Urban Flood Studies and Rainfall Trend Analysis: A Case Study of the Pili River, Nagpur
Keywords:
Urban Flood Studies, Rainfall Trend AnalysisAbstract
Urban flooding is becoming a stand-alone challenge throughout the globe, particularly in fast urbanizing regions with low drainage infrastructure. This study, therefore, localizes itself to the urban flooding along the Pili River, Nagpur, India, to identify flood-prone areas and analyse the factors contributing to flood events. The assessment is derived from 25 years of rainfall data, and hydrological survey assessments and discharge estimations to quantify flooding in the region. The research identifies that urbanization, encroachment, and inefficient waste management enhance the risk of flooding. Various recommendations are proposed to improve flood resilience by upgrading flood infrastructures, sustainable planning, and enhanced flood management practice.
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Dash, S., Topre, C., Vijay, R., & Gupta, R. (2016). Hydrodynamic Simulation of Pili River for Riverbed Development Using HEC-RAS. National Conference on Water Resources & Flood Management with Special Reference to Flood Modelling, October 14-15, 2016, SVNIT Surat. Retrieved from https://www.researchgate.net/publication/319449524
Gaurkhede, N. T., & Adane, V. S. (2022). Flood Susceptibility Analysis Using Freely Available Data, GIS, And Frequency Ratio Model for Nagpur, India. Applied Ecology and Environmental Research, 21(3), 2341–2361. https://doi.org/10.15666/aeer/2103_23412361
Eastman, S. O., & Khan, B. (2022). Statistical Analysis of Rainfall Data: A Case Study of Georgetown, Guyana. IJIRMPS, 10(6). E-ISSN: 2349-7300. Available under the Creative Commons Attribution-ShareAlike 4.0 International License.
Bhagat, R. S., & Bisen, D. K. (2013). Flood study of Wainganga River in Maharashtra using GIS & remote sensing techniques. International Journal of Science and Research (IJSR), 5(4), 1 6, pp 782-784.
Tanim, A. H., McRae, C. B., Tavakol-Davani, H., & Goharian, E. (2021). Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning. Water, 14(7), 1140. https://doi.org/10.3390/w14071140
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