Search

Abstract Developing a regional damage function to quickly estimate direct economic losses (DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function (RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance. The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.

Natural disasters are becoming increasingly regional, frequent, and complex. Semi-quantitative risk assessments based on risk indices or relative risk levels lack quantitative parameters to characterize losses adequately. This study focuses on earthquakes and geological disasters, quantitatively assessing the risk of probable maximum loss (PML) from multi-hazard on the Tibet Plateau (TP) in terms of both absolute and relative values. The results indicate that total PML from multi-hazard on the TP is estimated at 465.5 billion CNY, with a relative value of 5.3%. The total loss value of towns with very high absolute PML is estimated at 254.8 billion CNY, while the relative loss value of towns with very high relative PML is 25.1%. Towns with high absolute value of PML for multi-hazard may have higher fixed asset values, while those with high relative value imply higher vulnerability. Adjusting building structures and reducing the vulnerability of buildings could effectively mitigate the risk of disaster losses caused by multiple hazards.

Precisely capturing and manipulating microparticles is the key to exploring microscopic mysteries. Optical tweezers play a crucial role in facilitating these tasks. However, existing optical tweezers are limited by their dependence on specific beam modes, which restrict their ability to flexibly switch and manipulate optical traps, thereby limiting their application in complex scientific challenges. Here, we propose a new method to achieve type switching and manipulation of optical traps using a single structured beam via optical coherence engineering. A conjugate-model random structured beam with a switch is designed. By altering the state of the switch, we can change the type of optical cage, enabling the capture of different particle types. Furthermore, the range, strength, and position of the optical trap can be controlled by adjusting the initial beam parameters. We hope that optical coherence engineering will extend the capabilities of existing structured optical tweezers, paving the way for advances in future optical tweezers applications.

Building collapse is a leading cause of casualties and economic losses during disasters. Accurate and timely assessment of building damage is critical for effective emergency response and recovery efforts. This article provides a comprehensive review of 242 papers, summarizing the progress and challenges in using deep learning models with remote sensing images for building damage detection. The study specifically examines datasets, deep learning models, and applications. The article highlights the scarcity of publicly available training datasets for hazard-damaged building detection, with only 13 datasets identified. This scarcity significantly limits the data accessible for deep learning training. Building damage detection models can be classified into single-temporal and bitemporal methods. The majority of studies focus on single-temporal methods, which utilize postevent images and employ convolutional neural network algorithms. These methods have shown promising results in comprehensive performance evaluation. Building damage detection systems typically involve two levels (damaged or undamaged) or four levels (degree of damage) classification. The performance accuracy of these systems varies widely, ranging from 52.2% to 95.82% for two-level classification and 14% to 86.5% for four-level classification. The differences in accuracy can be attributed to both the detection methods and the classification systems used, illustrating the complexity of building damage detection. Based on the current research, challenges in building damage detection include the insufficiency of datasets and limited accuracy in classifying the four damage categories. Future studies could focus on improving detection accuracy by considering different damage categories and exploring the fusion of multisource data.

Abstract On 6 February 2023, two 7.8 magnitude earthquakes consecutively hit south-central Türkiye, causing great concern from all governments, the United Nations, academia, and all sectors of society. Analyses indicate that there is also a high possibility of strong earthquakes with a magnitude of 7.8 or above occurring in the western region of China in the coming years. China is a country that is highly susceptible to catastrophic disasters such as earthquakes, floods, and other natural calamities, which can cause significant damages to both human life and property, as well as widespread impacts on the society. Currently, China’s capacity for disaster prevention and control is still limited. In order to effectively reduce the impact of catastrophic disasters, ensure the safety of people’s lives and property to the greatest extent possible, maintain social stability in high-risk areas, and ensure high-quality and sustainable regional development, it is urgent to improve the seismic resistance level of houses and critical infrastructure in high earthquake risk zones and increase the earthquake-resistant design level of houses in high-risk fault areas with frequent seismic activities; significantly enhance the ability to defend against extreme weather and ocean disasters in economically developed areas along the southeastern coast, as well as the level of fortification in response to extreme meteorological and hydrological disasters of coastal towns/cities and key infrastructure; vigorously enhance the emergency response capacity and disaster risk prevention level in western and ethnic minority regions; comprehensively improve the defense level of residential areas and major infrastructure in high geological hazard risk zones with flash floods, landslides, and mudslides; systematically promote national disaster prevention and mitigation education; and greatly enhance the societal disaster risk reduction ability, including catastrophic insurance.

Earthquake-induced landslides (EQILs) represent a serious secondary disaster of earthquakes, and conducting an effective assessment of earthquake-induced landslide susceptibility (ELSA) post-earthquake is helpful in reducing risk. In light of the diverse demands for ELSA across different time periods following an earthquake and the growing availability of data, this paper proposes using remote sensing data to dynamically update the ELSA model. By studying the Ms 6.2 earthquake in Jishishan County, Gansu Province, China, on 18 December 2023, rapid assessment results were derived from 12 pre-trained ELSA models combined with the spatial distribution of historical earthquake-related landslides immediately after the earthquake for early warning. Throughout the entire emergency response stage, the ELSA model was dynamically updated by integrating the EQILs points interpreted from remote sensing images as new training data to enhance assessment accuracy. After the emergency phase, the remote sensing interpretation results were compiled to create the new EQILs inventory. A high landslide potential area was identified using a re-trained model based on the updated inventory, offering a valuable reference for risk management during the recovery phase. The study highlights the importance of integrating remote sensing into ELSA model updates and recommends utilizing time-dependent remote sensing data for sampling to enhance the effectiveness of ELSA.

Aiming to solve the problems that a wind turbine foundation with a foundation pipe may suffer from grouting, where the concrete around the interface collapses and the interface disintegrates under a long-term wind load, a kind of wind turbine foundation with a constrained structural shear connector is proposed. In this article, the scaling model tests and a finite element simulation of a traditional stud foundation pipe, perforated steel shear connector foundation pipe, and three groups of constrained structural shear connector foundation pipes with different anchored depths are presented. The force transmission mechanism and damage mechanism of constrained structural shear connector wind turbine foundations are revealed, and the shear resistance of a constrained structural shear connector is analyzed. The influences of buried depth and other parameters on the mechanical properties of the shear connector are also investigated. The results show that the constrained structural shear connector has the advantages of stronger interfacial stiffness and significant force transfer and diffusion, and can more effectively connect the foundation pipe and concrete foundation to work together. It can give full play to the material advantages of concrete and reinforcements, and effectively improve the embedded stiffness and durability of concrete foundations. It can solve the problem of cracks in concrete caused by local pressure. At the same time, it is suggested that the diameter of the surrounding concrete should be in the range of 3 to 4 D, and the embedment depth of the stud should not be less than 0.4 D to give full play to the performance of the constrained structural shear connector.

Accurate and efficient post-earthquake building damage assessment methods enable key building damage information to be obtained more quickly after an earthquake, providing strong support for rescue and reconstruction efforts. Although many methods have been proposed, most have limited effect on accurately extracting severely damaged and collapsed buildings, and they cannot meet the needs of emergency response and rescue operations. Therefore, in this paper, we develop a novel building damage heterogeneity enhancement network for pixel-level building damage classification of post-earthquake unmanned aerial vehicle (UAV) and remote sensing data. The proposed BDHE-Net includes the following three modules: a data augmentation module (DAM), a building damage attention module (BDAM), and a multilevel feature adaptive fusion module (MFAF), which are used to alleviate the weight deviation of intact and slightly damaged categories during model training, pay attention to the heterogeneous characteristics of damaged buildings, and enhance the extraction of house integrity contour information at different resolutions of the image. In addition, a combined loss function is used to focus more attention on the small number of severely damaged and collapsed classes. The proposed model was tested on remote sensing and UAV images acquired from the Afghanistan and Baoxing earthquakes, and the combined loss function and the role of the three modules were studied. The results show that compared with the state-of-the-art methods, the proposed BDHE-Net achieves the best results, with an F1 score improvement of 6.19–8.22%. By integrating the DBA, BDAM, and MFAF modules and combining the loss functions, the model’s classification accuracy for severely damaged and collapsed categories can be improved.

Most of the current studies on the flood susceptibility of the Tibetan Plateau (TP) have relied on historical observations, lacking future projections and are not conductive to adaptive strategy optimization. Here, we examine the dynamics of flood susceptibility using a logistic regression model. When combined with climate and land use changes, the flood susceptibility in the TP is projected to increase under three Shared Socioeconomic Pathways (SSP). By the 2090 s, compared to the baseline period (1960–2020), the areas classified as very highly susceptible will experience an increase of 3.63 %, 6.17 %, and 15.26 % under the SSP126, SSP245, and SSP585 scenarios, respectively. The regions with high (4.98 %) and very high (6.88 %) flood susceptibility are primarily concentrated in densely populated areas located in the eastern and southern parts of the TP. As significant factors influencing flood susceptibility, the mean annual precipitation in the TP is expected to increase by 114 mm, 135 mm, and 223 mm from the baseline to the 2090 s under the SSP126, SSP245, and SSP585 scenarios, respectively. Additionally, the mean annual temperature is projected to rise by 2.2 ℃, 3.5 ℃, and 7.1 ℃, respectively. Furthermore, there will be a substantial expansion in the built-up area within the TP, with the percentage of built-up area increasing from 0.12 % in 2020 to 0.32 % by 2095. Our findings provide a quantitative assessment of how both natural environmental factors and human disturbances contribute to the susceptibility of the TP to flooding. Moving forward, it is expected that the built-up area on the northeastern edge of the TP will experience significant growth, resulting in an increased vulnerability to floods. Therefore, it is imperative to implement rational land use and land cover planning measures in order to mitigate the impact of flood disasters.

Based on situational awareness and information sharing, social media are regarded as a significant data source for disaster emergency management. Many studies have shown that social media can be used for rapid damage assessments during typhoon disasters, while few studies were able to extract victim information through social media. This study aims to determine whether and how we can mine accurate typhoon victim locations and spatial distributions using microblogs from Sina Weibo, one of the largest social media platforms in China, using a case study of the 2019 Super Typhoon Lekima. We first used the latent Dirichlet allocation (LDA) algorithm to classify disaster-related microblogs and exclude irrelevant information. Then, the SnowNLP library was applied to calculate the sentiment score. The negative sentiment contained victim and injury information but was not specific enough. Finally, Euclidean distance and Euclidean distance considering vulnerability were select to identify victim locations 72 hours after Lekima landed using Ordering Point to Identify the Cluster Structure (OPTICS) algorithm. Compared to the real victim locations, the hit rate of the former was 23.5%, while the latter was 31.8%. These results demonstrate that victim information recognition based on multi-source data fusion from Sina Weibo could be an effective new method for assisting disaster emergency response and rescue during typhoons.

Abstract Tropical cyclone (TC) damage function (DF) is widely used to model TC‐event level damage and thus assess the TC risk for a country or region. The scalability of these DFs at more localized scales, such as the province scale, has not been systematically explored. We use a unique Chinese data set to examine the damage at the TC‐event scale and province scale. Our results show that the parameters and performance of TC DF are spatially dependent. For a sigmoidal DF, the parameter dependence is manifested by a flatter curve calibrated on the TC‐event scale compared to the province scale. In the case of a power‐law DF, the dependence of its parameters is evident in the statistically more significant coefficients of the explanatory variables that are aggregated to the TC‐event scale, compared to the province scale. Performance comparison results further reveal that the scale dependence of performance is related to the type of DF. Integrating hazard, exposure, and vulnerability, the power‐law DF complements the typical sigmoidal DF, producing more accurate estimates of direct economic loss and annual average damage at both the TC‐event and province scales. However, its performance, compared to that of the sigmoidal DF, is more influenced by the scale at which it is calibrated. Our findings elucidate scale‐related research questions in TC risk assessment, offer insights into the selection of DFs, and inspire the future prospect of using multiple DFs to reduce the functional uncertainty.

The census and management of hazard-bearing entities, along with the integrity of data quality, form crucial foundations for disaster risk assessment and zoning. By addressing the challenge of feature confusion, prevalent in single remotely sensed image recognition methods, this paper introduces a novel method, Spatially Constrained Deep Learning (SCDL), that combines deep learning with spatial constraint strategies for the extraction of disaster-bearing bodies, focusing on dams as a typical example. The methodology involves the creation of a dam dataset using a database of dams, followed by the training of YOLOv5, Varifocal Net, Faster R-CNN, and Cascade R-CNN models. These models are trained separately, and highly confidential dam location information is extracted through parameter thresholding. Furthermore, three spatial constraint strategies are employed to mitigate the impact of other factors, particularly confusing features, in the background region. To assess the method’s applicability and efficiency, Qinghai Province serves as the experimental area, with dam images from the Google Earth Pro database used as validation samples. The experimental results demonstrate that the recognition accuracy of SCDL reaches 94.73%, effectively addressing interference from background factors. Notably, the proposed method identifies six dams not recorded in the GOODD database, while also detecting six dams in the database that were previously unrecorded. Additionally, four dams misdirected in the database are corrected, contributing to the enhancement and supplementation of the global dam geo-reference database and providing robust support for disaster risk assessment. In conclusion, leveraging open geographic data products, the comprehensive framework presented in this paper, encompassing deep learning target detection technology and spatial constraint strategies, enables more efficient and accurate intelligent retrieval of disaster-bearing bodies, specifically dams. The findings offer valuable insights and inspiration for future advancements in related fields.

Abstract Oxygen (O2) is the most abundant molecule in the atmosphere after nitrogen. Previous studies have documented that oxygen concentration remains nearly constant (20.946%) at all altitudes. Here we show for the first time that oxygen concentration varies significantly from earlier consensus and shows strong spatial and seasonal differences. Field observations on the Qinghai-Tibetan Plateau (QTP) indicate oxygen concentration of 19.94–20.66% (2018, n = 80), 19.98–20.78% (2019, n = 166) and 19.97–20.73% (2020, n = 176), all statistically different from earlier reports (p

Fu’s subcutaneous needling (FSN) is a novel acupuncture approach. Although it has been popularized and widely used by acupuncture practitioners, theoretical research on its mechanism of action remains lacking. This study establishes an immunological hypothesis for the efficacy of FSN basis existing knowledge in biomedical science, histology and physiology, and traditional acupuncture mechanisms. The theoretical inference indicates that FSN treatment may affect the body's immune system through subcutaneous connective tissue, similar to triggering a cobweb-like system. This article also proposes possible clinical implications of this theory, which require further studies to validate.

In this paper, we study the propagation properties of twisted Hermite–Gaussian Schell- model (THGSM) beams propagating in a uniaxial crystal orthogonal to the optical axis. We derive the concrete analytical expression of the cross-spectral density (CSD) function in the crystal and simulate the evolution characteristics of such beams, including normalized spectral intensity, the spectral degree of coherence (DOC), and effective beam width. We find that the spectral intensity distribution exhibits a non-circular symmetric self-splitting while rotating, and the distribution of the spectral DOC is non-circular symmetric rotationally distorted, which is quite different from that in an isotropic medium. The initial beam parameters and crystal parameters both affect the distribution of spectral intensity and DOC. Furthermore, increasing the twist factor and adjusting the ratio of the extraordinary light refractive index and the ordinary light refractive index ne/no of the uniaxial crystal can suppress the beam expansion as propagating in the crystal. Our results show that the uniaxial crystal can be used to determine whether light beams carry a twist phase or not, and to modulate the characteristics of light beams.

Abstract In this article, we recall the United Nations’ 30-year journey in disaster risk reduction strategy and framework, review the latest progress and key scientific and technological questions related to the United Nations disaster risk reduction initiatives, and summarize the framework and contents of disaster risk science research. The object of disaster risk science research is the “disaster system” consisting of hazard, the geographical environment, and exposed units, with features of regionality, interconnectedness, coupling, and complexity. Environmental stability, hazard threat, and socioeconomic vulnerability together determine the way that disasters are formed, establish the spatial extent of disaster impact, and generate the scale of losses. In the formation of a disaster, a conducive environment is the prerequisite, a hazard is the necessary condition, and socioeconomic exposure is the sufficient condition. The geographical environment affects local hazard intensity and therefore can change the pattern of loss distribution. Regional multi-hazard, disaster chain, and disaster compound could induce complex impacts, amplifying or attenuating hazard intensity and changing the scope of affected areas. In the light of research progress, particularly in the context of China, we propose a three-layer disaster risk science disciplinary structure, which contains three pillars (disaster science, disaster technology, and disaster governance), nine core areas, and 27 research fields. Based on these elements, we discuss the frontiers in disaster risk science research.

Abstract The relationship between natural hazard-induced disasters and macroeconomic growth has been examined widely on global and national scales, but little research has been focused on the subnational level, especially in China. We examined the impacts of natural hazard-induced disasters on the regional growth in China based on subnational panel data for the period from 1990 to 2016. First, we used the number of people affected and the direct economic losses as the measures of the scale of disasters. Then, we used the direct damages of meteorological disasters and earthquakes as disaster measures separately to examine the impacts of different disaster types. Finally, we performed intraregional effects regressions to observe the spatial heterogeneity within the regions. The results show that the adverse short-term effects of disasters is most pronounced in the central region, while the direct damage of disasters is a positive stimulus of growth in the whole of China. However, this stimulus is observed in a lagged way and is reflected differently—meteorological disasters in central and eastern China and earthquakes in western China are related to regional growth. The results demonstrate that the short-term macroeconomic impacts of these disasters in the three geographical regions of China largely depend on regional economic development levels and the disaster types.

The existence of multiple disaster-hit regions with spatially heterogeneous economic structures is vital to the investigation of the spatial ripple effects of disaster disturbances in the inter-regional supply-demand chain. We employed the inter-regional input-output (IRIO) model to modify the adaptive regional input-output model (ARIO) and reproduce indirect economic losses (IELs) after the Bohai Sea ice disaster event in the mainland of China. The results showed that the total IEL in China comprised 38% of the direct economic loss. The disaster area (the Bohai Rim region) lost 1460.55 million CNY, and other provinces were burdened with 49% of the total IEL. Agricultural services absorbed 44% of the total IEL in China. Furthermore, lingering effects of the disaster were detected in non-disaster areas ∼5–10 months after the removal of the spatial ripple effect compared with the disaster areas. Finally, the difference in total IEL in China caused by spatial heterogeneity in the supply-demand chain was ∼46.8%. Our findings suggest that the spatial ripple effect and the spatial heterogeneity of economic structures play an important role in the extent of disaster impacts on inter-regional economic systems.

Due to the complexity of airport background and runway structure, the performances of most runway extraction methods are limited. Furthermore, at present, the military fields attach greater importance to semantic changes of some objects in the airport, but few studies have been done on this subject. To address these issues, this paper proposes an accurate runway change analysis method, which comprises two stages: airport runway extraction and runway change analysis. For the former stage, some airport knowledge, such as chevron markings and runway edge markings, are first applied in combination with multiple features of runways to improve the accuracy. In addition, the proposed method can accomplish airport runway extraction automatically. For the latter, semantic information and vector results of runway changes can be obtained simultaneously by comparing bi-temporal runway extraction results. In six test images with about 0.5-m spatial resolution, the average completeness of runway extraction is nearly 100%, and the average quality is nearly 89%. In addition, the final experiment using two sets of bi-temporal very high-resolution (VHR) images of runway changes demonstrated that semantic results obtained by our method are consistent with the real situation and the final accuracy is over 80%. Overall, the airport knowledge, especially chevron markings for runways and runway edge markings, are critical to runway recognition/detection, and multiple features of runways, such as shape and parallel line features, can further improve the completeness and accuracy of runway extraction. Finally, a small step has been taken in the study of runway semantic changes, which cannot be accomplished by change detection alone.

Tropical cyclones (TCs) have devastating impacts and are responsible for significant damage. Consequently, for TC-induced direct economic loss (DEL) attribution all factors associated with risk (i.e. hazard, exposure and vulnerability) must be examined. This research quantifies the relationship between TC-induced DELs and maximum wind speed, asset value and Gross Domestic Product (GDP) per capita using a regression model with TC records from 2000 to 2015 for China’s mainland area. The coefficient of the maximum wind speed term indicates that a doubling of the maximum wind speed increases DELs by 225% [97%, 435%] when the other two variables are held constant. The coefficient of the asset value term indicates that a doubling of asset value exposed to TCs increases DELs by 79% [58%, 103%]; thus, if hazard and vulnerability are assumed to be constant in the future, then a dramatic escalation in TC-induced DELs will occur given the increase in asset value, suggesting that TC-prone areas with rapid urbanization and wealth accumulation will inevitably be subject to higher risk. Reducing the asset value exposure via land-use planning, for example, is important for decreasing TC risk. The coefficient of GDP per capita term indicates that a doubling in GDP per capita could decrease DELs by 54% [39%, 66%]. Because accumulated assets constantly increase people’s demand for improved security, stakeholders must invest in risk identification, early warning systems, emergency management and other effective prevention measures with increasing income to reduce vulnerability. This research aims to quantitatively connect TC risk (expected DELs, specifically) to physical and socioeconomic drivers and emphasizes how human dimensions could contribute to TC risk. Moreover, the model can be used to estimate TC risk under climate change and future socioeconomic development in the context of China.

Exposure and vulnerability are the main contributing factors of growing impact from climate-related disasters globally. Understanding the spatiotemporal dynamic patterns of vulnerability is important for designing effective disaster risk mitigation and adaptation measures. At national scale, most cross-country studies have suggested that economic vulnerability to disasters decreases as income increases, especially for developing countries. Research covering sub-national climate-related natural disasters is indispensable to obtaining a comprehensive understanding of the effect of regional economic growth on vulnerability reduction. Taking China as a case, this subnational scale study shows that economic development is correlated with the significant reduction in human fatalities but increase in direct economic losses (DELs) from climate-related disasters since 1949. The long-term trend in climate-related disaster vulnerability, reflected by mortality (1978–2015) and DELs (1990–2015) as a share of the total population and Gross Domestic Product, has seen significant decline among all economic regions in China. While notable differences remain among its West, Central and East economic regions, the temporal vulnerability change has been converging. The study further demonstrated that economic development level is correlated with human and economic vulnerability to climate-related disasters, and this vulnerability decreased with the increase of per-capita income. This study suggested that economic development can have nuanced effects on overall human and economic vulnerability to climate-related disasters. We argue that climate change science needs to acknowledge and examine the different pathways of vulnerability effects related to economic development.

Accurate exposure estimation is essential for seismic risk assessment. Recent rapid urbanization and economic growth in China have led to massive spatiotemporal changes in both the asset value and GDP exposed to seismic hazards. Using available GDP data, the asset value dataset produced by Wu et al ( 2014a ) and spatial disaggregation technology, gridded maps of GDP and asset value are overlaid with the latest seismic map to investigate spatiotemporal changes in economic exposure in the most seismically hazardous areas (MSHAs) in China in 1990, 2000 and 2010. We found that 15.4% of China’s asset value and 14.1% of China’s GDP were located in MSHAs in 2010, and the asset value and GDP exposed to MSHAs reached 15.9 trillion CNY and 6.2 trillion CNY, respectively, with average annual rates of increase of 14.4% and 11.3% over the two decades. The evidence of increased exposure provides valuable information regarding whom or what risk managers should give the most attention based on the economic exposure changes in earthquake-prone areas of China. Notably, the North China seismic belt, which is associated with the largest economic exposure to earthquakes and a rapidly increasing rate of economic exposure compared to those in other seismic belts, and the Qinghai-Tibet seismic belt, which has the highest earthquake occurrence, are two seismic belts of interest. A more detailed study is required to determine the relationship between increased economic exposure and earthquake disaster losses combined with hazard level and vulnerability.

Brain functional networks model the brain's ability to exchange information across different regions, aiding in the understanding of the cognitive process of human visual attention during target searching, thereby contributing to the advancement of camouflage evaluation. In this study, images with various camouflage effects were presented to observers to generate electroencephalography (EEG) signals, which were then used to construct a brain functional network. The topological parameters of the network were subsequently extracted and input into a machine learning model for training. The results indicate that most of the classifiers achieved accuracy rates exceeding 70%. Specifically, the Logistic algorithm achieved an accuracy of 81.67%. Therefore, it is possible to predict target camouflage effectiveness with high accuracy without the need to calculate discovery probability. The proposed method fully considers the aspects of human visual and cognitive processes, overcomes the subjectivity of human interpretation, and achieves stable and reliable accuracy. [ABSTRACT FROM AUTHOR]