A Study of Bank Line Shifting of the Selected Reach of Jamuna River Using Multi-Variant Regression Model

Document Type : Regular Article


1 M.Sc. Student, Department of Water Resources Engineering, BUET, Bangladesh

2 Professor, Department of Water Resources Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka-1000, Bangladesh


Jamuna river is a morphologically very dynamic river. It carries a vast sediment load from the erosive foothills of Himalaya mountain. The length of the Jamuna River is 220 km. For this research work Jamalpur district is selected to assess morphological changes using hydrodynamic, Artificial intelligence and google satellite images. First, the hydrodynamic model was calibrated and validated at Kazipur station for the years 2018 and 2019 respectively. Then, left overbank maximum discharge, water level, velocity, the slope was extracted from HEC-RAS 1D at 300 m interval interpolated cross-section. Then, this cross-section was exported as a shapefile. In google earth, the erosion rate was measured corresponding to this interpolated cross-section. The results of the hydrodynamic model were given as input variable and erosion rate as an output variable in Machine learning and deep learning technique. Calibration and validation of the regression model was done for the years 2018 and 2019 respectively. This research work can be helpful to locate the area which are vulnerable to bank erosion.


Main Subjects

[1]     Hooke JM. An analysis of the processes of river bank erosion. J Hydrol 1979;42:39–62. https://doi.org/10.1016/0022-1694(79)90005-2.
[2]     Bridge JS. Rivers and floodplains: forms, processes, and sedimentary record. John Wiley & Sons; 2009.
[3]     CEGIS. Prediction of river bank erosion along the Jamuna, the Ganges, and the Padma River, Prepared for BWDB, Dhaka, Bangladesh. Center for Environmental and Geographic Information Services; 2018.
[4]     Islam MDF, Rashid ANMB. Riverbank erosion displacees in Bangladesh: need for institutional response and policy intervention. Bangladesh J Bioeth 2011;2:4–19.
[5]     Centre for Environmental and Geographic Information Services (CEGIS), Impact of Climate Change on Morphological Processes in Different River of Bangladesh 2010.
[6]     Center for Environmental and Geographic Information Services (CEGIS), River Erosion Prediction Model. https://www.cegisbd.com/DivisionInfo?Div=MOR n.d.
[7]     Nardi L, Campo L, Rinaldi M. Quantification of riverbank erosion and application in risk analysis. Nat Hazards 2013;69:869–87. https://doi.org/10.1007/s11069-013-0741-8.
[8]     Atkinson PM, German SE, Sear DA, Clark MJ. Exploring the Relations Between Riverbank Erosion and Geomorphological Controls Using Geographically Weighted Logistic Regression. Geogr Anal 2003;35:58–82. https://doi.org/10.1111/j.1538-4632.2003.tb01101.x.
[9]     Abam TKS. Factors affecting distribution of instability of river banks in the Niger delta. Eng Geol 1993;35:123–33. https://doi.org/10.1016/0013-7952(93)90074-M.
[10]   Raudkivi AJ. Loose boundary hydraulics. CRC Press; 1998.
[11]   Roslan ZA, Naimah Y, Roseli ZA. River bank erosion risk potential with regards to soil erodibility. River Basin Manag VII Wessex Inst Technol UK 2013:289–297.
[12]   Zhang W, Zhang R, Wu C, Goh ATC, Lacasse S, Liu Z, et al. State-of-the-art review of soft computing applications in underground excavations. Geosci Front 2020;11:1095–106. https://doi.org/10.1016/j.gsf.2019.12.003.
[13]   Zhang WG, Li HR, Wu CZ, Li YQ, Liu ZQ, Liu HL. Soft computing approach for prediction of surface settlement induced by earth pressure balance shield tunneling. Undergr Sp 2021;6:353–63. https://doi.org/10.1016/j.undsp.2019.12.003.
[14]   Zhang W, Goh ATC. Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front 2016;7:45–52. https://doi.org/10.1016/j.gsf.2014.10.003.
[15]   Zhang W, Wu C, Zhong H, Li Y, Wang L. Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geosci Front 2021;12:469–77. https://doi.org/10.1016/j.gsf.2020.03.007.
[16]   Zhang W, Li H, Li Y, Liu H, Chen Y, Ding X. Application of deep learning algorithms in geotechnical engineering: a short critical review. Artif Intell Rev 2021;54:5633–73. https://doi.org/10.1007/s10462-021-09967-1.
[17]   Zhang W, Zhang R, Wu C, Goh ATC, Wang L. Assessment of basal heave stability for braced excavations in anisotropic clay using extreme gradient boosting and random forest regression. Undergr Sp 2022;7:233–41. https://doi.org/10.1016/j.undsp.2020.03.001.
[18]   Saadon A, Abdullah J, Muhammad NS, Ariffin J. Development of riverbank erosion rate predictor for natural channels using NARX-QR Factorization model: a case study of Sg. Bernam, Selangor, Malaysia. Neural Comput Appl 2020;32:14839–49. https://doi.org/10.1007/s00521-020-04835-5.
[19]   Ashraf M, Shakir AS. Prediction of river bank erosion and protection works in a reach of Chenab River, Pakistan. Arab J Geosci 2018;11:145. https://doi.org/10.1007/s12517-018-3493-7.
[20]   Pal PK, Rahman A, Yunus A. A Study on Seasonal Variation of Hydro-Morphodynamic Parameters of Jamuna River. Int J Eng Res 2017;6:307–11.
[21]   Khan M. Modeling the bank erosion of selected reach of Jamuna river 2015.