Predicting climate factors based on big data analytics based agricultural disaster management. (December 2022)

• Record Type: Journal Article
• Authors: Jaber, Mustafa Musa; Ali, Mohammed Hasan; Abd, Sura Khalil; Jassim, Mustafa Mohammed; Alkhayyat, Ahmed; Aziz, Hussein Waheed; Alkhuwaylidee, Ahmed Rashid
• Is Part Of: Physics and chemistry of the earth. Volume 128(2022)
• Publication Date: 2022-12
• Subjects: Big data analytics -- Environment -- Agricultural disaster -- Climate factor; Geophysics -- Periodicals; Geochemistry -- Periodicals; Earth sciences -- Periodicals; Geodesy -- Periodicals; Astrophysics -- Periodicals; 550
• DOI: 10.1016/j.pce.2022.103243
• Languages: English
• ISSNs: 1474-7065

Abstract

Abstract: Aggressive, unexpected, and catastrophic changes in the environment-induced or impacted by the cultivation of land, crops, and cattle are known as agricultural disasters. In agriculture, the volume of data unpredictability, processing, and data management standards for interoperability are significant concerns. While natural catastrophes are still a considerable problem, the enormous amount of data available has opened up new avenues for coping. Accordingly, big data analytics has profoundly changed the way people respond to disasters in the agriculture sector. In this paper, the Data handling model using big data analytics (DHM-BDA) explores the role of big data in managing agricultural disasters and highlights the technical status of delivering practical and efficient disaster management solutions. DHM-BDA is used to address the essential sources of big data that include climatic causes and associated successes and developing technological problems in different disaster management phases. In addition, it aids in the monitoring, mitigation, alleviation, and acceptance of agricultural catastrophes and the process of recovery and rebuilding. The simulation findings have been executed, and the suggested model enhances the prediction ratio of 98.9%, decision-making level of 97.8%, data management of 96.5%, production ratio of 95.6%, and risk reduction ratio of 97.1% compared to other existing approaches. Highlights: Aggressive, unexpected, and catastrophic changes in the environment-induced. Data unpredictability, processing, and data management standards for interoperability. Data handling model using big data analytics (DHM-BDA)explores. The simulation findings have been executed, and the suggested model enhances.