Abstract
Recently, across the globe use of big data has been increased to a great extent. Due to the use of computer, the generation of data in every domain has been increased to a great extent, due to the same a concept of big data has been crystallized among the researchers. Flood situation in India is very common, and due to the poor infrastructure, its control and management are difficult at the grassroot level. Surat is one of the cities in western India, which is highly prone to flooding situations. Urban flood control is one of the necessary needs in the context of smart urban planning and the said can be derived with the help of big data in today’s era. Similar to most advanced and developed cities of countries like US, Europe, China, and Japan leveraging the benefits of big data for flood control and mitigation, Surat can also make use of the big data. In the present paper, we have started with the overview the need of early prediction of flood and its control across the urban areas of the globe as well as Surat city, followed by the common advantages of big data in flood control for coastal cities like Surat as well as field-specific uses and concluded with a broad framework portraying big data in context to flood control of Surat city.
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References
Wang, R.Q.: Big data of urban flooding: dance with social media, citizen science, and artificial intelligence. In: Geophysical Research Abstracts, vol. 20. EGU2018–404, 2018 EGU General Assembly 2018
Dhiman, R., Vishnu Radhan, R., Eldho, T.I., Inamdar, A.: Flood risk and adaptation in Indian coastal cities: recent scenarios. Appl. Water Sci. 9(5), 1–16.
Zhang, H., Ma, W.C., Wang, X.R.: Rapid urbanization and implications for flood risk management in hinterland of the Pearl River Delta, China: the Foshan study. Sensors 8(4), 2223–2239 (2008)
Bhat, G.K., Raghupathi, U., Rajasekar, U., Karanath, A.: Urbanization—Poverty—Climate change. A Ssynthesis Report—India, vol I. TARU Leading Edge Pvt. Ltd., Gurgaon, Haryana, India
Biswas, A.K., Saklani, U., Tortajada, C.: Truth about urban flooding: cities like Mumbai get inundated regularly due to administrative apathy, not climate change. Times of India, Mumbai Edition 31.08.2017 (2017)
Mohan, R., Dasgupta, S.: Urban development in India in the twenty first century: policies for accelerating urban growth. In: Fifth Annual Conference on Indian Economic Policy Reform at the Stanford Centre for International Development on 4–5 June 2004. Working Paper No. 231 (2004)
Ahmed, A., Dinye, R.D.: Urbanisation and the challenges of development controls in Ghana: a case study of WA Township. J. Sustain. Dev. Afr. 13(7), 210–235 (2011)
Nukala, R.B., Mutz, D.: Strategic approach for sustainable land use in an emerging country—case of India. In: 2015 World Bank Conference on Land and Poverty. The World Bank, Washington DC, 23–27 Mar 2015 (2015)
Waghwala, R., Aagnihotri, P.G.: Flood risk assessment and resilience strategies for flood risk management: a case study of Surat City. Int. J Disaster Risk Reduct. 9(5), 1–16 (2019)
Dewan, A.M., Yamaguchi Y: Effect of land cover changes on flooding: example from Greater Dhaka of Bangladesh. Int J Geoinf 4(1). https://creativecity.gscc.osaka-cu.ac.jp/IJG/article/view/604 (2008)
Hammond, M.J., Chen, A.S., Djordjević, S., Butler, D., Mark, O.: Urban flood impact assessment: A state-of-the-art review. Urban Water J 12(1), 14–29 (2015). https://doi.org/10.1080/1573062X.2013.857421
Dewan, A.M., Islam, M.M., Kumamoto, T., Nishigaki, M.: Evaluating flood hazard for land-use planning in greater Dhaka of Bangladesh using remote sensing and GIS techniques. Water Resour. Manage. 21, 1601–1612 (2007). https://doi.org/10.1007/s11269-006-9116-1
Dewan, A.M., Yamaguchi, Y.: Land use and land cover change in Greater Dhaka, Bangladesh: using remote sensing to promote sustainable urbanization. Appl. Geogr. 29(3), 390–401 (2009)
Patel, K.A., Bhagat, S.S.: An overview of flood resilience: a case of Surat City. Int. J. Manage. Technol. Eng. 9(1), 3043–3049 (2019)
Brody, S., Blessing, R., Sebastian, A., Bedient, P.: Examining the impact of land use/land cover characteristics on flood losses. J. Environ. Planning Manage. 57(8), 1252–1265 (2014)
Thieken, A.H., Apel, H., Merz, B.: Assessing the probability of large-scale flood loss events: a case study for the river Rhine, Germany. J. Flood Risk Manage. 8(3), 247–262 (2015)
Witting, A., Kallee, D.: Ruling big data in urban flood risk mitigation. In: Yearbook of Swiss Administrative Sciences, pp. 11–22 (2015)
Keiza, S.P., Mary, A.V.A.: Prediction of rapid floods from big data using map reduce technique. Glob. J. Pure Appl. Math. 12(1), 369–373 (2016). ISSN 0973-1768
Wu, Z., Shen, Y., Wang, H.: Assessing urban areas’ vulnerability to flood disaster based on text data: a case study in Zhengzhou City. Sustainability 11(4548), 1–15 (2019). https://doi.org/10.3390/su11174548
Lin, T., Liu, X.F., Song, J.C., Zhang, G.Q., Jia, Y.Q., Tu, Z.Z., Zheng, Z.H., Liu, C.L.: Urban waterlogging risk assessment based on internet open data: a case study in China. Habitat Int. 71, 88–96 (2018)
Sadler, J.M., Goodall, J.L., Morsy, M.M., Spencer, K.: Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest. J. Hydrol. 559, 43–55 (2018)
Yang, X., Beiqun, L., Zaiwu, G.: Real-time identification of urban rainstorm waterlogging disasters based on Weibo big data. Nat. Hazards 94, 833–842 (2018)
City Mayor’s Report. The world’s fastest growing cities and urban area from 2006 to 2020. https://www.citymayors.com/statistics/urban_growth1.html. Accessed 07 Dec 2018
Singapuri, N.C., Bipinkumar: Two local industries of Surat under the muddled conditions of business environment–evaluation under the international factors. J. Adv. Scholar. Res. Allied Educ. (JASRAE). 15(12), 783–801. E-ISSN 2230-7540. https://doi.org/10.29070/JASRAE.
Kale, V. S.: Long period fluctuations in monsoon floods in the Deccan peninsula, India. J. Geol. Soc. India 53, 5–15 (1999). https://hdl.handle.net/11718/114
Agnihotri, P.G., Patel, J.N.: Improving carrying capacity of river Tapi (Surat, India) by channel modification. Int. J. Adv. Eng. Technol II(II), 231–238 (2011)
Mavalankar, D.: Lessons from massive floods of 2006 in Surat city: a framework for application of MS/OR techniques to improve dam management to prevent flood (2008)
Ahmad, A., Khan, M., Paul, A., Din, S., Rathore, M.M., Jeon, G., Chio, G.S.: Towards modeling and optimization of features selection in big data based social Internet of things. Future Gener. Comput. Syst. 82, 715–726 (2018)
Aqib, M., Mehmood, R., Alzahrani, A., Katib, I., Albeshri, A., Altowaijri, S.M.: Rapid transit systems: smarter urban planning using big data, in-memory computing, deep learning, and GPUs. Sustainability 11, 2736 (2019)
Wang, R.Q., Mao, H., Wang, Y., Rae, C., Shaw, W.: Hyper-resolution monitoring of urban flooding with social media and crowdsourcing data. Comput. Geosci. 111, 139–147 (2018)
Eilander, D., Trambauer, P., Wagemaker, J., Van Loenen, A.: Harvesting social media for generation of near real-time flood maps. Procedia Eng. 154, 176–183 (2016)
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Gandhi, P.J., Agnihotri, P.G. (2022). A Study on Benefits of Big Data for Urban Flood Control in Surat City. In: Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-16-0739-4_93
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