This data set collates and collects various geological hazard points, topographic relief, landslide, elevation, land use and other influencing factors, with a resolution of 90m. The above factor layers and sample data are used to obtain the risk grade map with random forest. Data sets / atlas are mainly generated by: raw data (investigation, collection and purchase, etc.), processing data (calculation and simulation). The data source is downloaded from the open source website with an accuracy of 90m. The data is downloaded from the open source website and calculated in spider with their own random forest code. The training set is 80% and the test set is 20%. Open it with a computer that can run ArcGIS.
Based on the concept of Height Above Nearest Drainage ( HAND ) derived from the international digital elevation model, the HAND model was used to identify the flooded area, and the spatial distribution of flood hazard level in the flood area of the study area was established. Flood hazard in the study area is divided into 1-5 grades, of which 5 represent very high risk, 4 represent high risk, 3 represent medium risk, 2 represent low risk, 1 represent very low risk.
Based on China's daily ground meteorological elements data set, national geographic basic data, demographic data, and 30M resolution DEM data, statistical yearbook data, historical disaster records, and other related data, using multi-methods like PCA, random forests to calculate hazard and vulnerability indicators, based on extreme precipitation，high temperature, flood, snow hazard, collapse and landslide hazards, to build comprehensive disaster risk index, and process them with normalization. Among them, we consider all the above disaster types in Hengduan Mountain area, and flood, snow disaster, collapse and landslide disaster in sichuan-tibet railway. The natural disasters hazard map, vulnerability map and comprehensive risk map of Hengduan Mountains (Sichuan-Tibet Railway) are included in the dataset.
1) In recent years, with the global climate change, coupled with the internal dynamic disturbance and strong tectonic uplift, mountain disasters and floods in the Qinghai Tibet Plateau occur frequently, which poses a great threat to rural settlements in mountainous areas. Village disaster vulnerability and comprehensive risk prevention ability have gradually become an important topic of rural disaster prevention and reduction. 2) This data comes from a random questionnaire survey conducted from June to September 2021 in tuomai village, Lang Town, Lang County, Nyingchi City, Bangna village, Linzhi Town, Bayi District, xuewaka village, Gu township, Bomi County, Beibeng village, Beibeng Township, Motuo County, Xueni village, zhuwagen Town, Chayu County, Ranwu village, Ranwu Town, Basu County, Qamdo city and Zhuba village, Baima Town, Basu county, And the respondents are mainly adults familiar with family conditions. 3) Based on the principles of scientificity, applicability, feasibility, typicality and specificity, the questionnaire is designed for the individual villages around the Himalayas on the Qinghai Tibet Plateau. In order to ensure the reliability and validity of the design content of the questionnaire, a pre survey was conducted before the formal survey to further modify and improve the questionnaire. Before the formal start of the questionnaire survey, the investigators were explained the contents of the questionnaire and trained in survey skills. 4) A total of 231 questionnaires were completed, including 35 in tuomai village, 24 in Bangna village, 21 in xuewaka village, 38 in Beibeng village, 16 in Xueni village, 72 in Ranwu village and 25 in Zhuba village. The effective rate of the questionnaire was 98.6%.
This data uses a landslide hazard risk assessment model consisting of four modules: landslide hazard causative factors, landslide susceptibility model, exposed population and population casualty rate. The module of hazard-causing factors includes DEM, slope, rainfall, temperature, snow cover, GDP, and vegetation cover factors. The landslide hazard susceptibility model is a statistical analysis using a logistic regression model to obtain landslide susceptibility probability values. The population exposure module uses the landslide susceptibility values overlaid with population data. The population casualty rate module is based on the ratio of historical landslide casualties to the population exposed to landslides during the same period. Finally, by substituting the 2020 population data, the exposed population under different levels of landslide hazard susceptibility is calculated and multiplied with the historical period landslide hazard population casualty rate to assessIntegrated multi-hazard population risk in the peri-Himalayan and Asian water tower regions
Data content: permeability and permeability stability test data of soil materials with different dry densities Data source: the test data orginated from each piezometer, osmometer, stopwatch and measuring cylinder. All instruments are submitted for inspection every year. Collection location and method: seepage Laboratory of Chinese Academy of Water Sciences. Test the dry density according to the gradation and sample preparation thickness. Collection time: August 1, 2020 to August 20, 2020 Data quality description: through the permeability and permeability stability test of piping soil material under different density and grading, the data content includes seepage flow, water head and time. The test data come from various pressure measuring tubes, osmometers, stopwatches and measuring cylinders, which were submitted for inspection every year.
Data content: permeability and permeability stability test data of soil materials with different fine particle amounts Data source: through the seepage and seepage stability test of piping soil material under different density and grading, the data content includes seepage flow, water head and time. Collection location and method: seepage Laboratory of Chinese Academy of water sciences. Test the dry density according to the gradation and sample preparation thickness. Collection time: August 1, 2020 to August 20, 2020 Data quality description: the test data are from various pressure measuring tubes, osmometers, stopwatches and measuring cylinders, and all instruments are submitted for inspection every year.
1) The work of automatically dividing a wide and complex geospatial area or even a complete watershed into repeatable and geomorphically consistent topographic units is still in the stage of theoretical concept, and there are great challenges in practical operation. Terrain unit is a further subdivision of topography and geomorphology, which can ensure the maximum uniformity of geomorphic features in slope unit and the maximum heterogeneity between different units. It is suitable for geomorphic or hydrological modeling, landslide detection in remote sensing images, landslide sensitivity analysis and geological disaster risk assessment. 2) Slope unit is an important type of topographic unit. Slope unit is defined as the area surrounded by watershed and catchment line. In fact, the area surrounded by watershed and catchment line is often multiple slopes or even a small watershed. Theoretically, each slope unit needs to ensure the maximum internal homogeneity and the maximum heterogeneity between different units. The slope unit is an area with obviously different topographic characteristics from the adjacent area. These topographic characteristics can be based on the characteristics of catchment or drainage boundary, slope and slope direction, such as ridge line, valley line, platform boundary, valley bottom boundary and other geomorphic boundaries. According to the high-precision digital elevation model, the slope unit with appropriate scale and quality can be drawn manually, but the manual drawing method is time-consuming and error prone. The quality of the divided slope unit depends on the subjective experience of experts, which is suitable for small-scale areas and has no wide and universal application value. Aiming at the gap in practical operation in this field, we propose an innovative modeling software system to realize the optimal division of slope units. Automatic division system of slope unit based on confluence analysis and slope direction division v1 0, written in Python programming language, runs and calculates as the grass GIS interpolation module, and realizes the automatic division of slope units in a given digital elevation data and a set of predefined parameters. 4) Based on python programming language, the code is flexible and changeable, which is suitable for scientific personnel with different professional knowledge to make a wide range of customization and personalized customization. In addition, the software can provide high-quality slope unit division results, reflect the main geomorphic characteristics of the region, and provide a based evaluation unit for fine landslide disaster evaluation and prediction. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development.
1) In mountainous areas, due to the complex topographic and geological background conditions, landslides are very easy to occur triggered by external factors such as rainfall, snow melting, earthquake and human engineering activities, resulting in the loss of life and property and the destruction of the natural environment. In order to meet the safety of project site construction, the rationality of land use planning and the urgent needs of disaster mitigation, it is necessary to carry out regional landslide sensitivity evaluation. When many different evaluation results are obtained by using a variety of different methods, how to effectively combine these results to obtain the optimal prediction is a technical problem that is still not difficult to solve at present. It is still very lack in determining the optimal strategy and operation execution of the optimal method for landslide sensitivity evaluation in a certain area. 2) Using the traditional classical multivariate classification technology, through the evaluation of model results and error quantification, the optimal evaluation model is combined to quickly realize the high-quality evaluation of regional landslide sensitivity. The source code is written based on the R language software platform. The user needs to prepare a local folder separately to read and store the software operation results. The user needs to remember the folder storage path and make corresponding settings in the software source code. 3) The source code designs two different modes to display the operation results of the model. The analysis results are output in the standard format of text and graphic format and the geospatial mode that needs spatial data and is displayed in the standard geographic format. 4) it is suitable for all people interested in landslide risk assessment. The software can be used efficiently by experienced researchers in Colleges and universities, and can also be used by government personnel and public welfare organizations in the field of land and environmental planning and management to obtain landslide sensitivity classification results conveniently, quickly, correctly and reliably. It can serve regional land use planning, disaster risk assessment and management, disaster emergency response under extreme induced events (earthquake or rainfall, etc.), and has great practical guiding significance for the selection of landslide monitoring equipment and the reasonable and effective layout and operation of early warning network. It can be popularized and applied in areas with serious landslide development
Data content: statistical analysis data of characteristic laws of large-scale landslide dams based on 1230 worldwide cases Data source: a large database containing 1230 dam cases around the world based on literature retrieval. Collection method: statistical analysis of the basic characteristics of landslide dam database through Excel, origin and other data analysis software and drawing software. Data quality description: Based on the established large-scale dam database, the distribution, inducement, service life, shape, collapse and other characteristics of dams at home and abroad were statistically analyzed. The correlation analysis of some characteristics was carried out, such as the correlation analysis of geological causes and service life of landslide dam, the correlation analysis of inducing factors and geological causes of landslide dam.