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Related Experiment Video

Updated: May 16, 2026

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests
09:42

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests

Published on: September 28, 2021

Composite Heat Wave Risk (CHWR) framework using Principal Component Analysis (PCA) and machine learning for assessing

Dolgobinda Pal1, Saon Banerjee1, Nidhi Nagabhatla2

  • 1Department of Agricultural Meteorology and Physics, Bidhan Chandra Krishi Viswavidyalaya, Mohanpur, Nadia, West Bengal, 741252, India.

Journal of Environmental Management
|May 14, 2026
PubMed
Summary

This study developed a novel heat wave risk assessment for West Bengal, India, integrating climate, agriculture, and socio-economic data. The predictive model accurately identified high-risk agricultural zones, aiding climate-resilient planning.

Keywords:
ExposureHazardHeat wave risk hotspotIPCC AR6PCA-LSTM-HOAVulnerability

Related Experiment Videos

Last Updated: May 16, 2026

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests
09:42

Isolation and Selection of Entomopathogenic Fungi from Soil Samples and Evaluation of Fungal Virulence against Insect Pests

Published on: September 28, 2021

Area of Science:

  • Climate Change Adaptation
  • Agricultural Science
  • Geospatial Risk Assessment

Background:

  • Rising temperatures and extreme heat events threaten food security, especially in climate-sensitive regions like South Asia.
  • West Bengal, India, faces significant socio-climatic stress due to diverse agroecology and climate-vulnerable farming systems.
  • District-level heat wave risk assessments integrating multiple dimensions are limited, hindering effective agricultural planning.

Purpose of the Study:

  • To develop a composite, spatially explicit heat wave risk assessment for agricultural communities in West Bengal.
  • To integrate climatic hazards, exposure, and socio-economic vulnerability using the IPCC AR6 framework.
  • To predict district-level heat wave risk classes using advanced modeling for climate resilience.

Main Methods:

  • Developed a Composite Heat Wave Risk Index (CHWRI) using Principal Component Analysis (PCA).
  • Employed a Long Short-Term Memory (LSTM) model optimized with the Hippopotamus Optimization Algorithm (HOA) for spatio-temporal risk prediction.
  • Integrated climatic, agricultural, and socio-economic data for a comprehensive risk framework.

Main Results:

  • Identified significant spatial heterogeneity in heat wave risk across West Bengal's agroecological zones.
  • The PCA-LSTM-HOA model demonstrated strong agreement between observed and predicted risk classes (72.73% accuracy).
  • Purulia district was identified as a major heat wave risk hotspot, with significant spatial clustering of risk.

Conclusions:

  • The PCA-LSTM-HOA framework provides a novel, interpretable tool for prioritizing heat wave risk in agriculture.
  • The findings support targeted early warning systems and climate-resilient agricultural planning in vulnerable regions.
  • This research contributes to building resilience against accelerating global warming impacts on food security.