Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

471
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...
471
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

286
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...
286
Survival Tree01:19

Survival Tree

388
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
388
Responses to Drought and Flooding02:41

Responses to Drought and Flooding

11.9K
Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.
11.9K
Manipulation and Analysis01:21

Manipulation and Analysis

287
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...
287
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

259
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
259

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A framework for selecting Nature-based Solutions: applications and challenges at the catchment scale.

Journal of environmental management·2025
Same author

Empirical approaches to estimate rainfall erosivity from coarse temporal resolution precipitation data in the Mediterranean region.

The Science of the total environment·2025
Same author

Rapid landslide detection from free optical satellite imagery using a robust change detection technique.

Scientific reports·2025
Same author

Understanding spatio-temporal complexity of vegetation using drones, what could we improve?

Journal of environmental management·2024
Same author

Dataset of 100-year flood susceptibility maps for the continental U.S. derived with a geomorphic method.

Data in brief·2017
Same author

Watershed influence on fluvial ecosystems: an integrated methodology for river water quality management.

Environmental monitoring and assessment·2008

Related Experiment Video

Updated: Jan 16, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K

Mapping flood susceptibility using Random Forest exploiting satellite observations and geomorphic features.

Jorge Saavedra Navarro1, Ruodan Zhuang1, Cinzia Albertini1

  • 1Department of Civil, Construction and Environmental Engineering (DICEA), University of Naples Federico II, Naples, Italy.

The Science of the Total Environment
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

This study enhances flood susceptibility mapping in Italy using the Random Forest model and 26 flood conditioning factors. The best model, incorporating Geomorphic Flood Index, improved accuracy, aiding risk management.

Keywords:
Flood conditioning factorsFlood susceptibilityGeomorphic Flood IndexRandom ForestSatellite observations

More Related Videos

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
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

10.7K

Related Experiment Videos

Last Updated: Jan 16, 2026

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.8K
Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K
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

10.7K

Area of Science:

  • Environmental Science
  • Geosciences
  • Data Science

Background:

  • Flood events pose significant destructive risks, necessitating effective risk management strategies.
  • Accurate flood susceptibility mapping is crucial for societal and environmental protection.

Purpose of the Study:

  • To assess flood susceptibility in Italy using the Random Forest (RF) model.
  • To evaluate the effectiveness of 26 flood conditioning factors (FCFs) and identify optimal predictor sets.
  • To improve the accuracy and efficiency of flood-prone area identification.

Main Methods:

  • Employed the Random Forest (RF) model for flood susceptibility assessment.
  • Utilized Average Merit of Information (AMI) to maximize FCF information and Pearson correlation/VIF to address collinearity.
  • Calibrated the model with satellite observations and historical flood records; evaluated eleven factor sets (SoFs) against official maps.

Main Results:

  • The RF model trained with SoF-1 (including mean maximum daily precipitation, Geomorphic Flood Index (GFI), distance from river, elevation, lithology, soil, NDVI, land cover) showed superior generalization.
  • Inclusion of GFI significantly enhanced prediction accuracy, particularly in previously unmapped areas.
  • Challenges remain in predicting susceptibility in flat terrains and data-scarce regions.

Conclusions:

  • Integrating satellite data, complementary datasets, and optimized predictors (like GFI) improves flood susceptibility mapping accuracy.
  • The developed approach streamlines computational processes for preliminary analysis.
  • Findings provide valuable insights for decision-makers in flood risk management.