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 Experiment Video

Updated: May 14, 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

Assessing urbanisation and ecological integrity coupling in Malaysia using interpretable machine learning.

Qinyu Shi1, Mariney Mohd Yusoff2, Nisfariza Mohd Noor1

  • 1Department of Geography, Universiti Malaya, Kuala Lumpur, 50603, Malaysia.

Scientific Reports
|May 12, 2026
PubMed
Summary

Related Concept Videos

Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...

You might also read

Related Articles

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

Sort by
Same author

Environmental eustress inhibits CCL2 to overcome doxorubicin-induced cardiotoxicity and alleviate tumor growth.

Science bulletin·2026
Same author

SLAMF7 Restrains Pro-Inflammatory Macrophage Activation to Counteract Doxorubicin-Induced Cardiotoxicity.

JACC. Basic to translational science·2025
Same author

Hyperuricemia suppresses lumican, exacerbating adverse remodeling after myocardial infarction by promoting fibroblast phenotype transition.

Journal of translational medicine·2024
Same author

Mn(II)-Catalyzed Markovnikov Hydroiodination of Terminal Aryl Alkynes.

The Journal of organic chemistry·2024
Same author

Lewis acid catalyzed Markovnikov hydrobromination and hydrochlorination of alkynes using TMSX (X = Br, Cl).

Organic & biomolecular chemistry·2024
Same author

ESCRT-III Component CHMP4C Attenuates Cardiac Hypertrophy by Targeting the Endo-Lysosomal Degradation of EGFR.

Hypertension (Dallas, Tex. : 1979)·2023

Urbanisation and environment interactions are complex and non-linear. This study introduces an AI-driven framework to analyze these dynamics, revealing critical thresholds and the importance of natural assets for sustainable development.

Area of Science:

  • Environmental Science
  • Urban Planning
  • Data Science

Background:

  • Urbanisation-environment interactions exhibit non-linear dynamics, often missed by traditional linear models.
  • Existing Coupling Coordination Degree (CCD) frameworks struggle with complex feedback loops in urban-ecological systems.

Purpose of the Study:

  • To develop an integrated framework combining CRITIC-weighted CCD with interpretable machine learning (ML) to analyze urbanisation-environment co-evolution.
  • To identify non-linear relationships, feedback loops, and sustainability tipping points in Malaysia's urban-ecological system.

Main Methods:

  • Utilized multi-source remote sensing and longitudinal statistics for 16 Malaysian states.
  • Employed CRITIC-weighted Coupling Coordination Degree (CCD) assessment.
Keywords:
Coupling coordination degree (CCD)Ecological environmentInterpretable machine learningMalaysiaSHAPUrbanisation

Related Experiment Videos

Last Updated: May 14, 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

  • Applied interpretable ML models (Random Forest, XGBoost) and SHAP values for analysis.

    • Main Results:

    • XGBoost model achieved high predictive accuracy (Test R² ≈ 0.87), confirming significant non-linear coupling effects.
    • Identified spatial decoupling in specific regions and a 'hump-shaped' threshold effect for built-up expansion.
    • Highlighted the crucial role of forest and water assets as non-linear ecological buffers.

    Conclusions:

    • Sustainable transitions are more influenced by spatial structure and natural asset management than income growth.
    • The interpretable AI-CCD framework offers a scalable toolkit for low- and middle-income countries to balance development and ecological preservation.
    • Findings underscore the need for dynamic, non-linear approaches to understand and manage urban-ecological systems.