning pipeline, which significantly limits the model performance under actual application scenarios; the model performance and stability for multi-type structural damage categories are still challenging. To address the above issues, this study proposes a dual-stage optimization-based few-shot learning segmentation method using only a few images with supervised information for multi-type structural damage recognition. A dual-stage optimization paradigm is established encompassing an internal network optimization based on meta-task and an external meta-learning machine optimization based on meta-batch. The underlying image features pertinent to various structural damage types are learned as prior knowledge to expedite adaptability across diverse damage categories via only a few samples. Furthermore, a mathematical framework of optimization-based few-shot learning is formulated to intuitively express the perception mechanism. Comparative experiments are conducted to verify the effectiveness and necessity of the proposed method on a small-scale multi-type structural damage image set. The results show that the proposed method could achieve higher segmentation accuracies for various types of structural damage than directly training the original image segmentation network. In addition, the generalization ability for the unseen structural damage category is also validated. The proposed method provides an effective solution to achieve image-based structural damage recognition with high accuracy and robustness for bridges and buildings, which assists the unmanned intelligent inspection of civil infrastructure using drones and robotics in smart cities. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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Figure 1

Figure 1
<p>Overall schematic of dual-stage optimization-based few-shot learning for multi-type structural damage segmentation.</p> Full article ">Figure 2
<p>Network structure of internal semantic segmentation U-Net model.</p> Full article ">Figure 3
<p>Schematic of ship connection between the same stage of encoder and decoder.</p> Full article ">Figure 4
<p>Representative image-annotation pairs of investigated multi-type structural damage.</p> Full article ">Figure 4 Cont.
<p>Representative image-annotation pairs of investigated multi-type structural damage.</p> Full article ">Figure 5
<p>Representative test results of image segmentation for multi-type structural damage.</p> Full article ">Figure 5 Cont.
<p>Representative test results of image segmentation for multi-type structural damage.</p> Full article ">Figure 6
<p>Comparative boxplots of test evaluation metrics using DOFSL and original U-Net for multi-type structural damage. (Solid lines represent the min, Q1 quartile, Q2 quartile, Q3 quartile, and max values of the statistical evaluation metric, dashed lines indicate the value range, and circles denote the outliers).</p> Full article ">Figure 7
<p>Representative test segmentation results for unseen category of steel corrosion.</p> Full article ">Figure 7 Cont.
<p>Representative test segmentation results for unseen category of steel corrosion.</p> Full article ">Figure 8
<p>Comparative boxplots of test evaluation metrics using DOFSL and original U-Net for unseen category of steel corrosion. (Solid lines represent the min, Q1 quartile, Q2 quartile, Q3 quartile, and max values of the statistical evaluation metric, and dashed lines indicate the value range).</p> Full article ">Figure 9
<p>Comparative boxplots of test evaluation metrics using DOFSL and original U-Net. (Solid lines represent the min, Q1 quartile, Q2 quartile, Q3 quartile, and max values of the statistical evaluation metric, dashed lines indicate the value range, and circles denote the outliers).</p> Full article ">

19 pages, 11545 KiB  

Open AccessArticle

Bridging Human Expertise with Machine Learning and GIS for Mine Type Prediction and Classification

by Adib Saliba, Kifah Tout, Chamseddine Zaki and Christophe Claramunt

ISPRS Int. J. Geo-Inf. 2024, 13(7), 259; https://doi.org/10.3390/ijgi13070259 - 20 Jul 2024

Viewed by 937

Abstract

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of [...] Read more.

This paper introduces an intelligent model that combines military expertise with the latest advancements in machine learning (ML) and Geographic Information Systems (GIS) to support humanitarian demining decision-making processes, by predicting mined areas and classifying them by mine type, difficulty and priority of clearance. The model is based on direct input and validation from field decision-makers for their practical applicability and effectiveness, and accurate historical demining data extracted from military databases. With a survey polling the inputs of demining experts, 95% of the responses came with an affirmation of the potential of the model to reduce threats and increase operational efficiency. It includes military-specific factors that factor in the proximity to strategic locations as well as environmental variables like vegetation cover and terrain resolution. With Gradient Boosting algorithms such as XGBoost and LightGBM, the accuracy rate is almost 97%. Such precision levels further enhance threat assessment, better allocation of resources, and around a 30% reduction in the cost and time of conducting demining operations, signifying a strong synergy of human expertise with algorithmic precision for maximal safety and effectiveness in demining. Full article

(This article belongs to the Topic Machine Learning and Big Data Analytics for Natural Disaster Reduction and Resilience)

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<p>Demining process and extensions.</p> Full article ">Figure 2
<p>Study area.</p> Full article ">Figure 3
<p>Study area samples (grid 10 × 10) (Reference [<a href="#B1-ijgi-13-00259" class="html-bibr">1</a>]).</p> Full article ">Figure 4
<p>Observation posts and confrontation line.</p> Full article ">Figure 5
<p>Anti-vehicle mines along the southern border.</p> Full article ">Figure 6
<p>Predictions for mined areas along the southern border.</p> Full article ">Figure 7
<p>Predictions for anti-vehicle mined areas.</p> Full article ">Figure 8
<p>High-priority prediction in agricultural areas.</p> Full article ">Figure 9
<p>Difficulty prediction from the referenced model.</p> Full article ">Figure 10
<p>Difficulty prediction from the current model.</p> Full article ">Figure 11
<p>Feature importance.</p> Full article ">Figure 12
<p>Predictions on unseen data.</p> Full article ">

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