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AI for Climate Modelling & Extreme Weather Prediction
Neural networks for climate prediction, extreme weather forecasting, sea-level simulation, atmospheric modeling, and climate hazard risk assessment.
ISIAI-SGS 2026 invites original research papers, review articles, case studies, short papers, and application-focused contributions at the intersection of AI and environmental sustainability.
Authors may submit work that is theoretical, empirical, infrastructural, policy-facing, or strongly application-oriented, as long as the contribution is clearly positioned within environmental sustainability.
Neural networks for climate prediction, extreme weather forecasting, sea-level simulation, atmospheric modeling, and climate hazard risk assessment.
Reinforcement learning for grid management, renewable energy forecasting, demand response, EV charging infrastructure integration, and resilient microgrids.
Satellite and drone remote sensing, crop yield forecasting, soil microbiome intelligence, automated irrigation optimization, and climate-resilient food supply systems.
Computer vision for wildlife monitoring, bioacoustic AI for species tracking, forest canopy density mapping, and automated ecological health assessment.
Automated greenhouse gas accounting, scope 1-3 lifecycle assessment, ESG data intelligence, green auditing algorithms, and supply chain decarbonization.
Marine biodiversity sensing, polar ice sheet monitoring, autonomous ocean robotics, coral reef degradation detection, and sea-level prediction models.
Multi-hazard early warning systems, real-time flood and wildfire monitoring, autonomous disaster relief robotics, and resilient infrastructure defense.
Generative AI for sustainable materials discovery, biodegradable polymers, battery recycling optimization, industrial symbiosis, and circular economy design.
All deadlines are listed in IST. The moment final dates are announced, the site and subscriber list will be updated together.
Submissions are reviewed for relevance, originality, clarity, and contribution to environmental sustainability.
Accepted papers are included in ISBN-registered proceedings subject to editorial and formatting compliance.
ScholarVault verification supports transparent conference positioning and anti-predatory signalling for authors.
Papers can combine AI methods with policy, ecology, engineering, remote sensing, or sustainability operations.
Well-scoped real-world deployments, pilots, benchmarks, and operational case studies are welcome.
Early-stage but high-signal work can be considered when the contribution is clearly articulated.