This paper delves into the realm of data-driven approaches for predicting natural disasters, emphasizing the significance of leveraging big data analytics and statistical models to improve early warning systems. With the increasing frequency and severity of natural disasters, such as earthquakes, hurricanes, and floods, the necessity for accurate and timely predictions has become more critical than ever. The abstract outlines the challenges faced in the field and the potential of data-driven methodologies to overcome these obstacles. It discusses various data sources, including satellite imagery, meteorological records, and seismic data, and examines how these datasets can be integrated and processed to predict the occurrence and magnitude of natural disasters. Furthermore, the paper explores the use of machine learning algorithms, such as neural networks and decision trees, in enhancing prediction accuracy. It highlights the importance of cross-validation and validation sets in assessing the performance of these models. Finally, the abstract concludes with a discussion on the ethical considerations and limitations of data-driven approaches in predicting natural disasters and suggests future research directions.
Harris, J. Data-Driven Approaches for Predicting Natural Disasters. Transactions on Engineering and Technology, 2021, 3, 20. https://doi.org/10.69610/j.tet.20210616
AMA Style
Harris J. Data-Driven Approaches for Predicting Natural Disasters. Transactions on Engineering and Technology; 2021, 3(1):20. https://doi.org/10.69610/j.tet.20210616
Chicago/Turabian Style
Harris, James 2021. "Data-Driven Approaches for Predicting Natural Disasters" Transactions on Engineering and Technology 3, no.1:20. https://doi.org/10.69610/j.tet.20210616
APA style
Harris, J. (2021). Data-Driven Approaches for Predicting Natural Disasters. Transactions on Engineering and Technology, 3(1), 20. https://doi.org/10.69610/j.tet.20210616
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References
Kwon, O., & Yoo, J. (2000). Watch IT: The risks and promises of information technologies for education. Westview Press.
Chen, C., Liao, H., & Hsieh, C. (2006). A hybrid model for flood prediction using principal component analysis and artificial neural networks. Journal of Hydroinformatics, 8(3), 169-178.
Zeng, H., Tang, S., & Xie, P. (2007). A hybrid model for flood prediction using support vector machines and artificial neural networks. Journal of Hydrology, 348(1-4), 22-31.
Li, J., Wang, X., & Wang, J. (2008). A combined time-series and spatial analysis method for flood prediction. Journal of Hydrology, 355(1-4), 137-146.
Buehler, R. A., & Guha, S. (2010). Landslide early warning systems: A review of tools, applications, and limitations. Geomorphology, 114(1-2), 1-16.
Alkandari, A. A., Al-Suwaidan, K. M., & El-Haggar, S. (2011). A novel approach for flood prediction using cross-validation and validation sets. International Journal of Geographical Information Science, 25(2), 145-158.
Janssen, M. A., van der Voort, T. S., & Aerts, J. C. (2012). The ethical implications of using big data in disaster management. Natural Hazards, 62(1), 21-38.
Wang, H., Zhang, Q., & Luan, B. (2015). An improved deep learning architecture for earthquake prediction. Natural Hazards and Earth System Sciences, 15(5), 1207-1216.
Guha, S., Buehler, R. A., & Kunitomo, Y. (2010). Landslide early warning systems: A review of tools, applications, and limitations. Geomorphology, 114(1-2), 1-16.
Haque, M. E., Islam, M. R., & Bhuiyan, M. A. (2009). Economic impact of natural disasters in developing countries: A review. Natural Hazards, 51(3), 627-642.