Assessment of riverbank erosion and its prediction using geospatial and machine learning techniques

Md Naimur Rahman, Md Mushfiqus Saleheen, Hamza EL Fadili, Md Nazirul Islam Sarker

Research output: Chapter in book/report/conference proceedingChapterpeer-review

Abstract

The Jamuna River is of significant importance in Bangladesh, playing essential roles in irrigation, fishing, transportation, and as a source of drinking water. This chapter evaluates the erosion of the Jamuna River and its potential future changes within the Ulipur Upazila, Kurigram, Bangladesh. The research employs supervised and unsupervised classification methods to extract patterns of erosion and accretion. Furthermore, to project future erosion trends, an artificial neural network model is utilized. Key findings of the study reveal a rapid increase in riverbank erosion over the past two decades. Specifically, the area affected by erosion expanded to cover 3101 ha between 2003 and 2013, and this trend continued, encompassing 4232 ha from 2013 to 2022. Despite this, there is a notable overall reduction in erosion of 3820 ha during the entire period from 2003 to 2022, compared to the changes in each previous decade. Likewise, the prediction outcomes suggest a substantial decline in both erosion and accretion. Notably, by the year 2042, erosion is projected to affect a significantly smaller area of 132 ha. Hydrometeorological and anthropogenic factors could play a pivotal role in reducing the vulnerability to erosion and accretion in the area.

Original languageEnglish
Title of host publicationApplications of Geospatial Technology and Modeling for River Basin Management
EditorsSubodh Chandra Pal, Uday Chatterjee, Rabin Chakrabortty
PublisherElsevier
Chapter19
Pages493-509
Number of pages17
ISBN (Print)9780443238901
DOIs
Publication statusPublished - 28 Sept 2024

Publication series

NameModern Cartography Series
PublisherElsevier
Volume12
ISSN (Print)1363-0814

Scopus Subject Areas

  • Geography, Planning and Development
  • Earth and Planetary Sciences (miscellaneous)

User-Defined Keywords

  • accretion
  • erosion
  • geospatial
  • Jamuna
  • machine learning
  • river

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