1
Climate AI & Modelling
Neural networks for climate prediction, extreme weather forecasting, sea-level simulation, and atmospheric data analysis.
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, and atmospheric data analysis.
Crop monitoring, soil health analysis, drone inspection, irrigation optimisation, and food security.
Reinforcement learning for grid management, renewable forecasting, demand response, EV integration, and microgrids.
Computer vision for species identification, acoustic AI, satellite imagery, and ecosystem mapping.
IoT and AI for air and water quality, industrial emissions monitoring, and remediation planning.
Marine biodiversity, autonomous water systems, coral reef monitoring, and water resource management.
Lifecycle assessment, emissions automation, carbon credit verification, and net-zero pathways.
Efficient AI architectures, sustainable data centres, federated conservation AI, and governance.
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.