Search Results (23)

The integration of deep learning and remote sensing for the rapid detection of landslides from high-resolution remote sensing imagery plays a crucial role in post-disaster emergency response. However, the availability of publicly accessible deep learning datasets specifically for landslide detection remains limited, posing challenges for researchers in meeting task requirements. To address this issue, this study develops and releases a deep learning landslide dataset using Google Earth imagery, focusing on the impact zones of the 2008 Wenchuan Ms8.0 earthquake, the 2014 Ludian Ms6.5 earthquake, and the 2017 Jiuzhaigou Ms7.0 earthquake as the research areas. The dataset contains 2727 samples with a spatial resolution of 1.06 m. To enhance landslide recognition, a lightweight boundary-focused attention (BFA) mechanism designed using the Canny operator is adopted. This mechanism improves the model’s ability to emphasize landslide edge features and is integrated with the ResUNet model, forming the ResUNet–BFA architecture for landslide identification. The experimental results indicate that the ResUNet–BFA model outperforms widely used algorithms in extracting landslide boundaries and details, resulting in fewer misclassifications and omissions. Additionally, compared with conventional attention mechanisms, the BFA achieves superior performance, producing recognition results that more closely align with actual labels. Full article

The stability and diversity of the natural landscape is critical to maintaining the ecological functions of a region. However, ecosystems in the Yunnan section of the Tropic of Cancer face increasing pressure from climate change, human activities, and natural disasters, which significantly influence their vulnerability. Ecosystem vulnerability is determined by structural and functional sensitivity, coupled with insufficient adaptability to external stressors. While previous research has emphasized the effects of climate change, the multidimensional impacts of land use and human activities have often been overlooked. This study aims to comprehensively assess the ecological vulnerability of the Yunnan section of the Tropic of Cancer, addressing this research gap by utilizing geographic information system (GIS) technology and the Vulnerability Scoping Diagram (VSD) model. The study constructs a multidimensional evaluation index system based on exposure, sensitivity, and adaptive capacity, with a specific focus on the effects of land use, human activities, and natural disasters. Key indicators include road and population density, soil erosion, and geological hazards, along with innovative considerations of economic adaptive capacity to address gaps in previous assessments. The findings highlight that ecological vulnerability is predominantly concentrated in areas with low vegetation cover and severe soil erosion. Human activities, particularly road and population density, are identified as significant drivers of ecological vulnerability. Sensitivity is heavily influenced by soil erosion and geological disasters, while economic adaptability emerges as a critical factor in mitigating ecological risks. By proposing targeted policy recommendations—such as enhancing ecological protection and restoration, optimizing land use planning, and increasing public environmental awareness—this study provides actionable strategies to reduce ecological vulnerability. The findings offer crucial scientific support for improving the ecological environment in the Tropic of Cancer region and contribute to achieving sustainable development goals. Full article

This study focuses on the ecological restoration of abandoned mines to develop an evaluation model and propose policy recommendations. The research highlights that while mining contributes to economic growth, it also brings numerous abandoned mines and associated environmental issues. Western countries initiated research on abandoned mines earlier, while China continues to explore various restoration approaches. This paper, adhering to the principles of scientific validity, employs the analytic hierarchy process (AHP) and entropy method to construct an evaluation indicator system. Seven indicators were selected, and their weights were determined using both methods to derive the combined weights. The results indicate that factors such as the water source conditions significantly influence the selection of restoration methods, with different approaches yielding varied evaluation scores. Based on this foundation, sustainable development strategies are proposed for development models regarding composite tourism development, geopark development, and ecological regreening. These strategies include enhancing resource utilization efficiency, strengthening ecological protection, and promoting the transformation of ecological value, etc. The aim is to align the rehabilitation of abandoned mines with the goal of sustainable development, offering a scientific basis and policy guidance for the ecological rehabilitation of mines while fostering the sustainable development of the region. Full article

Selecting the most effective prediction model and correctly identifying the main disaster-driving factors in a specific region are the keys to addressing the challenges of geological hazards. Fuyuan County is a typical plateau mountainous town, and slope geological hazards occur frequently. Therefore, it is highly important to study the spatial distribution characteristics of hazards in this area, explore machine learning models that can be highly matched with the geological environment of the study area, and improve the accuracy and reliability of the slope geological hazard risk zoning map (SGHRZM). This paper proposes a hazard mapping research method based on multisource remote sensing data extraction and machine learning. In this study, we visualize the risk level of geological hazards in the study area according to 10 pathogenic factors. Moreover, the accuracy of the disaster point list was verified on the spot. The results show that the coupling model can maximize the respective advantages of the models used and has highest mapping accuracy, and the area under the curve (AUC) is 0.923. The random forest (RF) model was the leader in terms of which single model performed best, with an AUC of 0.909. The grid search algorithm (GSA) is an efficient parameter optimization technique that can be used as a preferred method to improve the accuracy of a model. The list of disaster points extracted from remote sensing images is highly reliable. The high-precision coupling model and the single model have good adaptability in the study area. The research results can provide not only scientific references for local government departments to carry out disaster management work but also technical support for relevant research in surrounding mountainous towns. Full article

At present, most of the research on shared electric bikes mostly focuses on the scheduling, operation and maintenance of shared electric bikes, while insufficient attention has been paid to the behavioral characteristics and influencing factors of shared cycling in plateau cities. This paper takes Kunming as a research case. According to the user’s cycling behavior, the spatiotemporal cube model and emerging hotspot analysis are used to explore the spatiotemporal characteristics of the citizens’ cycling in the plateau city represented by Kunming, and the method of geographical detectors is used to study the specific factors affecting the shared travel of citizens in Kunming and conduct interactive detection. The findings are as follows: ① the use of shared electric bikes in Kunming varies greatly on weekdays, showing a bimodal feature. In space, the overall distribution of cycling presents a “multi-center” agglomeration feature with distance decay from the center of the main urban area. ② The geographic detector factor detection model quantitatively analyzes the interactive influence between factors, providing a good supplement to the independent influence results of each factor. Through the dual factor interactive detection model, we found that the overall spatiotemporal distribution of cycling during each time period is most significantly affected by the distribution of service facilities, followed by transportation accessibility, land use, and the natural environment. The research results can assist relevant departments in governance of urban shared transportation and provide a reference basis, and they also have certain reference value in urban pattern planning. Full article

Geological hazard susceptibility assessment (GSCA) is a crucial tool widely utilized by scholars worldwide for predicting the likelihood of geological disasters. The traditional information quantity model in geological disaster susceptibility evaluation, which superimposes the information quantity of each evaluation factor without considering their weights, often negatively impacts susceptibility zoning results. This paper introduces a method employing random forest (RF) empowerment information quantity to address this issue. The method involves calculating objective weights based on a parameter-optimized random forest model, assigning these weights to each evaluation factor, and then conducting a weighted superimposition of the information. Utilizing the natural discontinuity method, the resulting comprehensive information volume map was segmented. The proposed method was applied in Kang County, Gansu Province, and its performance was compared with that of traditional methods in terms of geological disaster susceptibility zoning maps, zoning of statistical disaster point density, and receiver operating characteristic (ROC) curve accuracy. The experimental findings indicate the superior accuracy and reliability of the proposed method over the traditional approach. Full article

The Late Permian strata of the Xuanwei Formation in the eastern Yunnan region exhibit extensive diverse morphological features within siderite deposits. These variations in siderite deposits suggest potential differences in their formation processes. In this study, fieldwork and comprehensive indoor studies revealed four distinct forms of siderite deposits: stratiform-laminated, lens-like nodule, sandstone cementation, and fracture filling. The stratiform-laminated siderite, varying in color from bluish-grey to dark grey, is composed of uniformly sized microcrystalline to fine-grained siderite along with detrital matter, displaying precise layering and banding structures that suggest direct deposition from cyclic iron-rich seawater under reducing conditions. Lens-like-nodule siderite, which appears grey-yellow, is composed of mud microcrystalline siderite, medium to coarse-grained pseudo-ooids, and glauconite. It shows conformable distribution characteristics resulting from the diagenetic differentiation of iron-rich sediments under reducing conditions during the diagenetic and early diagenetic periods. Siderite as sandstone cementation exhibits a yellow-brown color and consists of dispersed colloidal siderite and cemented siderite clumps that fill intergranular pores of detrital particles. It precipitated under reducing conditions within those intergranular pores. Siderite filling fractures typically appear as vein-like or network-like structures intersecting bedding at large angles. They exhibit grain structures with significant variations in size. These siderite deposits exhibit exceptional purity and result from siderite dissolution during sedimentary periods, followed by reprecipitation within regional extensional fractures during the diagenetic phase. The primary occurrence of siderite deposits in the study area is within coal-bearing strata, as revealed by the integration of sedimentary profiles and sedimentary facies analysis. The coal-bearing strata, influenced by the Emeishan large igneous province, underwent iron enrichment during and after volcanic eruptions while developing a reducing environment, which was facilitated by abundant vegetation. Consequently, geological processes led to siderite layers, lens-like siderite nodules, and siderite cementation. The Yanshan orogeny induced extensive high-angle fracture development in epigenetic coal-bearing strata, facilitating fluid circulation and the redistribution of soluble siderite. This geological activity resulted in the formation of vein-like structures composed of siderite. Full article

Geological disasters are prevalent during urbanization in the mountainous areas of southwest China due to the complex geographic and fragile geologic conditions. This paper relies on the ArcGIS platform as the model operation carrier and takes Yiliang County of Yunnan Province as the research area. Nine evaluation factors such as slope and elevation were selected, and the risk assessment of geological disasters in Yiliang County is carried out by using the combination weighting method. The results show that: (1) the extremely high-risk areas and high-risk areas are distributed in the central, western, and northeastern parts of Yiliang County, of which 164 disaster points are distributed in the area, accounting for 72.56% of the total disaster points; (2) the elevation, human engineering activities, vegetation coverage, and distance from the river are the four main factors affecting the development of geological disasters in the area; (3) the proportions of extremely high-risk areas, high-risk areas, medium-risk areas, and low-risk areas in the total area of the county were 8.08%, 19.61%, 30.59%, and 41.72%, respectively; (4) the verification of the evaluation results by the receiver operating characteristic (ROC) curve shows that the evaluation accuracy is 80%, and the zoning results are consistent with the spatial and temporal distribution of historical disaster points. The combined weighting method can effectively evaluate the risk of geological disasters in Yiliang County, and the results can be used as a scientific reference for local government departments to carry out relevant work. Full article

An assessment of regional vulnerability to geological disasters can directly indicate the extent and intensity of risks within the study area; thus, providing precise guidance for disaster management efforts. However, in the evaluation of geological disaster susceptibility using a single deterministic coefficient model, the direct superimposition of deterministic coefficient values for each evaluation factor, without considering their objective weights, can impact the accuracy of susceptibility zoning outcomes. To address this limitation, this research proposes a novel approach: geological disaster susceptibility evaluation using a random-forest-weighted deterministic coefficient model. In this method, the objective weight of each evaluation factor is calculated based on a deterministic coefficient model and a parameter-optimized random forest model. By weighting and superimposing the deterministic coefficient values of each evaluation factor, a comprehensive deterministic coefficient map is generated. This map is further divided using the natural breakpoint method to obtain a geological disaster susceptibility zoning map. To validate the accuracy of the evaluation results, partition statistics and the ROC (Receiver Operating Characteristic) curve of the test sample points are utilized. The findings demonstrate that the model performs well in evaluating geological disaster susceptibility in Huize County. The evaluation results are considered reliable and accurate, highlighting the effectiveness of the proposed approach for assessing and zoning geological disaster susceptibility in the region. Full article

Shuangbai County, located in Yunnan Province, Southwest China, possesses a complex and diverse geological environment and experiences frequent landslide disasters. As a significant area for disaster prevention and control, it is crucial to assess the susceptibility of landslides for effective geological disaster prevention, urban planning, and development. This research focuses on eleven influencing factors, including elevation, slope, slope direction, rainfall, NDVI, and distance from faults, selected as evaluation indexes. The assessment model is constructed using the information quantity method and the information quantity logistic regression coupling method to analyze the landslide susceptibility in Shuangbai County. The entire region’s landslide susceptibility is classified into four categories: not likely to occur, low susceptibility, medium susceptibility, and high susceptibility. The accuracy and reasonableness of the models are tested and compared. The results indicate that the coupled information–logistic regression model (80.0% accuracy) outperforms the single information model (74.2% accuracy). Moreover, the density of disaster points in the high-susceptibility area of the coupled model is higher, making it more reasonable. Thus, this model can serve as a valuable tool for evaluating regional landslide susceptibility in Shuangbai County and as a basis for disaster mitigation planning by relevant authorities. Full article

The determination of the sustainable development of a region requires estimating its carrying capacity in terms of resources and environment. It is essential to investigate the carrying capacity of Kunming City to comprehend its rapid development and create a resource and environment-friendly society. This research involved the selection of a set of 35 evaluation indicators from three categories: resources, environment, and social economy. These indicators were chosen based on statistical data obtained from Kunming City between 2011 and 2020. An evaluation system was established using the entropy weight method to determine the weight of these indicators. Subsequently, the fuzzy matter–element analysis method was utilized to construct the European closeness model of Kunming’s resource and environmental carrying capacity. The correlation between the carrying capacity of resources and environment and sub-carrying capacities was analyzed using Pearson’s correlation coefficient to determine the degree of influence of different aspects on the carrying capacity of resources and environment in Kunming. The results show a consistent upward trend in the carrying capacity of resources and environment in Kunming City from 2011 to 2019. However, in 2020, due to national policy adjustments and the impact of COVID-19 on the social economy, the resource and environment carrying capacity index in Kunming City slightly decreased. Full article

In China, the majority of mountainous regions are characterized by complex topography and a delicate, sensitive geological environment. These areas, which exhibit insufficient infrastructure and widespread irrational human engineering activities, are often susceptible to geological hazards such as slope instability and soil mass movements. These geological hazards pose substantial threats to human lives and property, hindering the progress of mountainous areas. Therefore, conducting research on evaluating the vulnerability of slope rock and soil mass movement geological hazards (hereinafter referred to as geological hazards) is of utmost importance for hazard prevention, emergency management, and economic advancement in these regions. This study focuses on Xuanwei City and selects eight factors for evaluation, including elevation, gradient, slope aspect, normalized vegetation index, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads. These factors are chosen based on a comprehensive analysis of the spatial and temporal distribution of geological hazards and hazard incubation conditions. Two paired models, the deterministic coefficient model + logistic regression model (CF+LR) and the information quantity model + logistic regression model (I+LR), were employed to assess the study area quantitatively. The performance of these models was assessed by employing receiver operating characteristic (ROC) curves and calculating the corresponding area under curve (AUC) values. The results indicate that: (1) The AUC values for the coupled CF+LR and I+LR models are 0.799 and 0.772, respectively. These results indicate that both models provide an objective and reliable assessment of the vulnerability to geological hazards, specifically slope rock and soil mass movements, in the study area. (2) Based on the CF+LR model calculations, the geological hazard susceptibility of Xuanwei City can be categorized into four zones: extremely high susceptibility (6.09%), high susceptibility (31.08%), medium susceptibility (32.26%), and low susceptibility (30.57%). (3) The CF+LR model more accurately represents the evaluation results and offers a strong reference value. Full article

China is actively promoting the construction of clean energy to reach its objective of achieving carbon neutrality. However, engineering constructions in mountainous regions are susceptible to landslide disasters. Therefore, the assessment of landslide disaster susceptibility is indispensable for disaster prevention and risk management in construction projects. In this context, the present study involved conducting a field survey at 42 landslide points in the selected planned site region. According to the geological and geographical conditions of the study region, the existing regulation, and the influencing factors of landslides, the assessment in the field survey was performed based on 11 impact factors, namely, the slope, slope aspect, curvature, relative relief, NDVI, road, river, fault, lithology, the density of the landslide points, and the land-use type. Next, based on their respective influences, these impact factors were further divided into subfactors according to AHP, and the weights of each factor and subfactor were calculated. The GIS tools were employed for linear combination calculation and interval division, and accordingly, a landslide susceptibility zone map was constructed. The ROC curve was adopted to test the partition evaluation results, and the AUC value was determined to be 0.845, which indicated the high accuracy of the partition evaluation results. Full article

The Kaimuqi area in the Eastern Kunlun Orogen (EKO) contains many lherzolite, olivine websterite, gabbro and diorite intrusions, and new zircon U–Pb dating, Lu–Hf isotope and whole-rock geochemical data are presented herein to further confirm the Late Triassic mafic–ultramafic magmatism with Cu–Ni mineralization and to discuss the petrogenesis and geodynamic setting. Zircon U–Pb dating shows that the Late Triassic ages, corresponding to 220 Ma and 222 Ma, reveal the mafic–ultramafic and dioritic magmatism in Kaimuqi, respectively. Zircon from gabbro has ε_Hf(t) values of −3.4 to −0.2, with corresponding T_DM1 ages of 994–863 Ma. The mafic–ultramafic rocks generally have low SiO₂, (Na₂O+K₂O) and TiO₂ contents and high MgO contents and Mg^# values. They are relatively enriched in light rare earth elements (LREEs) and large ion lithophile elements (LILEs) and depleted in heavy REEs (HREEs) and high-field-strength elements (HFSEs), indicating that the primary magma was derived from the metasomatized lithospheric mantle. The diorites show sanukitic high-Mg andesite properties (e.g., MgO = 2.78%–3.54%, Mg^# = 50–55, Cr = 49.6–60.0 ppm, Sr = 488–512 ppm, Y = 19.6–21.8 ppm, Ba = 583–722 ppm, Sr/Y = 23.5–25.4, K/Rb = 190–202 and Eu/Eu* = 0.73–0.79), with LREEs and LILEs enrichments and HREEs and HFSEs depletions. We suggest that the primary Kaimuqi diorite magma originated from enriched lithospheric mantle that was metasomatized by subduction-derived fluids and sediments. The Kaimuqi mafic–ultramafic and dioritic intrusions, with many other mafic–ultramafic and K-rich granitic/rhyolitic rocks in the EKO, formed in a dynamic extensional setting after the Palaeo-Tethys Ocean closure. Full article

In order to explore the possibility that adding an appropriate amount of alkaline activator into fly ash cement may improve the early activity of fly ash and ensure the strength performance of concrete, this study analyzed the influence of 0–30% fly ash substitute on the early and late (3–28 days) compressive strength of concrete by using three methods, namely, the concrete laboratory test, orthogonal test, and neural network, under the condition of 0.5 water binder. We obtained the following results: (1) The strength of the concrete mixed with fly ash at the same alkali and the same age decreases with the increase of fly ash content and decreases with the decrease of age; the strength is the highest when the alkali content is 6% or 5%. (2) The higher the content of fly ash, the lower the strength of the mixture, and the greater the decrease of the early strength of the mixture, while the optimum dosage of NaOH is the same. (3) Orthogonal experimental design can be effectively used to analyze the primary and secondary degree of each factor and the best combination of them (cement, fly ash, NaOH, standard, water, etc.). (4) High correlations between the compressive strength and the component composition of concrete can be obtained using the prediction abilities of the neural networks. The above test results show that on the basis of the concrete compressive strength test, the comprehensive application of the orthogonal test and the neural network method can be used to analyze the relationship between strength and the variables and to test the influence of the variables and their interaction on concrete strength, and the results are accurate and reliable. Full article