Assessing the Impact of the Farakka Barrage on Hydrological Alteration in the Padma River with Future Insight

Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Shumona Akhter, Kutub Uddin Eibek, Md. Mostafizur Rahman, Swades Pal, Mohd Waseem Naikoo, Atiqur Rahman and Amir Mosavi
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Abu Reza Md. Towfiqul Islam: Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
Swapan Talukdar: Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi 110025, India
Shumona Akhter: Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
Kutub Uddin Eibek: Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh
Md. Mostafizur Rahman: Department of Environmental Sciences, Jahangirnagar University, Dhaka 1342, Bangladesh
Swades Pal: Department of Geography, University of Gour Banga, Malda 732101, India
Mohd Waseem Naikoo: Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi 110025, India
Atiqur Rahman: Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi 110025, India
Amir Mosavi: John von Neumann Faculty of Informatics, Obuda University, 1034 Budapest, Hungary

Sustainability, 2022, vol. 14, issue 9, 1-26

Abstract: Climate change and human interventions (e.g., massive barrages, dams, sand mining, and sluice gates) in the Ganga–Padma River (India and Bangladesh) have escalated in recent decades, disrupting the natural flow regime and habitat. This study employed innovative trend analysis (ITA), range of variability approach (RVA), and continuous wavelet analysis (CWA) to quantify the past to future hydrological change in the river because of the building of the Farakka Barrage (FB). We also forecast flow regimes using unique hybrid machine learning techniques based on particle swarm optimization (PSO). The ITA findings revealed that the average discharge trended substantially negatively throughout the dry season (January–May). However, the RVA analysis showed that average discharge was lower than environmental flows. The CWA indicated that the FB has a significant influence on the periodicity of the streamflow regime. PSO-Reduced Error Pruning Tree (REPTree) was the best fit for average discharge prediction (RMSE = 0.14), PSO-random forest (RF) was the best match for maximum discharge (RMSE = 0.3), and PSO-M5P (RMSE = 0.18) was better for the lowest discharge prediction. Furthermore, the basin’s discharge has reduced over time, concerning the riparian environment. This research describes the measurement of hydrological change and forecasts the discharge for upcoming days, which might be valuable in developing sustainable water resource management plans in this location.

Keywords: flow regimes; innovative trend analysis; range of variability; machine learning; data science; artificial intelligence; big data; hydrological model; climate change; environmental flows (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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