Al-Humairi et al. (2026) Integration of IoT sensors and AI platforms for advanced weather forecasting and environmental surveillance systems

Identification

• Journal: Elsevier eBooks
• Year: 2026
• Date: 2026-01-01
• Authors: Safaa Najah Saud Al-Humairi, Avines Panneer Selvam, Prisma Megantoro
• DOI: 10.1016/b978-0-443-36408-2.00020-5

Research Groups

• Faculty of Information Sciences and Engineering, Management and Science University, Shah Alam, Malaysia
• School of Graduate Studies, Management and Science University, Shah Alam, Malaysia
• Strateq Group of Companies, Petaling Jaya, Selangor, Malaysia
• Faculty of Advanced Technology and Multidiscipline, Universitas Airlangga, Surabaya, Indonesia

Short Summary

This chapter explores the integration of Internet of Things (IoT) sensors and Artificial Intelligence (AI) platforms to enhance weather forecasting and environmental surveillance systems. It highlights how these advanced technologies improve prediction accuracy, support critical decision-making, and bolster societal resilience against climate change impacts.

Objective

• To synthesize the importance and potential of integrating IoT sensors and AI platforms for advanced weather forecasting and environmental surveillance systems, emphasizing their role in improving societal preparedness and resilience against climate change.

Study Configuration

• Spatial Scale: Conceptual; discusses applications globally without defining a specific study area.
• Temporal Scale: Conceptual; discusses ongoing advancements and future implications for forecasting without defining a specific study period.

Methodology and Data

• Models used: Discusses the general application of computer models and machine learning (ML) algorithms in weather forecasting, but no specific models are used or developed within this chapter.
• Data sources: Discusses the general use of IoT sensors and satellite images for data collection, but no specific data sources are analyzed within this chapter.

Main Results

• The integration of IoT sensors and AI platforms significantly enhances the reliability and accuracy of weather forecasting.
• This technological advancement enables more timely critical warnings and judicious decision-making across various sectors, including agriculture, transport, and emergency management.
• Improved forecasting capabilities contribute to greater societal preparedness for natural events, strategic resource planning, and the development of resilient cultures against climate change impacts.

Contributions

• This chapter provides a foundational overview and synthesis of the critical role of IoT and AI in modern weather forecasting and environmental surveillance.
• It articulates the societal benefits, from improved disaster preparedness to supporting sustainable development strategies, thereby framing the importance of future research and investment in these integrated systems.

Funding

• Not specified in the provided text.

Citation

@article{AlHumairi2026Integration,
  author = {Al-Humairi, Safaa Najah Saud and Selvam, Avines Panneer and Megantoro, Prisma},
  title = {Integration of IoT sensors and AI platforms for advanced weather forecasting and environmental surveillance systems},
  journal = {Elsevier eBooks},
  year = {2026},
  doi = {10.1016/b978-0-443-36408-2.00020-5},
  url = {https://doi.org/10.1016/b978-0-443-36408-2.00020-5}
}