Special Issue: Big Data Analytics for Agricultural Disaster Management

Submission Deadline: February 1, 2021

The significance of big data analytics in the agriculture disaster management field shows an immense impact on the implications and prediction of possible improvements to forecast disaster regions. Big data represents a high volume, speed, and variety of information assets that require different technologies and analytical techniques to transform them into measurable value. Big data are used to provide a forecast of harvested activities, to promote operational decisions in a real-time scenario to redesign business processes. Due to the effects of environmental changes, including global warming, climatic disrupts, and ecological impact, humans are facing a startling number of natural agricultural disasters and crises as well as their related consequences. Information and communication technologies are becoming increasingly popular and used in daily lives, which helps to address these challenges and implications. However, these techniques have not been used in relief and disaster response.

The agricultural evolution, the development of mobile data, GPS systems, social networking assisted location-based information, satellite imaging, and smart card data has been explored to resolve the challenges of Agricultural Disaster Management. The explosion of this sensing information with big data ensures a new way of avoiding research methodology challenges for effective agricultural disaster management. It encourages tremendous amounts to study and improve relevant innovations and software to accomplish more efficient disaster management perspectives. Social science researchers are more interested in exploring the practical issues of climate change and meteorological disaster management for technological innovation in economics to reduce weather loss and achieve sustainable development goals for broader development objectives. There are many research values to the environment, and meteorological data has been offered by big data analysis with technological support for these studies.

The main objective of this special issue is to present a multi-disciplinary field of research and development and to encourage practitioners and researchers who work on Big Data Analytics to look into Agricultural disaster management as a promising application for various technologies.

The topics of interest for the special issue include, but not limited to the following:

• Advanced Information, Communication, and technology (ICT) including big data, AI, internet of things (IoT) and Unmanned Aerial Vehicles (UAV) for disaster management
• Agricultural disaster risk management and capability assessment
• Agricultural disaster relief, resilience, and research
• Agricultural disaster information processing
• Risk management and risk governance
• Security and privacy issues in agricultural disaster management
• Hazard and vulnerability analysis in smart farming
• The state-of-the-art in the command and control room
• Simulation and gaming for agricultural disaster management
• Resource management and optimization for sustainable agriculture
• Big data in agricultural disaster management
• Disaster prevention, mitigation, preparedness, response, and recovery using big data
• Interaction, access, visualization of big data for agricultural disaster management
• Cloud-based decision support system
• Environmental big data and knowledge management
• Big data analysis in climate economics and management science
• Assessment models for climatic and meteorological disaster
• Emergency management of meteorological disasters
• Integrating climate factors into modeling in economics and management science

Submission Deadline: February 1, 2021

We also encourage submission of papers that have been published in conference proceedings, with the requirement that the authors have made significant extensions as compared to the already published version of the study.

Please read through our Information for Authors prior to submitting your research for consideration. Questions or pre-submission queries? Please contact Lead Guest Editor Dr. S. Balamurugan at balamurugan@ieee.org