Role of Artificial Intelligence (AI) in Combined Disaster Management
Keywords:
Artificial intelligence; Geographic information; Remote Sensing; Disaster Management
Abstract
Abstract Views: 372
Disaster management is an important part in case of catastrophe for the wellbeing of humanity to save them from severe effects. For active disaster management combined approaches may be used such as applications of artificial intelligence, e.g. geographical analysis, risk mapping and tracking, obscure sensing ability, advance technologies for drone and machine learning and urban planning based on smart cities technology, analysis of hotspots and analysis of environmental impacts are modern technologies which needs to be studied more in the context of disaster management. Researchers of social sciences used different procedures and technology to analyze the hazards, risks and catastrophe between disciplinal.They utilized empirical and evaluative collection of data and itsanalysis techniques. The current study is an overview of present applications of AI being helpful in disaster management throughout its tetrahedral states. AI is important in all states of disaster management as it's a faster solution than other technologies. Integration of two basic supportive tools which are Remote Sensing (RS) and Geographically Information System (GIS) in disaster management provides higher level of planning and its analysis. It can help authorities to take quick and effective decisions in case of natural disaster.
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References
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Chen, J., Li, Q., Wang, H., & Deng, M. (2020). A machine learning ensemble approach based on random forest and radial basis function neural network for risk evaluation of regional flood disaster: a case study of the Yangtze River Delta, China. International journal of environmental research and public health, 17(1), 49.
Chen, W., Peng, J., Hong, H., Shahabi, H., Pradhan, B., Liu, J., . . . Duan, Z. (2018). Landslide susceptibility modelling using GIS-based machine learning techniques for Chongren County, Jiangxi Province, China. Science of The Total Environment, 626, 1121-1135.
Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of The Total Environment, 651, 2087-2096.
Ci, T., Liu, Z., & Wang, Y. (2019). Assessment of the degree of building damage caused by disaster using convolutional neural networks in combination with ordinal regression. Remote Sensing, 11(23), 2858.
Der Sarkissian, R., Zaninetti, J.-M., & Abdallah, C. (2019). The use of geospatial information as support for Disaster Risk Reduction; contextualization to Baalbek-Hermel Governorate/Lebanon. Applied geography, 111, 102075.
Deshmukh, K. H., & Bamnote, G. R. (2021). Role of Deep Learning in Disaster Prediction. In Proceedings of Integrated Intelligence Enable Networks and Computing (pp. 697-705): Springer.
Dottori, F., Martina, M. L. V., & Figueiredo, R. (2018). A methodology for flood susceptibility and vulnerability analysis in complex flood scenarios. Journal of Flood Risk Management, 11, S632-S645.
Erdelj, M., Natalizio, E., Chowdhury, K. R., & Akyildiz, I. F. (2017). Help from the sky: Leveraging UAVs for disaster management. IEEE Pervasive Computing, 16(1), 24-32.
Fanos, A. M., & Pradhan, B. (2019). A novel hybrid machine learning-based model for rockfall source identification in presence of other landslide types using LiDAR and GIS. Earth Systems and Environment, 3(3), 491-506.
Fariza, A., Rusydi, I., Hasim, J. A. N., & Basofi, A. (2017). Spatial flood risk mapping in east Java, Indonesia, using analytic hierarchy process—natural breaks classification. Paper presented at the 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE).
Fava, P. R., Fritz, S., & Castellano, A. (2010). The Use of Geographic Information Systems for Disaster Risk Reduction Programmes in Africa. User Manual, 94.
Fu, Q., Sun, E., Meng, K., Li, M., & Zhang, Y. (2020). Deep Q-learning for routing schemes in SDN-based data center networks. IEEE Access, 8, 103491-103499.
Grant, R. A., Raulin, J. P., & Freund, F. T. (2015). Changes in animal activity prior to a major (M= 7) earthquake in the Peruvian Andes. Physics and Chemistry of the Earth, Parts A/B/C, 85, 69-77.
Jahan, Z., Sarwar, F., Younes, I., Sadaf, R., & Ahmad, A. (2019). Assessment of smog pattern and its effects on visibility in Lahore using remote sensing and GIS. International Journal of Economic and Environmental Geology, 10(2), 55-59.
Jena, R., Pradhan, B., & Alamri, A. M. (2020). Susceptibility to seismic amplification and earthquake probability estimation using recurrent neural network (RNN) Model in Odisha, India. Applied Sciences, 10(15), 5355.
Jena, R., Pradhan, B., Beydoun, G., Alamri, A. M., & Sofyan, H. (2020). Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Science of The Total Environment, 749, 141582.
Jilani, Z., Mehmood, T., Alam, A., Awais, M., & Iqbal, T. (2017). Monitoring and descriptive analysis of radon in relation to seismic activity of Northern Pakistan. Journal of environmental radioactivity, 172, 43-51.
Kabir, M. Y., Gruzdev, S., & Madria, S. (2020). STIMULATE: A System for Real-time Information Acquisition and Learning for Disaster Management. Paper presented at the 2020 21st IEEE International Conference on Mobile Data Management (MDM).
Kankanamge, N., Yigitcanlar, T., & Goonetilleke, A. (2020). How engaging are disaster management related social media channels? The case of Australian state emergency organisations. International Journal of Disaster Risk Reduction, 48, 101571.
Khosravi, K., Pham, B. T., Chapi, K., Shirzadi, A., Shahabi, H., Revhaug, I., . . . Bui, D. T. (2018). A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment, 627, 744- 755.
Khouj, M., López, C., Sarkaria, S., & Marti, J. (2011). Disaster management in real time simulation using machine learning. Paper presented at the 2011 24th Canadian Conference on Electrical and Computer Engineering (CCECE).
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Published
2022-05-10
Section
Articles
Copyright (c) 2022 Muhammad Arfan, Zara Khan, Nael Qadri, M. Hassan Hameed, Abdul Rehman Amir
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