Agricultural Disaster Risk Management and Capability Assessment Using Big Data Analytics

Area of Science:

• Agricultural Science
• Environmental Science
• Data Science

Background:

• Climate change and extreme weather events pose significant threats to global agriculture, leading to crop production declines.
• Natural disasters like floods and wildfires exacerbate agricultural vulnerabilities, increasing the need for robust disaster management strategies.
• The integration of information technology into emergency management is a growing area of academic and governmental focus.

Purpose of the Study:

• To review the current state of research in agricultural disaster risk management (ADRM).
• To analyze the role and significance of big data in ADRM.
• To provide a bibliometric analysis of ADRM research over the past decade, focusing on disaster types and big data utilization.

Main Methods:

• Bibliometric analysis of research publications from the last ten years.
• Assessment of annual publication growth, topic categories, and productivity.
• Evaluation of data flux and its impact on forecasting performance.
• Case study incorporating proposed ADRM mechanisms for flood prediction using Indian Meteorology Department data.

Main Results:

• Identified trends in ADRM research, including disaster types and the application of big data.
• Demonstrated the critical role of comprehensive data analysis in improving disaster forecasting.
• Showcased the effectiveness of the proposed ADRM mechanism in enhancing early flood prediction capabilities.

Conclusions:

• Big data analytics are crucial for advancing agricultural disaster risk management.
• The proposed ADRM framework, integrating meteorological data, significantly improves flood prediction accuracy and lead time.
• Continued research and technological integration are vital for building agricultural resilience against climate change impacts.