Chapter 1 Basic Concepts and Platform Introduction
1. Basic concepts
Types of geological hazards
The Developmental Characteristics and Distribution Rules of Geological Hazards
Hazard Characteristics of Geological Hazards
Analysis of Geological Conditions Pregnant in Geological Hazards
Inducing Factors and Forming Mechanism of Geological Disasters
2. GIS principle and ArcGIS platform introduction
Introduction to GIS
ArcGIS Basics
Spatial Data Acquisition and Organization
spatial reference
Transformation and processing of spatial data
Data Editing in ArcGIS
Visual representation of geographic data
Space Analysis:
Digital Terrain Analysis
Overlay Analysis
distance mapping
Density Mapping
Statistical Analysis
rearrange
3D analysis
Chapter II Geological Hazard Risk Assessment Model and Method
1. Evaluation model and method of geological disaster susceptibility
Evaluation unit determined
Susceptibility evaluation index system
Susceptibility Evaluation Model
Determination of weight
2. Evaluation of landslide susceptibility
Evaluation System
Terrain: elevation, slope, gully density, relief, etc.
Landform: landform unit, micro-geomorphology, overall topography, etc.
Stratum lithology: lithological characteristics, rock thickness, rock genetic type, etc.
Geological structure: faults, folds, joint fissures, etc.
Earthquake: Intensity, dynamic peak acceleration, historical seismic activity, etc.
Engineering geology: regional crustal stability, bedrock burial depth, main bearing layer lithology, bearing capacity, rock and soil engineering geological division, etc.
Extraction of common indicators
Factor extraction such as slope, slope type, elevation, terrain relief, fault zone distance, engineering geological rock group, slope structure, vegetation coverage, distance from water system, etc.
Index Factor Correlation Analysis
(1) Calculation and analysis of correlation coefficient
(2) Collinearity diagnosis
The amount of evaluation index information
Evaluation index weight determination
Analysis and drawing of landslide susceptibility assessment results
Landslide Susceptibility Composite Index
Susceptibility Classification
Graphical analysis of susceptibility evaluation results
2. Evaluation of collapse susceptibility
3. Evaluation of debris flow susceptibility
Debris flow evaluation unit extraction
Debris Flow Evaluation Index
Severity of landslides, length ratio of sediment recharge along the course, debris flow accumulation activity at the gully mouth, longitudinal slope of valleys, degree of regional structural influence, vegetation coverage of watersheds, engineering geological rock formations, reserves of loose deposits along the gully, watershed area, watershed Relative height difference, degree of channel blockage, etc.
Selection of Evaluation Indexes for Typical Debris Flows
Evaluation factor weight determination
Analysis and Mapping of Evaluation Results of Debris Flow Susceptibility
Calculation of Debris Flow Susceptibility Composite Index
Determination of Susceptibility Classification of Debris Flows
Evaluation results of debris flow susceptibility
4. Comprehensive evaluation of susceptibility to geological disasters
Comprehensive geological disaster susceptibility value = MAX [debris flow disaster susceptibility value, collapse disaster susceptibility value, landslide disaster susceptibility value]
Chapter 3 Risk Assessment of Geological Hazards
1. Risk assessment of geological disasters
2. Risk assessment of geological disasters
Selection of risk assessment factors
Under the action of certain triggering factors, the possibility of geological disasters of a certain scale and type occurring in a certain period of time in a certain area.
The complexity of regional structures, the development of active faults, and seismic activity may all induce geological disasters; the induction of heavy rainfall, the frequency and scale of disasters will also increase the probability of geological disasters.
Quantification of risk assessment factors
Quantification of risk factors for landslides
Count the number and area of disasters at all levels, and use the information volume calculation method to obtain the information volume values at all levels.
Debris flow risk assessment factor weight
Risk assessment and result analysis
3. Assessment of vulnerability to geological hazards
Vulnerability Factor Analysis of Geological Hazards
population vulnerability
house building vulnerability
agricultural vulnerability
forestry vulnerability
Livestock vulnerability
road traffic vulnerability
water vulnerability
Population Vulnerability Evaluation Factor Extraction
Population Density Data Processing
Use population density data to quantify population vulnerability, based on the population numbers obtained from the statistical yearbooks of each administrative unit, combined with housing construction area data, to quantify the spatial distribution of population, and based on GIS grid analysis, the population per unit area is obtained. density.
vulnerability assignment
Population Vulnerability Factor Extraction
Building Vulnerability Assessment
Data Processing of Building Area Density
Quantifying building vulnerability with building-building area density data, using building-building area data,
Based on the grid analysis of GIS, the area of the building area per unit area is obtained, that is, the density of the building area.
vulnerability assignment
Extraction of Building Vulnerability Factors
Vulnerability Assessment of Transportation Facilities
Acquisition of road data
Use the ArcGIS buffer analysis function to form the surface file of the road
Different types of roads for assignment
Analysis of Road Vulnerability Distribution Results
Comprehensive Vulnerability Assessment
Combined Vulnerability Overlay Weight
Extraction and Analysis of Comprehensive Vulnerability Evaluation Results
4. Extraction and analysis of geological disaster risk assessment results
Chapter Four: Application of GIS in post-disaster reconstruction
1. Analysis of emergency rescue path planning
Spatial analysis such as surface analysis, cost weighted distance, and raster data distance mapping;
Using the basic method of thematic map drawing, the emergency rescue route map for geological disasters in Maoxian County, Sichuan Province was made,
Extraction and analysis of the best path
2. Site selection analysis for disaster recovery and reconstruction
Determining the influencing factors of site selection
Determine the weight of each impact factor
Collect and process data for each influencer: terrain analysis, distance mapping analysis, reclassification
Restoration and reconstruction site selection analysis
3. Analysis of ecological environment changes after the earthquake
Use the powerful data acquisition, data processing, data storage and management, spatial query and spatial analysis, visualization and other functions of this type of software to evaluate ecological environment changes.
Chapter 5 Advanced: Python machine learning application and paper writing and reproduction
1. Analysis of the main points of thesis writing
2. Analysis of paper submission skills
3. Case analysis of the paper
Case: Research on Disaster Susceptibility Evaluation Using Machine Learning
Some results are reproduced:
Model introduction:
√ Logistic regression model
√ Random Forest Model
√ Support vector machine model
Implementation plan:
1. Operating environment deployment
√Python compilation environment configuration
√ sklearn library
2. Linear Probability Model - Logistic Regression
√ Introduction
√ Selection of connection function: Sigmoid function
√Disaster-causing factor data set: data introduction; correlation analysis; logistic regression model prediction; sample precision analysis; classification confusion matrix
√ Precautions
Three, SVM support vector machine
√ Linear Classifier
√SVM-kernel method: Introduction to kernel method; sklearn's SVM kernel method
√ Parameter optimization and adjustment
√ SVM data set: support vector machine model prediction; sample accuracy analysis; classification confusion matrix
4. Python Implementation of Random Forest
√Dataset
√ Random selection of data
√Random selection of features to be selected
√ Explanation of related concepts
√Parameter optimization and adjustment: random forest decision tree depth tuning; CV cross-validation definition; confusion matrix; sample precision analysis
√ Implementation of Random Forest based on pandas and scikit-learn: data introduction; random forest model prediction; sample accuracy analysis; classification confusion matrix
5. Comparative analysis of methods
√Accuracy analysis
√ Comparative analysis of results
