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Effective risk assessment of potential landslide disasters is crucial for informed decision-making in engineering projects. In practical engineering, the risk assessment of individual landslide disasters still relies mainly on qualitative evaluation methods. The qualitative risk level is obtained through the risk matrix based on danger and loss, which poses problems of multiple and discontinuous risk standards. This makes it difficult to compare and classify risks in actual engineering, and may also lead to errors in risk assessment results, misleading risk management. To address these issues, a probability model is employed to quantitatively describe the stability of landslide disasters, considering the size of losses to the element at risk. A quantified risk index for individual landslide disasters is obtained, introducing the concept of landslide disaster risk surface and risk contour. Corresponding evaluation and calculation software are developed to form a quantified risk assessment method for landslide disasters, eliminating the multiplicity and discontinuity of risk standards brought about by traditional landslide risk matrices. The accuracy of the evaluation is improved. By conducting quantitative risk assessment on six individual landslides in Fengjie County, Chongqing, the correctness and reliability of the quantitative risk assessment method were verified, providing a new approach for landslide disaster risk assessment.

Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms--Random Forest, Multilayer Perceptron, and Convolutional Neural Network--are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930-0.957 and an AUC value of 0.955. The CNNbased warning model demonstrated an accuracy of 0.945-0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930-0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on 5 August 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multi-classification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this study Frontiers in Earth Science 01 frontiersin.org Liu et al. 10.3389/feart.2024.1419421 contribute to valuable attempts in landslide disaster warning model research, with anticipated further improvements through the gradual accumulation of samples and practical application verification. [ABSTRACT FROM AUTHOR]

Le'an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town's terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le'an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le'an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value. [ABSTRACT FROM AUTHOR]

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Copyright of Tunnel Construction / Suidao Jianshe (Zhong-Yingwen Ban) is the property of Tunnel Construction Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Landslide disasters occur frequently in Fujian Province, and early warning of landslide disasters on a regional scale is an important means of effective disaster prevention and mitigation. Due to the complex mechanism of landslide disasters, the traditional regional landslide early warning methods have such problems as insufficient accuracy. Deep learning mainly refers to the technology of feature extraction, abstraction, representation and learning by constructing the neural network model, which is a kind of machine learning. As a classical deep learning algorithm, convolutional neural network has more powerful classification and representation ability than traditional machine learning. Taking Fujian Province as the research area, this paper introduces the convolution neural network into the field of landslide disaster early warning and constructs a regional landslide early warning model of Fujian Province. The process is as follows: (1) The SMOTE optimization algorithm is used to optimize the sample database of landslide disasters in Fujian Province from 2010 to 2018, enlarging the number of positive samples and expanding the proportion of positive and negative samples from 1∶3.4 to 1∶2, and the total number of samples reaches 18040. (2) Construct a convolution neural network model structure, which includes an input layer, two convolution layers, two maximum pooling layers, a full connection layer and an output layer. (3) Use the convolution neural network to train the optimized samples (80% of the samples from 2010 to 2018 as the training set), and use the Bayesian optimization algorithm to optimize the model parameters to obtain the regional landslide early warning model of Fujian Province. (4) The model is tested with 20% of the samples from 2010 to 2018 as the test set, and the confusion matrix and ROC curve are used to test the model. The results show that the accuracy of the model ranges from 0.96 to 0.97, the AUC value is 0.977, indicating that the model accuracy and generalization ability are good. (5) The actual situation of the landslide disaster in the flood season of 2019 is taken as a positive sample, negative samples are collected through the method of time-space sampling, and the 2019 regional landslide sample verification set (603 samples) is constructed. The model is further verified by using the confusion matrix and ROC curve. The results show that the accuracy of the model ranges from 0.75 to 0.85, and the AUC value is 0.852. Although only the actual landslide samples in the flood season of 2019 is used for verification, good results is also achieved. In this paper, the convolution neural network algorithm is applied to the regional landslide early warning, which provides a new way to establish the regional landslide early warning model. The preliminary verification shows that the model is effective and will be further applied and verified in Fujian Province in the future.

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Le’an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town’s terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le’an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le’an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value.

The primary objective of this study was to enhance the community's understanding and awareness of landslide risks and mitigation strategies by employing Geographic Information Systems (GIS) to identify landslide-prone areas in the Magetan District, East Java. The initiative unfolded in several stages, which included: (1) emphasizing the importance of mapping areas susceptible to landslides; (2) consolidating data through satellite image analysis and GIS; (3) developing GIS-based maps that depict landslide-prone areas; and (4) circulating these maps among the local community and governing bodies. In conclusion, this study demonstrates that a participatory approach, combined with advanced technologies such as GIS, can play a pivotal role in disaster mitigation. Early identification of landslide risks and appropriate mitigation strategies using GIS and community education can significantly enhance community preparedness and resilience to landslides. The potential applicability of this method to other regions with similar topographical and geological conditions underscores its relevance in improving community readiness and resilience against landslide disasters.

Landscapes disturbed by shallow landslides present several residual risks. For example, residual soils on sliding surfaces may lead to the re-occurrence of shallow landslides, and landslide deposits on valley floors may introduce multiple hazards associated with river blocking or landslide damming. Despite such risks, few studies have investigated the residual risks following shallow landslides, and thus, no methodology has been established for assessing such risks. In this study, we combined portable cone penetration test (PCPT) and electrical resistivity tomography (ERT) to investigate the post-event risks associated with a landslide scar resulting from the 2018 Hokkaido Eastern Iburi earthquake (HEIE) in Japan. The multi-point PCPT identified the interface between a weak volcanic soil layer and underlying weathered sedimentary rocks. This interface exhibited an abrupt spatial change in resistivity, ranging from 20 to 100 Ωm. The thickness of the residual soil layer was 0.3–3.2 m and that of the landslide deposit exceeded 2.0 m. An infinite-slope stability analysis was performed to predict the re-occurrence of shallow landslides due to a lower intensity earthquake than that of HEIE in this region. Considering the area affected by the HEIE and the physical properties of soil layers, our findings imply a widespread risk of re-occurrence of shallow landslides over an area of 20 km × 20 km. Although the areal coverage of the combined ERT/PCPT method needs to be extended, it is effective for assessing the residual risks associated with shallow landslides. [ABSTRACT FROM AUTHOR]

The landslides induced by typhoons and rainstorms in Southern China are often shallow, fluidized and clustered. A large number of investigations have shown that vegetation infiltration has a significant impact on the formation of granite residual soil landslides. However, most of the current studies have focused on the effect of vegetation roots on soil infiltration. The influence mechanism of vegetation infiltration enhancement effect on shallow landslides has not been revealed. Therefore, the mass landslide disaster on September 9, 2019 in Longchuan County in Guangdong is taken as an example, and a large number of landslide site investigations are made to identify the geological environment conditions and vegetation development in the landslide area and analyze the infiltration effect of vegetation on shallow landslides. The “double-ring method” is used to measure infiltration rate of different vegetation types, and to analyze the infiltration process and infiltration law of different vegetation types. A typical landslide profile is selected to establish a geological model, and the Geo-Studio software is used to simulate the seepage law of shallow landslide and the stress-strain characteristics of the soil under heavy rainfall conditions (250 mm/d). Finally, combined with the simulation results and field investigations, the infiltration effects of vegetation and their response mechanisms to shallow landslides under heavy rainfall are analyzed. The results show that vegetation can effectively enhance the permeability of soil, and the order of permeability is coniferous forest land, shrub forest land and bare land. Under the influence of vegetation infiltration effect, rainwater infiltration to the bottom of the root-soil composite layer will cause water stagnation, the shallow soil tends to be saturated rapidly, the pore water pressure and seepage force in the soil increase instantaneously, the soil is saturated with water, the residual soil softens, and the weight of the slope increases, which eventually leads to the overall instability of the slope. The research results explain the formation mechanism of granite residual soil landslide in high vegetation covered area under heavy rainfall in Southern China, and provide scientific basis for early warning and prediction of such disasters, which are of great significance.

Although it is known that the generation of odor is one of the precursory phenomena of landslide disasters, the mechanism by which offensive odors are detected before landslides is not fully clarified. It has been reported that high odor intensity was observed from the pore air in soil at 0-3m depth on natural slopes with a history of landslide disaster history. In this study, laboratory experiments were conducted to examine the possibility of the generation of odors during rainwater infiltration process. We made a model ground mixed with odorants and conducted the following two experiments, 1) measurement of odor intensity on the surface of 10 cm height soil layer when the groundwater level rose by injecting water from the bottom, 2) measurement of odor intensity on the surface during rainwater infiltration process using a 1 m height soil layer and a laboratory rainfall device. From these tests, it was found that there is no or very little increase in odor intensity on the surface in the process that the pore air in soil was released from the surface accompanied by the rainwater infiltration. However, in both tests, the measured odor intensity increased rapidly when the ground water level approached the surface. It can be said that the generation of odor as a precursor phenomenon can be caused when the level of groundwater containing odorants reaches near the ground surface. [ABSTRACT FROM AUTHOR]

An adaptive structural learning method of restricted Boltzmann machine (RBM) and deep belief network (DBN) has been developed as one of prominent deep learning models. The neuron generation–annihilation algorithm in RBM and layer generation algorithm in DBN make an optimal network structure for given input during the learning. In this article, our model is applied to an automatic recognition method of road network system, called RoadTracer. RoadTracer can generate a road map on the ground surface from aerial photograph data. A novel method of RoadTracer using the teacher–student-based ensemble learning model of adaptive DBN is proposed, since the road maps contain many complicated features so that a model with high representation power to detect should be required. The experimental results showed the detection accuracy of the proposed model was improved from 40.0% to 89.0% on average in the seven major cities among the test dataset. In addition, we challenged to apply our method to the detection of available roads when landslide by natural disaster is occurred, in order to rapidly obtain a way of transportation. For fast inference, a small size of the trained model was implemented on a small embedded edge device as lightweight deep learning. We reported the detection results for the satellite image before and after the rainfall disaster in Japan.

The analysis of potential landslide deformation on the reservoir bank is an important link to ensure the safe operation of national water conservancy and hydropower facilities. In response to the problem of inaccurate monitoring of potential landslide deformation on the reservoir bank using single-track SAR data only, this paper uses SBAS-InSAR technology to construct the radar line-of-sight directional deformation of the study area from July 2019 to July 2021 by combining elevated-track Sentinel-1 SAR data time series, and unmanned aircraft field surveys were combined to analyse the deformation characteristics of typical potential landslides on the reservoir bank of Baihetan hydropower station. The results show that: (1) under the influence of water storage factors, the average growth rate of potential landslide deformation on the reservoir bank of Baihetan hydropower station reaches more than 10 mm/a. Reservoir bank stability is undermined; (2) water storage changes are the key causal factor in the development of reservoir bank potential landslides at present. The potential landslides on the reservoir bank of Baihetan Hydropower Station are at risk of instability with the joint effect factors such as water storage and rainfall; (3) the method which combined lift-track SAR data can effectively overcome the problems such as geometric distortion caused by a single track, and make the monitoring of potential landslide deformation on the reservoir bank of hydropower station more accurate and comprehensive. The research result can help to understand the impact of reservoir storage on the deformation trend of reservoir bank potential landslides, and can provide scientific support for regional-scale disaster prevention and emergency management.

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper,a geological disaster early warning model is constructed based on GIS,and a susceptibility evaluation system is constructed by using GIS spatial information system to obtain seven influencing factors,including slope,topographic relief,slope shape,rock and soil mass type,geological structure,land use type and vegetation coverage rate.The information quantity value of each influencing factor is determined based on the information model method.At the same time,the hidden points of geological disasters in research area are superimposed and the prone zones are divided to provide reference for the prediction and prevention of geological disasters in this area.

Landslide disasters seriously threaten the safety of people􀆳s lives and property. Monitoring landslides is of great significance for disaster prevention and mitigation. The down-orbit data of Sentinel-1 in 58 scenes covering the Muchang ancient landslide were obtained, and the deformation monitoring of the Muchang ancient landslide was carried out based on the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology. Combined with the geological characteristics of the Muchang ancient landslide, the time series deformation trend of the landslide was analyzed. The results show that the deformation rate of the ancient landslide in Muchang is negative as a whole, and the deformation rate is larger in the middle and lower parts. The deformation trend is basically the same, showing a downward trend in general, indicating that the Muchang ancient landslide body is in a continuous development stage, especially the secondary landslides H1, H2 and H3 are particularly developed. Combined with the on-site deformation signs, the accuracy of SBAS deformation monitoring was verified. The research results promote the application of InSAR technology in landslides, in order to provide a scientific basis for landslide disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The China-Pakistan Economic Corridor is located in the alpine and glacier valleys, which is the most sensitive and complex area of global climate change. In recent years, with global warming, the glaciers in the corridor have melted, rainfall has increased, and landslides have occurred frequently, which seriously threatens the safety of local residents and the smooth implementation of major projects. The northern mountainous area of the China-Pakistan Economic Corridor was used as the study area. On the basis of analyzing the regional natural geological environment and the distribution characteristics of landslide disasters, 10 evaluation factors including relative height difference, slope, aspect, curvature, rock group, fault zone density, river network density, distance from glaciers, seismic acceleration and land use were selected. The static risk assessment of landslide disasters in the study area was carried out through the coupled information-logistic regression model. On the basis of verifying the reliability of the model, the effects of dynamic rainfall and air temperature on the risk of landslides were introduced, and the dynamic risk was evaluated and predicted. It is concluded that the high-risk area are mainly concentrated in the area from Khunjerab to Havilian, especially on sides of the river valley with steep slopes and large undulating terrain. And under the influence of climate change, the proportion of high-risk areas in the study area fluctuated and increased. The relevant results have important guiding significance for the development planning and disaster prevention and mitigation of the China-Pakistan Economic Corridor. [ABSTRACT FROM AUTHOR]

The focus of this study is to analyze the resilience of rubber farmer households due to landslides in the lake village, Nalo Tantan subdistrict, Merangin Regency, Jambi Province. This study used a qualitative approach with descriptive type, and the informants of this study were carried out by purposive sampling. The informants of this research are rubber farmer households experiencing pressure from landslides. Data collection methods started from in-depth interviews, observation and document data collection. The unit of analysis in this study is at the household level, namely the rubber farmer household. Data analysis was carried out in several stages, namely data collection, data reduction, data study and drawing conclusions, which were referred to in the Miles and Hubermen model. The results of this study indicate that the household life of rubber farmers is included in the lower middle economy. Rubber farmer households continue to survive driven by the lack of capital to find adequate housing to keep living. The problem found by researchers is that there are no other jobs or other skills owned by rubber farmers. The income of rubber farmers can be seen from the amount or nominal amount of money generated from the sale of rubber. The high and low prices of rubber greatly affect production which ultimately affects the income received by farmers. [ABSTRACT FROM AUTHOR]

In recent years, landslides occurred frequently in mountain and gorge areas, which brought serious threats to people's life and property safety. Most scholars use SAR single-track data for early identification of landslides in alpine and canyon areas, but some landslides cannot be identified due to geometric distortion of SAR imaging, and the identification results are not comprehensive. In order to carry out comprehensive and accurate early identification of landslide hazards in alpine valley area, this paper adopts bas-INSAR technology, takes the deep cut alpine valley area along the Xiaojiang River in Dongchuan as the research area, and adopts the combination of SAR data of lifting and lowering orbit to identify landslide hazards, and introduces high-resolution optical images as auxiliary identification. Finally, 18 landslide disaster bodies were identified, among which 5 were high-risk potential landslides, and three types of typical potential landslides were analyzed. The analysis results show that the use of elevator rail SAR data combined with complementary way, can effectively avoid the SAR single orbital data geometric distortion problem in mountain valley area, at the same time, this method can more accurately comprehensively to early identification of alpine valley area of landslide hazard, the cause of disaster prevention and mitigation and government decision-making provides a effective means.

The Eastern Himalayan Syntaxis (EHS), featured by an exceptionally intense compression orogeny and an extremely rapid surface erosion rate, stands out as a prime area for investigating surface processes and landscape evolution. Understanding the formation of the EHS holds significant importance in advancing our comprehension of the evolutionary dynamics within the Tibetan Plateau and the complex interplay between endogenic and exogenic geological processes. Currently, two models, namely the tectonic aneurysm and the syntaxis expansion model, have been proposed to explain the formation and evolution of this syntaxis. In this study, we employ a multi-disciplinary approach, integrating geomorphic indices such as slope, relief, hypsometric integral (HI), normalized channel steepness (Ksn), river slope-basin area integration (χ), Gilbert metric, combined with erosion rates on long-medium-short term timescales obtained from thermochronology, cosmogenic nuclides 10Be and landslide disaster probability data. Our findings suggest a relatively high tectonic activity in the confluence of the Yigong and Parlung Rivers in the northern end of the syntaxis. Moreover, our data reveals that spatial variability of tectonic activity is the primary driver of regional geomorphologic features and the spatial-temporal characteristics of erosion observed in the EHS, with lithology and climatic precipitation playing a lesser role. Furthermore, the northward migration of the EHS appears to elucidate the shift of tectonic activity and rapid erosion from the core of the syntaxis near the Namche-Barwa towards the confluence of the Yigong and Parlung Rivers. This northward migration of the EHS underscores the need for increased attention to the high geological hazard risk in the northern part of the EHS. • Geomorphic indices show higher tectonic activity at the confluence of Yigong and Parlung. • Geomorphic analyses were linked to erosion patterns at varying spatial-temporal scales. • Northward migration of the Eastern Himalayan Syntaxis derives rapid uplift and erosion. [ABSTRACT FROM AUTHOR]

Fill material flows created by land development earthworks are anthropogenic agents that generate massive energy use from their heavy loads. However, formal quantification of these flows has been neglected. We use Osaka Prefecture in Japan as a case study to quantify fill flows and associated CO2 emissions. We collected data on fill flows, including fill generation and acceptance. We mapped these publicly uncounted fill flows and calculated the CO2 emissions from the associated energy use. We also simulated a scenario in which optimized shortest-distance matching is achieved between fill generators and acceptors. We estimated the current fill flows based on distance and weight and broke down the total by type of site and activity. We compared our estimates of current fill flows with estimates from our matching simulation and found the simulation could achieve an 8448 km reduction in flow length and a 5724 t-CO2 reduction in emissions associated with transportation. We discussed the implications of flexible matching, especially in different construction sectors, and the importance of continuous, spatially geo-referenced monitoring of these fill flows toward further environmental impact mitigation. The approach presented here could apply to assessing environmental loads arising from landform changes in other cities and lead to development of a new regional- and global-scale fill material science in the Anthropocene. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Island Studies / Tosho Kenkyu is the property of Japan Society of Island Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Geological Hazard & Control is the property of China Institute of Geological Environmental Monitoring (CIGEM) Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing methods have shortcomings, such as a long and costly deposit volume acquisition cycle, lack of knowledge and guidance, complex operations for scene modeling expression, and low scene rendering efficiency. Therefore, this paper focuses on the study of a three-dimensional visualization and optimization method for landslide disaster scenes guided by knowledge, and discusses key technologies such as the rapid acquisition of landslide deposit volume information based on three-dimensional reconstruction, the knowledge-guided dynamic modeling visualization of disaster scenes, and scene optimization considering visual significance. The prototype systems are developed and used in a case experiment and analysis. The experimental results show that the proposed method can quickly obtain the deposit volume, and the results are equivalent to ContextCapture, Metashape, and Pix4Dmapper software. The method realizes the dynamic visualization of the whole disaster process, provides rich information, achieves high readability, and improves the efficiency of scene rendering, with a stable average rendering frame rate of more than 80 frames/second. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Engineering Geology / Gongcheng Dizhi Xuebao is the property of Journal of Engineering Geology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Yajiang County, located in the middle reaches of Yalong River in western Sichuan Province, is dominated by the landform of middle mountain-alpine valley, with frequent geological disasters. The risk assessment of landslide disasters in the upper reaches of Yajiang County is carried out on the basis of overlay analysis of logistic regression (LR) and certainty factor (CF) to ensure people's life and property security. The results show that the landslides are prone to occur in the hard rock interbedded with soft rock zone, with an altitude of 2 500-3 000 m, relative height above river of 600-900 m, gradient of 30-45° and distance of 0-200 m from river system. The high and very high risk areas in the upper reaches of Yajiang County account for 46.75% of the total area, and 65.91% of the total landslide occurrences, indicating the dense distribution of landslides and high damage degree, which is consistent with the field survey results. The Xiala Town mostly belongs to very high and high risk area, Waduo Township mostly medium risk area, and Murong Township and Pubarong Township mostly low and very low risk area. The assessment results have high accuracy through verification of inspection points and ROC curve, and thus can serve as a reasonable plan basis for disaster prevention and mitigation, river development and utilization in the study area. [ABSTRACT FROM AUTHOR]

China is a country with many mountains. Mountains, plateaus, and hills account for 69% of the total area. With the increase of rainfall in July and August every year, geological disasters such as landslides and debris flows occur, and road burials and house collapses occur, resulting in economic losses. Especially in the Midwest, landslides and other geological disasters seriously threaten the lives and property of people living near the mountains. According to statistics, landslide is a serious threat to people's lives and property safety. Therefore, the effective early warning of geological disasters such as ground slide and debris flow is the top priority. Economic development has improved people's living standards, but the number of obese people, especially the increase in the proportion of obese teenagers, has also brought some problems. According to relevant media reports, the incidence of obesity in youth in Beijing, Shanghai, and other first tier cities has reached 21%, and the average incidence in China is close to 10%. Thirty obese college students were given moderate aerobic exercise for two semesters. The changes of body shape index, physiological index, and blood biochemical index before and after exercise were compared. The results showed that after the second semester of weight loss exercise, body mass index, body fat rate, abdominal subcutaneous fat, upper arm subcutaneous fat, scapular subcutaneous fat, fasting blood glucose, blood lipid, vital capacity, and other obese students; body mass index were improved. Aerobic exercise can effectively reduce the degree of obesity of obese students and improve body shape, physiological indexes, and blood biochemical indexes. [ABSTRACT FROM AUTHOR]

The existing slope safety monitoring studies mainly focus on long-term monitoring of slowly evolving horizontal deformations. However, during the practical applications, sudden deformations may also lead to landslides due to the natural and man-made trigger factors. This article first proposes a reusable and distributed fiber Bragg grating (FBG) inclinometer for simultaneous measurement of horizontal deformation and vibration induced by sudden deformation, providing more extensive monitoring for slopes. The proposed inclinometer utilizes ten FBG tilt sensors in series as measuring elements, which are characterized by easy installation and removal, thus making them reusable and eliminating excessive waste. A signal decomposition method is presented for splitting the coupled signal of the tilt sensor into two parts: the inclination angle signal and the vibration signal. The inclination angle signals can be converted to distributed horizontal deformation through a cumulative algorithm. And the vibration intensity can be inferred from the vibration signal. The measuring principle, structure design, and calibration experiment of the inclinometer were present in detail. A distributed horizontal deformation generating device with high precision was specially developed to simulate the internal deformation of slope. Verification experiments further validated the feasibility of the proposed inclinometer for simultaneous measurement of horizontal deformation and vibration induced by sudden deformation. [ABSTRACT FROM AUTHOR]

Landslide disasters threaten the safety of human life and properties. It is important to accurately assess the risk of regional landslides. At present, common machine learning methods cannot extract deeper data features and obtain higher precision landslide hazard assessment maps. Therefore, this paper proposes a deep one-dimensional convolutional neural network method for landslide risk assessment, in order to extract deeper features and obtain higher precision evaluation results by based on its powerful learning ability. Taking Lushan, Sichuan, China as an example, 346 historical landslide pionts of Lushan in 2016 were selected for model training and verification. In order to verify the validity and precision of the model, the support vector machine, the information value method and the proposed convolutional neural network model were compared in the experiments. The overall accuracies of the convolutional neural network and the support vector machine are 0. 9015 and 0. 842. The AUC values of the one-dimensional convolutional neural network, support vector machine, and information value model are 0. 888, 0. 876, and 0. 818, respectively, The results show that the proposed convolutional neural network model has a higher accuracy and is superior to the support vector machine and the information value method in data fitting ability and evaluation effect, and can be effectively applied to the regional landslide risk evaluation. [ABSTRACT FROM AUTHOR]

This paper based on the concept of fractal features and the box dimension method to explore the fractal characteristics of influencing factors in different disasters. By analyzing the internal relationship between the spatial distribution of landslide hazards and various influencing factors, the fractal dimension value of each evaluation element was obtained, which was used to sort the sensitivity of landslide disasters in different evaluation factors. In order to verify the feasibility of the fractal theory evaluation model, this paper compared the evaluation values of different influencing factors, which was obtained from the logistic model, the evidence weight function model, the evidence credibility function model, and the information amount model. The comparison of the evaluation values shows that its feasible to use fractal theory to explore the development and evaluation of landslide hazards. The sensitivity relationship between landslide hazard and various influencing factors can be reflected quantitatively in the fractal dimension. It provides an important reference value when determining the weight. [ABSTRACT FROM AUTHOR]

The purpose of a learning media is to become a learning device to facilitate the teaching and learning process in class. The learning process would be more efficient, and it would help students concentrate better. This research aims to develop an android-based learning media application so teachers and students can use it in the teaching and learning process. This research uses the RD (Research and Development) method. This research uses ADDIE (Analysis, Design, Development, Implementation, and Evaluating) development model which results in a product, which is a valid learning media that can be implemented according to validator assessment that was done in the product trial test in Universitas Negeri Yogyakarta. It can be concluded that this media belongs in the good category based on assessment from media and material specialists validator, 85,7% average percentage is obtained and belongs to the valid category, so android-based physics media integrated with landslide disaster education is feasible to be used in physics learning. This media is made with an interesting and interactive interface so students can have a better understanding of physics learning integrated with landslide disaster education as well as disaster preparedness.

The rapid acquisition of deposit volume information and dynamic modeling, as well as the visualization of disaster scenes, have great significance for the sharing of landslide information and the management of emergency rescue. However, existing methods have shortcomings, such as a long and costly deposit volume acquisition cycle, lack of knowledge and guidance, complex operations for scene modeling expression, and low scene rendering efficiency. Therefore, this paper focuses on the study of a three-dimensional visualization and optimization method for landslide disaster scenes guided by knowledge, and discusses key technologies such as the rapid acquisition of landslide deposit volume information based on three-dimensional reconstruction, the knowledge-guided dynamic modeling visualization of disaster scenes, and scene optimization considering visual significance. The prototype systems are developed and used in a case experiment and analysis. The experimental results show that the proposed method can quickly obtain the deposit volume, and the results are equivalent to ContextCapture, Metashape, and Pix4Dmapper software. The method realizes the dynamic visualization of the whole disaster process, provides rich information, achieves high readability, and improves the efficiency of scene rendering, with a stable average rendering frame rate of more than 80 frames/second.

This study aims to explore building education capacity to improve disaster management in public awareness. We can improve public awareness of disaster risks through public education on landslide disasters. The current education in disaster courses is mainly aimed at students and ambulance personnel. Those education methods focus on textbook teaching and professional training, and the contents are too professional. There is no effective communication channel between educators and the public. Thus, this paper proposes a visual representation method of landslide disasters for public education. We prepared a concept map to organize and represent bits of knowledge of landslide, instantiating nodes of concept map as animation, then used animation to publicize. The landslide on June 24, 2017 in Diexi, Maoxian was selected to conduct a case test analysis. One short animation created by the method and one brief disaster report summarized by that animation is produced for a questionnaire. We collected and analyzed the results of the questionnaire. The results show that people tend to learn about landslides by watching animation, and they answer more questions correctly in the questionnaire after watching animation than reading the report. So the proposed method in this paper can effectively construct the visual content of landslide disaster knowledge and quickly help the public understand the landslide disaster knowledge. [ABSTRACT FROM AUTHOR]

Objective: As a frequent geological disaster, landslide is very sudden, destructive and easy to cause secondary disasters, which brings great threat to human life and property. It is very important to construct the virtual scene of landslide disaster for understanding the information of landslide disaster to assist emergency decision making. However, the existing landslide scene modeling has some shortcomings, such as complex operation, information readability and so on. Therefore, we propose a knowledge-guided dy-namic expression method of landslide disaster scene. Methods: We adopt the knowledge-guided method to construct the landslide disaster scene. Firstly, we explore the content, model and expression features of landslide disaster scene as well as their interrelations. Then, we establish hierarchical semantic constraint rules of landslide disaster scene modeling, including the information completeness constraint rules, the spatial semantic constraint rules, the information readability constraint rules and the logical relationship constraint rules. Finally, the above rules are used to construct the knowledge template of landslide disaster modeling. Under the guidance of the knowledge template, the dynamic expression of the whole process of landslide disaster is realized. The prototype system is implemented, and a case experiment analysis is performed using the 10·11 Jinshajiang landslide and 6·24 Maoxian landslide as examples. Results Under the guidance of knowledge, the important information of landslide disaster, such as geological structure, historical disaster, meteorological conditions, disaster process and disaster situation, is selected, and the elements such as text, abstract symbols, animation and visual variables were integrated to realize the dynamic expression of the whole process of landslide disaster from cause, process to impact. Conclusions: The experimental Results: show that the dynamic expression method of knowledge-guided landslide disaster scene can simplify the complex modeling operation, quickly construct landslide disaster scene, have rich expression content, good visualization effect, complete disaster process, clear disaster information, and help users to understand landslide disaster more effectively. [ABSTRACT FROM AUTHOR]

Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.