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Related Concept Videos

Methods of Obtaining Topography01:25

Methods of Obtaining Topography

Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
Topographic Surveying and Contours01:29

Topographic Surveying and Contours

Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
Plotting of Topographic Maps01:29

Plotting of Topographic Maps

Topographic maps represent the Earth's surface features using contour lines, which connect points of equal elevation to create a two-dimensional representation of three-dimensional terrain. Creating a topographic map requires a systematic approach.Begin by plotting a scaled grid and marking intersections corresponding to the survey's elevation data points. Assign elevation values at these intersections to build the base map. Next, determine contour levels using a consistent contour interval,...

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Related Experiment Video

Updated: Jul 4, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Advanced graph-based and deep learning frameworks for landslide susceptibility mapping in mountain transportation

Yousef Bahrami1, Abbas Maghsoudi2, Amin Beiranvand Pour3

  • 1Department of Mining and Metallurgy, Amirkabir University of Technology, Tehran, Iran. y.bahrami@aut.ac.ir.

Scientific Reports
|July 2, 2026
PubMed
Summary

A novel graph-based artificial intelligence framework (GraphSAGE-CatBoost optimized by the Reptile Search Algorithm) significantly improves landslide susceptibility mapping in mountainous transportation corridors. This advanced approach outperforms existing methods, offering more accurate predictions for critical infrastructure safety.

Keywords:
Autoencoder-XGBoostGoogleNet-CNNGraphSAGE-CatBoostHHOLandslide susceptibility mappingMountain transportation corridorsRSA

Related Experiment Videos

Last Updated: Jul 4, 2026

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Area of Science:

  • Geosciences and Artificial Intelligence
  • Geotechnical Engineering
  • Machine Learning for Geospatial Analysis

Background:

  • Landslide susceptibility mapping in mountainous transportation corridors is crucial for infrastructure safety.
  • Existing methods struggle to capture complex nonlinear relationships and spatial dependencies among geo-environmental factors.
  • There is a need for advanced modeling approaches that explicitly account for topological dependencies in terrain units.

Purpose of the Study:

  • To propose and validate a hybrid graph-based artificial intelligence framework for landslide susceptibility assessment.
  • To model terrain units as nodes in a spatially structured graph, capturing topological dependencies.
  • To compare the proposed framework against state-of-the-art deep learning and hybrid machine learning models.

Main Methods:

  • Development of a hybrid GraphSAGE-CatBoost framework optimized by the Reptile Search Algorithm (RSA).
  • Compilation of a comprehensive database including 409 landslides, 409 non-landslides, and ten conditioning factors.
  • Benchmarking against GoogleNet-CNN (Harris Hawks Optimization) and Autoencoder-XGBoost using random split and spatial cross-validation.

Main Results:

  • The GraphSAGE-CatBoost-RSA model achieved superior performance (AUC-ROC=0.972, Accuracy=0.932) over benchmarks.
  • The model demonstrated strong generalization capabilities with spatial cross-validation (mean AUC-ROC=0.924).
  • High-susceptibility zones are linked to steep slopes, weak lithologies, proximity to roads/rivers, and north-facing aspects.

Conclusions:

  • The proposed graph-based AI framework offers a robust and spatially explicit approach for landslide susceptibility mapping.
  • This method effectively captures complex spatial dependencies, outperforming conventional and advanced alternatives.
  • The resulting susceptibility map aids in identifying critical areas for targeted mitigation along transportation corridors.