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Related Concept Videos

Manipulation and Analysis01:21

Manipulation and Analysis

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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...
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Applications of GIS: Disaster Management and Emergency Response01:29

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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...
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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...
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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Methods of Obtaining Topography01:25

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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,...
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Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Updated: Dec 31, 2025

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Evaluation of Recent Advanced Soft Computing Techniques for Gully Erosion Susceptibility Mapping: A Comparative

Alireza Arabameri1, Thomas Blaschke2, Biswajeet Pradhan3,4

  • 1Department of Geomorphology, Tarbiat Modares University, Tehran 36581-17994, Iran.

Sensors (Basel, Switzerland)
|January 16, 2020
PubMed
Summary

Accurate prediction of gully erosion susceptibility is crucial for sustainable development. Machine learning models, particularly Maximum Entropy, show superior performance in mapping gully erosion risks.

Keywords:
GISIranensemblegully erosionhybrid modelsoft computing

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Area of Science:

  • Environmental Science
  • Geosciences
  • Remote Sensing

Background:

  • Gully erosion poses significant environmental and economic challenges, necessitating accurate predictive models.
  • Sustainable development requires effective strategies to mitigate losses from gully formation and expansion.

Purpose of the Study:

  • To evaluate and compare the predictive performance of seven multiple-criteria decision-making (MCDM), statistical, and machine learning (ML) models for gully erosion susceptibility mapping (GESM).
  • To identify the most effective models and their ensembles for accurate GESM in the Dasjard River watershed, Iran.

Main Methods:

  • Utilized a database of 306 gully head cuts and 15 conditioning factors within a GIS framework.
  • Trained and verified seven distinct models (MCDM, statistical, ML) and their ensembles using a 70:30 data split.
  • Assessed model performance using metrics including Area Under Prediction Rate Curve (AUPRC), Area Under Success Rate Curve (AUSRC), accuracy, and kappa.

Main Results:

  • Slope was identified as a critical factor in gully formation.
  • The Maximum Entropy (ME) ML model demonstrated the highest performance (AUSRC=0.947, AUPRC=0.948).
  • Ensemble approaches, such as combining Random Forest (RF) with statistical models (SI, FR) and integrating Generalized Linear Models (GLM) with Functional Data Analysis (FDA), enhanced predictive capabilities.

Conclusions:

  • Geographic Information Systems (GIS) combined with remote sensing (RS)-based ML models offer a robust solution for GESM.
  • The findings provide valuable tools for natural resource managers and planners to mitigate gully erosion damages, especially in developing regions.
  • Accurate GESM supports informed decision-making for land management and sustainable development.