AI for Natural Disasters Detection, Prediction and Modeling

Abstract submission deadline

25 April 2025

Manuscript submission deadline

25 July 2025

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Topic Information

Dear Colleagues,

In recent years, we have witnessed escalating climate change and its increasing impact on global ecosystems, human lives, and the world economy. This situation calls for advanced tools that can leverage artificial intelligence (AI) for the early detection, prediction, and modeling of natural disasters. The increasing frequency and intensity of events such as wildfires, flooding, storms, and other catastrophic incidents necessitate innovative approaches for mitigation and response. This call for papers invites contributions that address the critical aspects of this interesting field, focusing on the integration of AI methodologies with remote sensing data. We encourage submissions that span a wide range of topics, including reviews of state-of-the-art AI applications for natural disaster management, risk assessment and hazard prediction; the use of AI to detect and track specific events; modeling techniques employing AI; and the development of advanced forecasting models utilizing AI methodologies.

The aim of this call is to bring together researchers and experts from various areas to foster collaborative efforts in developing cutting-edge solutions that will enhance our ability to anticipate, understand, and respond to the increasing challenges posed by natural disasters in an era of climate change.

Dr. Moulay A. Akhloufi
Dr. Mozhdeh Shahbazi
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
AI ai	3.1	7.2	2020	17.6 Days	CHF 1600	Submit
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800	Submit
Fire fire	3.0	3.1	2018	18.4 Days	CHF 2400	Submit
GeoHazards geohazards	-	2.6	2020	20.4 Days	CHF 1000	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit

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Published Papers (7 papers)

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All AI BDCC Fire GeoHazards Remote Sensing

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24 pages, 8367 KiB  

Open AccessArticle

Detecting Hailstorms in China from FY-4A Satellite with an Ensemble Machine Learning Model

by Qiong Wu, Yi-Xuan Shou, Yong-Guang Zheng, Fei Wu and Chun-Yuan Wang

Remote Sens. 2024, 16(18), 3354; https://doi.org/10.3390/rs16183354 - 10 Sep 2024

Viewed by 163

Abstract

Hail poses a significant meteorological hazard in China, leading to substantial economic and agricultural damage. To enhance the detection of hail and mitigate these impacts, this study presents an ensemble machine learning model (BPNN+Dtree) that combines a backpropagation neural network (BPNN) and a [...] Read more.

Hail poses a significant meteorological hazard in China, leading to substantial economic and agricultural damage. To enhance the detection of hail and mitigate these impacts, this study presents an ensemble machine learning model (BPNN+Dtree) that combines a backpropagation neural network (BPNN) and a decision tree (Dtree). Using FY-4A satellite and ERA5 reanalysis data, the model is trained on geostationary satellite infrared data and environmental parameters, offering comprehensive, all-day, and large-area hail monitoring over China. The ReliefF method is employed to select 13 key features from 29 physical quantities, emphasizing cloud-top and thermodynamic properties over dynamic ones as input features for the model to enhance its hail differentiation capability. The BPNN+Dtree ensemble model harnesses the strengths of both algorithms, improving the probability of detection (POD) to 0.69 while maintaining a reasonable false alarm ratio (FAR) on the test set. Moreover, the model’s spatial distribution of hail probability more closely matches the observational data, outperforming the individual BPNN and Dtree models. Furthermore, it demonstrates improved regional applicability over overshooting top (OT)-based methods in the China region. The identified high-frequency hail areas correspond to the north-south movement of the monsoon rain belt and are consistent with the northeast-southwest belt distribution observed using microwave-based methods. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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22 pages, 20392 KiB  

Open AccessArticle

AI-Driven Computer Vision Detection of Cotton in Corn Fields Using UAS Remote Sensing Data and Spot-Spray Application

by Pappu Kumar Yadav, J. Alex Thomasson, Robert Hardin, Stephen W. Searcy, Ulisses Braga-Neto, Sorin C. Popescu, Roberto Rodriguez III, Daniel E. Martin and Juan Enciso

Remote Sens. 2024, 16(15), 2754; https://doi.org/10.3390/rs16152754 - 27 Jul 2024

Viewed by 596

Abstract

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum ( [...] Read more.

To effectively combat the re-infestation of boll weevils (Anthonomus grandis L.) in cotton fields, it is necessary to address the detection of volunteer cotton (VC) plants (Gossypium hirsutum L.) in rotation crops such as corn (Zea mays L.) and sorghum (Sorghum bicolor L.). The current practice involves manual field scouting at the field edges, which often leads to the oversight of VC plants growing in the middle of fields alongside corn and sorghum. As these VC plants reach the pinhead squaring stage (5–6 leaves), they can become hosts for boll weevil pests. Consequently, it becomes crucial to detect, locate, and accurately spot-spray these plants with appropriate chemicals. This paper focuses on the application of YOLOv5m to detect and locate VC plants during the tasseling (VT) growth stage of cornfields. Our results demonstrate that VC plants can be detected with a mean average precision (mAP) of 79% at an Intersection over Union (IoU) of 50% and a classification accuracy of 78% on images sized 1207 × 923 pixels. The average detection inference speed is 47 frames per second (FPS) on the NVIDIA Tesla P100 GPU-16 GB and 0.4 FPS on the NVIDIA Jetson TX2 GPU, which underscores the relevance and impact of detection speed on the feasibility of real-time applications. Additionally, we show the application of a customized unmanned aircraft system (UAS) for spot-spray applications through simulation based on the developed computer vision (CV) algorithm. This UAS-based approach enables the near-real-time detection and mitigation of VC plants in corn fields, with near-real-time defined as approximately 0.02 s per frame on the NVIDIA Tesla P100 GPU and 2.5 s per frame on the NVIDIA Jetson TX2 GPU, thereby offering an efficient management solution for controlling boll weevil pests. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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16 pages, 6768 KiB  

Open AccessArticle

Landslide Susceptibility Assessment in Active Tectonic Areas Using Machine Learning Algorithms

by Tianjun Qi, Xingmin Meng and Yan Zhao

Remote Sens. 2024, 16(15), 2724; https://doi.org/10.3390/rs16152724 - 25 Jul 2024

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Abstract

The eastern margin of the Tibetan Plateau is one of the regions with the most severe landslide disasters on a global scale. With the intensification of seismic activity around the Tibetan Plateau and the increase in extreme rainfall events, the prevention of landslide [...] Read more.

The eastern margin of the Tibetan Plateau is one of the regions with the most severe landslide disasters on a global scale. With the intensification of seismic activity around the Tibetan Plateau and the increase in extreme rainfall events, the prevention of landslide disasters in the region is facing serious challenges. This article selects the Bailong River Basin located in this region as the research area, and the historical landslide data obtained from high-precision remote sensing image interpretation combined with field validation are used as the sample library. Using machine learning algorithms and data-driven landslide susceptibility assessment as the methods, 17 commonly used models and 17 important factors affecting the development of landslides are selected to carry out the susceptibility assessment. The results show that the BaggingClassifier model shows advantageous applicability in the region, and the landslide susceptibility distribution map of the Bailong River Basin was generated using this model. The results show that the road and population density are both high in very high and high susceptible areas, indicating that there is still a significant potential landslide risk in the basin. The quantitative evaluation of the main influencing factors emphasizes that distance to a road is the most important factor. However, due to the widespread utilization of ancient landslides by local residents for settlement and agricultural cultivation over hundreds of years, the vast majority of landslides are likely to have occurred prior to human settlement. Therefore, the importance of this factor may be overestimated, and the evaluation of the factors still needs to be dynamically examined in conjunction with the development history of the region. The five factors of NDVI, altitude, faults, average annual rainfall, and rivers have a secondary impact on landslide susceptibility. The research results have important significance for the susceptibility assessment of landslides in the complex environment of human–land interaction and for the construction of landslide disaster monitoring and early warning systems in the Bailong River Basin. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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22 pages, 11376 KiB  

Open AccessArticle

Robust Landslide Recognition Using UAV Datasets: A Case Study in Baihetan Reservoir

by Zhi-Hai Li, An-Chi Shi, Huai-Xian Xiao, Zi-Hao Niu, Nan Jiang, Hai-Bo Li and Yu-Xiang Hu

Remote Sens. 2024, 16(14), 2558; https://doi.org/10.3390/rs16142558 - 12 Jul 2024

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Abstract

The task of landslide recognition focuses on extracting the location and extent of landslides over large areas, providing ample data support for subsequent landslide research. This study explores the use of UAV and deep learning technologies to achieve robust landslide recognition in a [...] Read more.

The task of landslide recognition focuses on extracting the location and extent of landslides over large areas, providing ample data support for subsequent landslide research. This study explores the use of UAV and deep learning technologies to achieve robust landslide recognition in a more rational, simpler, and faster manner. Specifically, the widely successful DeepLabV3+ model was used as a blueprint and a dual-encoder design was introduced to reconstruct a novel semantic segmentation model consisting of Encoder1, Encoder2, Mixer and Decoder modules. This model, named DeepLab for Landslide (DeepLab4LS), considers topographic information as a supplement to DeepLabV3+, and is expected to improve the efficiency of landslide recognition by extracting shape information from relative elevation, slope, and hillshade. Additionally, a novel loss function term—Positive Enhanced loss (PE loss)—was incorporated into the training of DeepLab4LS, significantly enhancing its ability to understand positive samples. DeepLab4LS was then applied to a UAV dataset of Baihetan reservoir, where comparative tests demonstrated its high performance in landslide recognition tasks. We found that DeepLab4LS has a stronger inference capability for landslides with less distinct boundary information, and delineates landslide boundaries more precisely. More specifically, in terms of evaluation metrics, DeepLab4LS achieved a mean intersection over union (mIoU) of 76.0% on the validation set, which is a substantial 5.5 percentage point improvement over DeepLabV3+. Moreover, the study also validated the rationale behind the dual-encoder design and the introduction of PE loss through ablation experiments. Overall, this research presents a robust semantic segmentation model for landslide recognition that considers both optical and topographic semantics of landslides, emulating the recognition pathways of human experts, and is highly suitable for landslide recognition based on UAV datasets. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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22 pages, 7648 KiB  

Open AccessArticle

Fire-RPG: An Urban Fire Detection Network Providing Warnings in Advance

by Xiangsheng Li and Yongquan Liang

Fire 2024, 7(7), 214; https://doi.org/10.3390/fire7070214 - 26 Jun 2024

Viewed by 939

Abstract

Urban fires are characterized by concealed ignition points and rapid escalation, making the traditional methods of detecting early stage fire accidents inefficient. Thus, we focused on the features of early stage fire accidents, such as faint flames and thin smoke, and established a [...] Read more.

Urban fires are characterized by concealed ignition points and rapid escalation, making the traditional methods of detecting early stage fire accidents inefficient. Thus, we focused on the features of early stage fire accidents, such as faint flames and thin smoke, and established a dataset. We found that these features are mostly medium-sized and small-sized objects. We proposed a model based on YOLOv8s, Fire-RPG. Firstly, we introduced an extra very small object detection layer to enhance the detection performance for early fire features. Next, we optimized the model structure with the bottleneck in GhostV2Net, which reduced the computational time and the parameters. The Wise-IoUv3 loss function was utilized to decrease the harmful effects of low-quality data in the dataset. Finally, we integrated the low-cost yet high-performance RepVGG block and the CBAM attention mechanism to enhance learning capabilities. The RepVGG block enhances the extraction ability of the backbone and neck structures, while CBAM focuses the attention of the model on specific size objects. Our experiments showed that Fire-RPG achieved an mAP of 81.3%, an improvement of 2.2%. In addition, Fire-RPG maintained high detection performance across various fire scenarios. Therefore, our model can provide timely warnings and accurate detection services. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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15 pages, 3788 KiB  

Open AccessArticle

Wildfire Susceptibility Prediction Based on a CA-Based CCNN with Active Learning Optimization

by Qiuping Yu, Yaqin Zhao, Zixuan Yin and Zhihao Xu

Fire 2024, 7(6), 201; https://doi.org/10.3390/fire7060201 - 16 Jun 2024

Viewed by 585

Abstract

Wildfires cause great losses to the ecological environment, economy, and people’s safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as [...] Read more.

Wildfires cause great losses to the ecological environment, economy, and people’s safety and belongings. As a result, it is crucial to establish wildfire susceptibility models and delineate fire risk levels. It has been proven that the use of remote sensing data, such as meteorological and topographical data, can effectively predict and evaluate wildfire susceptibility. Accordingly, this paper converts meteorological and topographical data into fire-influencing factor raster maps for wildfire susceptibility prediction. The continuous convolutional neural network (CCNN for short) based on coordinate attention (CA for short) can aggregate different location information into channels of the network so as to enhance the feature expression ability; moreover, for different patches with different resolutions, the improved CCNN model does not need to change the structural parameters of the network, which improves the flexibility of the network application in different forest areas. In order to reduce the annotation of training samples, we adopt an active learning method to learn positive features by selecting high-confidence samples, which contributes to enhancing the discriminative ability of the network. We use fire probabilities output from the model to evaluate fire risk levels and generate the fire susceptibility map. Taking Chongqing Municipality in China as an example, the experimental results show that the CA-based CCNN model has a better classification performance; the accuracy reaches 91.7%, and AUC reaches 0.9487, which is 5.1% and 2.09% higher than the optimal comparative method, respectively. Furthermore, if an accuracy of about 86% is desired, our method only requires 50% of labeled samples and thus saves about 20% and 40% of the labeling efforts compared to the other two methods, respectively. Ultimately, the proposed model achieves the balance of high prediction accuracy and low annotation cost and is more helpful in classifying fire high warning zones and fire-free zones. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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12 pages, 3871 KiB  

Open AccessArticle

Multitemporal Dynamics of Fuels in Forest Systems Present in the Colombian Orinoco River Basin Forests

by Walter Garcia-Suabita, Mario José Pacheco and Dolors Armenteras

Fire 2024, 7(6), 171; https://doi.org/10.3390/fire7060171 - 21 May 2024

Viewed by 747

Abstract

In Colombia’s Orinoco, wildfires have a profound impact on ecosystem dynamics, particularly affecting savannas and forest–savanna transitions. Human activities have disrupted the natural fire regime, leading to increased wildfire frequency due to changes in land use, deforestation, and climate change. Despite extensive research [...] Read more.

In Colombia’s Orinoco, wildfires have a profound impact on ecosystem dynamics, particularly affecting savannas and forest–savanna transitions. Human activities have disrupted the natural fire regime, leading to increased wildfire frequency due to changes in land use, deforestation, and climate change. Despite extensive research on fire monitoring and prediction, the quantification of fuel accumulation, a critical factor in fire incidence, remains inadequately explored. This study addresses this gap by quantifying dead organic material (detritus) accumulation and identifying influencing factors. Using Brown transects across forests with varying fire intensities, we assessed fuel loads and characterized variables related to detritus accumulation over time. Employing factor analysis, principal components analysis, and a generalized linear mixed model, we determined the effects of various factors. Our findings reveal significant variations in biomass accumulation patterns influenced by factors such as thickness, wet and dry mass, density, gravity, porosity, and moisture content. Additionally, a decrease in fuel load over time was attributed to increased precipitation from three La Niña events. These insights enable more accurate fire predictions and inform targeted forest management strategies for fire prevention and mitigation, thereby enhancing our understanding of fire ecology in the Orinoco basin and guiding effective conservation practices. Full article

(This article belongs to the Topic AI for Natural Disasters Detection, Prediction and Modeling)

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