Impacts of GERD on the Accumulated Sediment in Lake Nubia Using Machine Learning and GIS Techniques

Abdelazim Negm*, Mohamed Elsahabi, Mohamed Abdel-Nasser, Karar Mahmoud, Kamal Ali

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

2 Citations (Scopus)

Abstract

This chapter aims to study and discuss the effect (hypothesis) of constructing the GERD on the deposited sediment amount in the AHDL. To achieve the objective of this chapter; a machine learning approach represented in a regression tree (RTs) model was used and calibrated to simulate the changes in bed levels and water velocities in the study area within AHDL by using the field measured data and GIS analysis for the year 2008 (reference case). Furthermore, a model verification process has been done to ensure the applicability of the applied model using the available field data in the year 2012. The results of the bed levels and velocities during calibration and verification of the model show low values of RMSE % (for calibration 2.90 and 2.57 for bed levels and velocities, respectively, and for calibration 4.66 and 4.98% for bed levels and velocities, respectively) and high R 2 (for calibration 0.9975 and 0.9978 for bed levels and velocities, respectively, and for verification 0.9921 and 0.9959 for bed levels and velocities, respectively), indicating that the model was efficiently calibrated and verified. It shows good agreement between the simulated and measured data (by comparisons of simulated longitudinal and cross sections with the measured ones). Thus, this model is considered trustful and reliable to the prediction of sediment and erosion (bed changes) in the study area within AHDL after GERD construction. Accordingly, four of the possible scenarios are performed through the well-calibrated and verified model by reducing the flow quantity and its associated annual sediment rate by 5–10 and 60–65%, respectively. These scenarios are considered as prediction cases after GERD construction. The impact of GERD construction is then studied by comparing some sections along and across the studied lake portion before and after GERD construction (applied scenarios). This impact appeared clearly as a reduction in the amount of the accumulated sediment (decrease in bed levels) accompanied by an increase in erosion amount. Based on the applied scenarios, results showed that the amount of sediment was reduced by 25–27%, 52–55%, 76–81%, and 90–97% in the year 2030, 2040, 2050, and 2060, respectively, compared to the predicted amount of sediment in the year 2020 without GERD operation/construction. As a positive impact of the GERD construction, the lifetime of the upstream AHD reservoir will be prolonged due to the decrease in the amount of the accumulated sediment. This study provides decision-makers with a preliminary knowledge about the impact of GERD operation/construction on AHDL sediment pattern and consequently on Egypt and Sudan. Moreover, the current study opens new windows for future research to investigate the impacts of the different aspects of GERD of AHDL.

Original languageEnglish
Title of host publicationHandbook of Environmental Chemistry
Subtitle of host publicationGrand Ethiopian Renaissance Dam Versus Aswan High Dam
EditorsAbdelazim M. Negm, Sommer Abdel-Fattah
Pages271-327
Number of pages57
ISBN (Electronic)978-3-319-95600-8
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA3 Part of a book or another research book

Publication series

NameHandbook of Environmental Chemistry
PublisherSpringer
Volume79
ISSN (Print)1867-979X
ISSN (Electronic)1616-864X

Keywords

  • AHDL
  • Egypt
  • Ethiopia
  • GERD
  • GIS
  • Machine learning
  • Modeling
  • Regression tree
  • Sediment

Fingerprint Dive into the research topics of 'Impacts of GERD on the Accumulated Sediment in Lake Nubia Using Machine Learning and GIS Techniques'. Together they form a unique fingerprint.

Cite this