Articles | Volume 2, issue 1
https://doi.org/10.5194/soil-2-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/soil-2-1-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Original research article
| 
15 Jan 2016
Original research article |
| 15 Jan 2016
Development of a statistical tool for the estimation of riverbank erosion probability
School of Environmental Engineering, Technical University
of Crete, Chania, Greece
G. V. Giannakis
School of Environmental Engineering, Technical University
of Crete, Chania, Greece
M. A. Lilli
School of Environmental Engineering, Technical University
of Crete, Chania, Greece
E. Ioannidou
School of Environmental Engineering, Technical University
of Crete, Chania, Greece
N. P. Nikolaidis
School of Environmental Engineering, Technical University
of Crete, Chania, Greece
G. P. Karatzas
School of Environmental Engineering, Technical University
of Crete, Chania, Greece
Viewed
Total article views: 3,586 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Jun 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,632 | 1,671 | 283 | 3,586 | 231 | 253 |
- HTML: 1,632
- PDF: 1,671
- XML: 283
- Total: 3,586
- BibTeX: 231
- EndNote: 253
Total article views: 3,103 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jan 2016)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,470 | 1,364 | 269 | 3,103 | 218 | 240 |
- HTML: 1,470
- PDF: 1,364
- XML: 269
- Total: 3,103
- BibTeX: 218
- EndNote: 240
Total article views: 483 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Jun 2015)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 162 | 307 | 14 | 483 | 13 | 13 |
- HTML: 162
- PDF: 307
- XML: 14
- Total: 483
- BibTeX: 13
- EndNote: 13
Cited
16 citations as recorded by crossref.
- Predictive models for the estimation of riverbank erosion rates A. Saadon et al. 10.1016/j.catena.2020.104917
- Investigation and Quantification of Erosions in the Margins of Water Bodies: A Systematic Review V. de Souza Dias et al. 10.3390/w14111693
- Streambank Erosion Prediction A. Saadon et al. 10.1088/1755-1315/685/1/012007
- Downstream changes in riverbank sediment sources and the effect of catchment size G. Abbas et al. 10.1016/j.ejrh.2023.101340
- A random forest machine learning model to detect fluvial hazards M. Gava et al. 10.1002/rra.4353
- Long‐Term Landscape Changes in the Lake Tana Basin as Evidenced by Delta Development and Floodplain Aggradation in Ethiopia M. Abate et al. 10.1002/ldr.2648
- Assessing hydro-morphological changes in Mediterranean stream using curvilinear grid modeling approach - climate change impacts G. Morianou et al. 10.1007/s12145-017-0326-2
- Soil Erosion Susceptibility Mapping with the Application of Logistic Regression and Artificial Neural Network T. Sarkar & M. Mishra 10.1007/s41651-018-0015-9
- An overview of riverbank erosion prediction techniques applied to the Mekong Delta, Vietnam N. Pham et al. 10.1051/e3sconf/202453501020
- Development of riverbank erosion rate predictor for natural channels using NARX-QR Factorization model: a case study of Sg. Bernam, Selangor, Malaysia A. Saadon et al. 10.1007/s00521-020-04835-5
- River flow and sediment transport simulation based on a curvilinear and rectilinear grid modelling approach – a comparison study G. Morianou et al. 10.2166/ws.2017.031
- A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes H. Moayedi et al. 10.1080/19942060.2023.2300456
- Nonlinear multi independent variables in quantifying river bank erosion using Neural Network AutoRegressive eXogenous (NNARX) model A. Saadon et al. 10.1016/j.heliyon.2024.e26252
- Indicators of riverbank Erosion vulnerability assessment: A systematic literature review for future research N. Sultana & S. Paul 10.1016/j.hydres.2024.06.002
- Staying despite riverbank erosion: evidence of coastal Bangladesh A. Mallick & B. Mallick 10.1007/s43545-021-00104-x
- Identifying river bank erosion potential zones through geo-spatial and binary logistic regression modeling approach: a case study of river Ganga in Malda district (India) D. Ghosh & S. Saha 10.1007/s40808-023-01740-3
16 citations as recorded by crossref.
- Predictive models for the estimation of riverbank erosion rates A. Saadon et al. 10.1016/j.catena.2020.104917
- Investigation and Quantification of Erosions in the Margins of Water Bodies: A Systematic Review V. de Souza Dias et al. 10.3390/w14111693
- Streambank Erosion Prediction A. Saadon et al. 10.1088/1755-1315/685/1/012007
- Downstream changes in riverbank sediment sources and the effect of catchment size G. Abbas et al. 10.1016/j.ejrh.2023.101340
- A random forest machine learning model to detect fluvial hazards M. Gava et al. 10.1002/rra.4353
- Long‐Term Landscape Changes in the Lake Tana Basin as Evidenced by Delta Development and Floodplain Aggradation in Ethiopia M. Abate et al. 10.1002/ldr.2648
- Assessing hydro-morphological changes in Mediterranean stream using curvilinear grid modeling approach - climate change impacts G. Morianou et al. 10.1007/s12145-017-0326-2
- Soil Erosion Susceptibility Mapping with the Application of Logistic Regression and Artificial Neural Network T. Sarkar & M. Mishra 10.1007/s41651-018-0015-9
- An overview of riverbank erosion prediction techniques applied to the Mekong Delta, Vietnam N. Pham et al. 10.1051/e3sconf/202453501020
- Development of riverbank erosion rate predictor for natural channels using NARX-QR Factorization model: a case study of Sg. Bernam, Selangor, Malaysia A. Saadon et al. 10.1007/s00521-020-04835-5
- River flow and sediment transport simulation based on a curvilinear and rectilinear grid modelling approach – a comparison study G. Morianou et al. 10.2166/ws.2017.031
- A novel problem-solving method by multi-computational optimisation of artificial neural network for modelling and prediction of the flow erosion processes H. Moayedi et al. 10.1080/19942060.2023.2300456
- Nonlinear multi independent variables in quantifying river bank erosion using Neural Network AutoRegressive eXogenous (NNARX) model A. Saadon et al. 10.1016/j.heliyon.2024.e26252
- Indicators of riverbank Erosion vulnerability assessment: A systematic literature review for future research N. Sultana & S. Paul 10.1016/j.hydres.2024.06.002
- Staying despite riverbank erosion: evidence of coastal Bangladesh A. Mallick & B. Mallick 10.1007/s43545-021-00104-x
- Identifying river bank erosion potential zones through geo-spatial and binary logistic regression modeling approach: a case study of river Ganga in Malda district (India) D. Ghosh & S. Saha 10.1007/s40808-023-01740-3
Saved (final revised paper)
Latest update: 23 Nov 2024
Short summary
A statistical methodology is proposed to predict the probability of presence or absence of erosion in a river section considering locally spatial correlated independent variables.
The proposed tool is easy to use and accurate and can be applied to any region and river. It requires information from easy-to-determine geomorphological and/or hydrological variables to provide the vulnerable locations. This tool could be used to assist in managing erosion and flooding events.
A statistical methodology is proposed to predict the probability of presence or absence of...
