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Related Experiment Videos

Explainable machine learning for climate change attribution and hotspot identification: spatial cross-validation

Ram Chand1, Saeeddudin Sheikh2, Barkha Kanjwani3

  • 1Department of Natural Sciences, The Begum Nusrat Bhutto Women University, Sukkur, Sindh, Pakistan. ram.chand2k11@yahoo.com.

Scientific Reports
|June 23, 2026
PubMed
Summary

Machine learning models can identify climate change drivers and hotspots in Pakistan. Gradient Boosting showed robust spatial transferability, highlighting climate variables and temporal trends as key predictors for temperature anomalies.

Keywords:
Climate attributionClimate changeExplainable AIHotspot identificationMachine learningSpatial cross-validationTemperature anomaly

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Area of Science:

  • Climate Science
  • Machine Learning
  • Environmental Science

Background:

  • Climate change attribution is crucial for understanding regional impacts.
  • Machine learning offers novel approaches to analyze complex climate data.
  • Spatial transferability of models is essential for reliable climate change assessments.

Purpose of the Study:

  • To develop and evaluate a machine learning framework for climate change attribution and hotspot identification.
  • To test the geographic transferability of machine learning models using spatial cross-validation.
  • To identify key drivers of temperature anomalies in Sindh, Pakistan.

Main Methods:

  • Evaluation of eight machine learning models using 44 years of observational data.
  • Spatial transferability testing via Leave-One-District-Out Cross-Validation (LODO-CV).
  • SHAP (SHapley Additive exPlanations) for feature attribution and sensitivity analysis.

Main Results:

  • Gradient Boosting achieved the highest performance ([Formula: see text]) under LODO-CV, demonstrating robust spatial transferability.
  • Climate variables, temporal trends, and anthropogenic proxies were identified as significant predictors.
  • Seven climate change hotspots were pinpointed in Karachi and Hyderabad, facing compound risks.

Conclusions:

  • The developed machine learning framework effectively attributes temperature variability and identifies climate change hotspots.
  • Spatially explicit validation is critical for confirming the geographic transferability of climate ML models.
  • Findings provide practical insights for adaptation planning in vulnerable regions of Pakistan.