Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia

Hussen Ali Hassen; Yonatan Tibebu; Dagemawi Negashe; Mehret Ayana; Fikru Fentaw Abera

doi:doi:10.11648/j.ijiis.20231204.12

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Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia

Hussen Ali Hassen^*, Yonatan Tibebu, Dagemawi Negashe, Mehret Ayana, Fikru Fentaw Abera

Published in International Journal of Intelligent Information Systems (Volume 12, Issue 4)

Received: 27 November 2023 Accepted: 19 December 2023 Published: 28 December 2023

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Abstract

Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for modeling runoff and sediment yield of Maybar watershed, Awash River basin. The ANN model was trained and cross validated using MATLAB, supported by the NN toolbox package. The main input for the ANN model was selected using correlation results from Statistical Packages for Social Science (SPSS). Present rainfall and previous one-day runoff up to four days of runoff were selected as inputs for runoff modeling, and present rainfall, present runoff, and previous one-day runoff were selected as inputs for sediment yield modeling. The proposed model was developed, trained, and cross validated by considering 7 years of data (2010–2016) for model training and 2 years of data for model testing (cross-validation), and their performance was evaluated using performance indicators (R², RMSE, and NSE). Adding lag of runoff as input results increase the model efficiency during training. Of the five proposed ANN runoff models, model B (2 inputs, 3 hidden neurons, 1 output) performed better than the other proposed runoff models. Similarly, of the three proposed ANN sediment models, model III (3 inputs, 6 hidden neurons, 1 output) performed better than the other proposed sediment models. In general, the ANN model was applicable for predicting runoff and sediment in the Maybar watershed in daily time steps.

Published in	International Journal of Intelligent Information Systems (Volume 12, Issue 4)
DOI	10.11648/j.ijiis.20231204.12
Page(s)	63-75
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Artificial Neural Networks, Runoff Modeling, Sediment Yield Modeling, Maybar Watershed

References

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APA Style

Hassen, H. A., Tibebu, Y., Negashe, D., Ayana, M., Abera, F. F. (2023). Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia. International Journal of Intelligent Information Systems, 12(4), 63-75. https://doi.org/10.11648/j.ijiis.20231204.12

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ACS Style

Hassen, H. A.; Tibebu, Y.; Negashe, D.; Ayana, M.; Abera, F. F. Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia. Int. J. Intell. Inf. Syst. 2023, 12(4), 63-75. doi: 10.11648/j.ijiis.20231204.12

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AMA Style

Hassen HA, Tibebu Y, Negashe D, Ayana M, Abera FF. Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia. Int J Intell Inf Syst. 2023;12(4):63-75. doi: 10.11648/j.ijiis.20231204.12

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@article{10.11648/j.ijiis.20231204.12,
  author = {Hussen Ali Hassen and Yonatan Tibebu and Dagemawi Negashe and Mehret Ayana and Fikru Fentaw Abera},
  title = {Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia},
  journal = {International Journal of Intelligent Information Systems},
  volume = {12},
  number = {4},
  pages = {63-75},
  doi = {10.11648/j.ijiis.20231204.12},
  url = {https://doi.org/10.11648/j.ijiis.20231204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20231204.12},
  abstract = {Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for modeling runoff and sediment yield of Maybar watershed, Awash River basin. The ANN model was trained and cross validated using MATLAB, supported by the NN toolbox package. The main input for the ANN model was selected using correlation results from Statistical Packages for Social Science (SPSS). Present rainfall and previous one-day runoff up to four days of runoff were selected as inputs for runoff modeling, and present rainfall, present runoff, and previous one-day runoff were selected as inputs for sediment yield modeling. The proposed model was developed, trained, and cross validated by considering 7 years of data (2010–2016) for model training and 2 years of data for model testing (cross-validation), and their performance was evaluated using performance indicators (R2, RMSE, and NSE). Adding lag of runoff as input results increase the model efficiency during training. Of the five proposed ANN runoff models, model B (2 inputs, 3 hidden neurons, 1 output) performed better than the other proposed runoff models. Similarly, of the three proposed ANN sediment models, model III (3 inputs, 6 hidden neurons, 1 output) performed better than the other proposed sediment models. In general, the ANN model was applicable for predicting runoff and sediment in the Maybar watershed in daily time steps.
},
 year = {2023}
}

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TY  - JOUR
T1  - Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia
AU  - Hussen Ali Hassen
AU  - Yonatan Tibebu
AU  - Dagemawi Negashe
AU  - Mehret Ayana
AU  - Fikru Fentaw Abera
Y1  - 2023/12/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijiis.20231204.12
DO  - 10.11648/j.ijiis.20231204.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 63
EP  - 75
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20231204.12
AB  - Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for modeling runoff and sediment yield of Maybar watershed, Awash River basin. The ANN model was trained and cross validated using MATLAB, supported by the NN toolbox package. The main input for the ANN model was selected using correlation results from Statistical Packages for Social Science (SPSS). Present rainfall and previous one-day runoff up to four days of runoff were selected as inputs for runoff modeling, and present rainfall, present runoff, and previous one-day runoff were selected as inputs for sediment yield modeling. The proposed model was developed, trained, and cross validated by considering 7 years of data (2010–2016) for model training and 2 years of data for model testing (cross-validation), and their performance was evaluated using performance indicators (R2, RMSE, and NSE). Adding lag of runoff as input results increase the model efficiency during training. Of the five proposed ANN runoff models, model B (2 inputs, 3 hidden neurons, 1 output) performed better than the other proposed runoff models. Similarly, of the three proposed ANN sediment models, model III (3 inputs, 6 hidden neurons, 1 output) performed better than the other proposed sediment models. In general, the ANN model was applicable for predicting runoff and sediment in the Maybar watershed in daily time steps.

VL  - 12
IS  - 4
ER  -

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Author Information

Hussen Ali Hassen

Department of Hydraulic and Water Resources Engineering, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia

Contact Email
Yonatan Tibebu

Department of Hydraulic and Water Resources Engineering, Woldiya University, Woldiya, Ethiopia
Dagemawi Negashe

Department of Hydraulic and Water Resources Engineering, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia
Mehret Ayana

Department of Hydraulic and Water Resources Engineering, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia
Fikru Fentaw Abera

Department of Hydraulic and Water Resources Engineering, Kombolcha Institute of Technology, Wollo University, Kombolcha, Ethiopia

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APA Style

Hassen, H. A., Tibebu, Y., Negashe, D., Ayana, M., Abera, F. F. (2023). Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia. International Journal of Intelligent Information Systems, 12(4), 63-75. https://doi.org/10.11648/j.ijiis.20231204.12

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ACS Style

Hassen, H. A.; Tibebu, Y.; Negashe, D.; Ayana, M.; Abera, F. F. Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia. Int. J. Intell. Inf. Syst. 2023, 12(4), 63-75. doi: 10.11648/j.ijiis.20231204.12

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AMA Style

Hassen HA, Tibebu Y, Negashe D, Ayana M, Abera FF. Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia. Int J Intell Inf Syst. 2023;12(4):63-75. doi: 10.11648/j.ijiis.20231204.12

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@article{10.11648/j.ijiis.20231204.12,
  author = {Hussen Ali Hassen and Yonatan Tibebu and Dagemawi Negashe and Mehret Ayana and Fikru Fentaw Abera},
  title = {Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia},
  journal = {International Journal of Intelligent Information Systems},
  volume = {12},
  number = {4},
  pages = {63-75},
  doi = {10.11648/j.ijiis.20231204.12},
  url = {https://doi.org/10.11648/j.ijiis.20231204.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20231204.12},
  abstract = {Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for modeling runoff and sediment yield of Maybar watershed, Awash River basin. The ANN model was trained and cross validated using MATLAB, supported by the NN toolbox package. The main input for the ANN model was selected using correlation results from Statistical Packages for Social Science (SPSS). Present rainfall and previous one-day runoff up to four days of runoff were selected as inputs for runoff modeling, and present rainfall, present runoff, and previous one-day runoff were selected as inputs for sediment yield modeling. The proposed model was developed, trained, and cross validated by considering 7 years of data (2010–2016) for model training and 2 years of data for model testing (cross-validation), and their performance was evaluated using performance indicators (R2, RMSE, and NSE). Adding lag of runoff as input results increase the model efficiency during training. Of the five proposed ANN runoff models, model B (2 inputs, 3 hidden neurons, 1 output) performed better than the other proposed runoff models. Similarly, of the three proposed ANN sediment models, model III (3 inputs, 6 hidden neurons, 1 output) performed better than the other proposed sediment models. In general, the ANN model was applicable for predicting runoff and sediment in the Maybar watershed in daily time steps.
},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Artificial Neural Network Based Runoff and Sediment Yield Modeling of Maybar Watershed, Awash Basin, Ethiopia
AU  - Hussen Ali Hassen
AU  - Yonatan Tibebu
AU  - Dagemawi Negashe
AU  - Mehret Ayana
AU  - Fikru Fentaw Abera
Y1  - 2023/12/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijiis.20231204.12
DO  - 10.11648/j.ijiis.20231204.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 63
EP  - 75
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20231204.12
AB  - Runoff and sediment are important parameters to be understood and predict for managing land and water resource. So, understanding the dynamic process and prediction of the existing process by selecting suitable hydrological model is very essential. This study aims to test and evaluate the application of an artificial neural network (ANN) model for modeling runoff and sediment yield of Maybar watershed, Awash River basin. The ANN model was trained and cross validated using MATLAB, supported by the NN toolbox package. The main input for the ANN model was selected using correlation results from Statistical Packages for Social Science (SPSS). Present rainfall and previous one-day runoff up to four days of runoff were selected as inputs for runoff modeling, and present rainfall, present runoff, and previous one-day runoff were selected as inputs for sediment yield modeling. The proposed model was developed, trained, and cross validated by considering 7 years of data (2010–2016) for model training and 2 years of data for model testing (cross-validation), and their performance was evaluated using performance indicators (R2, RMSE, and NSE). Adding lag of runoff as input results increase the model efficiency during training. Of the five proposed ANN runoff models, model B (2 inputs, 3 hidden neurons, 1 output) performed better than the other proposed runoff models. Similarly, of the three proposed ANN sediment models, model III (3 inputs, 6 hidden neurons, 1 output) performed better than the other proposed sediment models. In general, the ANN model was applicable for predicting runoff and sediment in the Maybar watershed in daily time steps.

VL  - 12
IS  - 4
ER  -

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