# Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches

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## Abstract

**:**

_{a}), water inflow (I

_{w}), minimum water reservoir level (L

_{r}), and storage capacity of the reservoir (C

_{r}) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of R

_{a}, I

_{w}, L

_{r}, and C

_{r}. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment.

## 1. Introduction

^{2}catchment area of Tarbela Dam, one of the largest earth-filled dams in the world, for irrigation and power generation. It is situated on the Indus River and is 96.6 km long [2,3]. Its initial storage capacity has dropped by 41.2%, according to the annual sedimentation report of the Water and Power Development Authority (WAPDA) of Pakistan [4]. Since it was first commissioned, several sedimentation estimation studies have been performed by consultants using sediment rating curves (SRC) [5].

## 2. Literature Review

_{2}concentration on sediment yield in a small rural catchment located in NW Spain. The river basin has been explored along with the key findings of the hydrological analysis. For the analysis of the flood event using the suggested methodology, the photographic sampling technique and assessment of the sediment size distribution have been presented by Di Francesco et al. [25]. Xiao et al. [26] used the location-weighted landscape contrast index (LCI), which is based on the “source-sink” hypothesis, with this, it has been possible to determine how the sediment yield in the Poyang Lake drainage basin responds to changes in plant cover. The diurnal change of the suspended sediment content in the East China Sea, with an emphasis on Hangzhou Bay, has been studied by Yang et al. [27] using a coupled hydrodynamic-ecological model for regional and shelf seas (COHERENS).

- To find the amount of sediment deposited inside the Tarbela reservoir using the proposed artificial neural network model and the multivariate regression model considering four yearly influencing factors: R
_{a}, I_{w}, L_{r}, and C_{r}. - To make future predictions of the sedimentation volume inside the Tarbela reservoir using trained ANN based on the time series univariate forecasting model ETS forecasted values of R
_{a}, I_{w}, L_{r}, and C_{r}.

## 3. Materials and Methods

#### 3.1. Study Areas and Data Collection

_{a}), water inflow (I

_{w}), minimum reservoir level (L

_{r}), and reservoir storage capacity (C

_{r}) against the actual sedimentation volume (S

_{v}) deposited inside the Tarbela reservoir shown in blue on the secondary y-axis. The straight lines show the linear trends along with the equations of lines illustrating the relationship between influencing parameters and the actual volume of sedimentation retained inside the Tarbela reservoir. It can be seen that the parameters are correlated, and their regression analysis is performed in the subsequent section.

#### 3.2. Model Development

_{min}is the minimum value in the data, and X

_{max}is the maximum of the data. Equation (2) describes the tangent-sigmoid activation function employed in the neural network models considered in this study. After the first layer, each neuron’s output is estimated using Equation (3).

^{(i,L)}is the weight of the ith neuron in the Lth layer of the network, b

^{(i,L)}represents the bias of the corresponding neuron, and a

^{(i,L−1)}represents the signal of the previous layer’s neuron. The activation function is represented by σ.

#### 3.3. Experimental Protocols and Performance Evaluation Measures

_{i}-h

_{1}-h

_{o}, Nh

_{i}-h

_{1}-h

_{2}-h

_{o}, and Nh

_{i}-h

_{1}-h

_{2}-h

_{3}-h

_{o}were constructed for training purposes. Where N stands for the ANN architecture, h

_{i}, h

_{1}, h

_{2}, h

_{3}, and h

_{o}represent the number of neurons in the input layer, in the first hidden layer, in the second hidden layer, in the third hidden layer, and in the output layer, respectively. Different training functions are utilized for the training of proposed neural network architectures.

^{−6}. Based on the prediction value, the exact value (y) supplied using Equation (4), the MSE was determined. Regarding weights and biases, the mean squared error was reduced to a minimum. The biases and weights that were improved were saved for use in later simulations.

## 4. Results and Discussion

_{a}), water inflow only (I

_{w}), minimum water level only (L

_{r}), capacity of the reservoir only (C

_{r}), and different combinations of these variables and all variables in multivariate regression. The equations obtained after this regression analysis are given in Table 2.

## 5. Conclusions

_{a}), water inflow (I

_{w}), minimum level of reservoir (L

_{r}), and storage capacity of the reservoir (C

_{r}). It was determined that with the increase in the number of influencing parameters, the accuracy of the sediment deposition prediction is increased. Secondly, different neural network architectures with various training functions were employed. It was found that the typical N4-45-45-1 neural network architecture with the resilient propagation training function performs better with a minimum error of 3.01% compared to all other architectures and training functions. The proposed neural network model was then applied for validation and future prediction purposes. It was found that the four influencing parameters could be used to accurately estimate the amount of sediment deposited inside the Tarbela reservoir. The maximum error for the proposed neural network model was found to be 4.01%, whereas, in the case of the multivariate regression model, it was found to be 60.7%, leading to the conclusion that the proposed neural network model approximated the amount of sediment deposited more accurately than the multivariate regression model. Finally, the Tarbela reservoir’s sedimentation was predicted using forecasted data of 20 years for four input parameters from the ETS model. It was found that the predictions are in good agreement with the actual sediment deposition determined by WAPDA for the years 2013 to 2019. It was also concluded using Olden’s technique that the Tarbela’s storage capacity is the most important influencing parameter in determining the amount of sedimentation inside of it, whilst annual rainfall was found to be less significant in influencing the amount of sediment deposited.

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

ANN | Artificial Neural Network |

ETS | Error, Trend, Seasonal |

MT | Million Tonnes |

MST | Million Short Tons |

MSE | Mean Squared Error |

MAE | Mean Absolute Error |

NSE | Nash–Sutcliffe Efficiency |

R_{a} | Annual rainfall |

I_{w} | Water inflow annually |

L_{R} | The minimum water level in the reservoir |

C_{r} | Capacity of reservoir |

S_{R} | Amount of sediment deposited annually |

Nh_{i}-h_{1}-h_{o} | N stands for ANN architecture, h _{i} = number of neurons in the input layerh _{1} = number of neurons in the hidden layerh _{o} = number of neurons in the output layer |

Nh_{i}-h_{1}-h_{2}-h_{o} | N stands for ANN architecture, h _{i} = number of neurons in the input layerh _{1} = number of neurons in the first hidden layerh _{2} = number of neurons in the second hidden layerh _{o} = number of neurons in the output layer |

Nh_{i}-h_{1}-h_{2}-h_{3}-h_{o} | N stands for ANN architecture h _{i} = number of neurons in the input layerh _{1} = number of neurons in the first hidden layerh _{2} = number of neurons in the second hidden layerh _{3} = number of neurons in the third hidden layerh _{o} = number of neurons in the output layer |

R | Correlation Coefficient |

R.E. | Relative Error |

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**Figure 1.**Schematic flow procedure for the pre−processing of data and implementation of algorithms for the validation and prediction of sediments retained inside the reservoir.

**Figure 2.**Location map of the Tarbela Dam and the Reservoir confirmed from Indus River System Authority along with the other rivers located near the distance of 200 km.

**Figure 3.**Time series plot of yearly basis influencing parameters including rainfall, water inflow, water level, and the storage capacity of the reservoir used for the estimation of sediments retained inside the Tarbela reservoir from year 1985 to year 2012.

**Figure 4.**Typical neural network architecture (N4-45-45-1) describing four layers including one input layer, one output layer, and two hidden layers. Four neurons in the input layer, 45 neurons in the first hidden layer, 45 neurons in the second hidden layer, and one neuron in the output layer are also indicated.

**Figure 5.**82% of the data used for training and three different data sets (18% each) used for the validation of trained ANN model considering a randomly selected five-year data set, initial five-year data set, and last five-year data set.

**Figure 6.**Comparing ANN model with multivariate regression model to calculate the sedimentation amount deposited inside the Tarbela reservoir using only one yearly basis input parameter of (

**A**) rainfall, (

**B**) inflow of water, (

**C**) minimum reservoir level, and (

**D**) storage capacity.

**Figure 7.**Comparing ANN model with multivariate regression model to compute the amount of sediment in the Tarbela reservoir using only two yearly basis input parameters: (

**A**) rainfall and water inflow; (

**B**) water inflow and minimum reservoir level; (

**C**) minimum reservoir level and storage capacity; and (

**D**) storage capacity and water inflow.

**Figure 8.**Comparing ANN model with multivariate regression model to compute the Tarbela reservoir sediment deposition with the combination of yearly basis three and four two input parameters: (

**A**) rainfall, reservoir minimum level, and storage capacity; (

**B**) inflow of water, reservoir minimum level, and reservoir storage capacity; (

**C**) rainfall, inflow of water, and reservoir minimum level; and (

**D**) rainfall, inflow of water, minimum reservoir level, and storage capacity.

**Figure 9.**Selection of typical neural network architecture (N4-45-45-1) on the basis of percentage error computed for the validation data set using different ANN architectures.

**Figure 10.**Selection of training function on the basis of percentage error computed for the validation data set using different sets of training functions.

**Figure 11.**(

**A**) Comparison of actual volume of sediments deposited inside the Tarbela quoted by WAPDA (in green color), obtained by multiple regression model (in blue color) and the proposed ANN model (in gold color) using randomly selected data for 5 years (

**B**) Reduction in relative error of proposed ANN model (in gold color) in comparison with the multiple regression model (in blue color) using randomly selected data for 5 years.

**Figure 12.**(

**A**) Comparison of actual volume of sediments deposited inside the Tarbela quoted by WAPDA (in green color), obtained by multiple regression model (in blue color) and the proposed ANN model (in gold color) using data for initial 5 years (

**B**) Reduction in relative error of proposed ANN model (in gold color) in comparison with the multiple regression model (in blue color) using data for initial 5 years.

**Figure 13.**(

**A**) Comparison of actual volume of sediments deposited inside the Tarbela quoted by WAPDA (in green color), obtained by multiple regression model (in blue color) and the proposed ANN model (in gold color) using data for last 5 years (

**B**) Reduction in relative error of proposed ANN model (in gold color) in comparison with the multiple regression model (in blue color) using data for last 5 years.

**Figure 14.**Taylor Diagram for the evaluation of model performance, where blue dotted lines show the Pearson’s correlation coefficient (R), black dotted arcs represent the standard deviation and green dashed arcs show the root mean squared difference. Red dots show which model is closer to the reference WAPDA data and performing better.

**Figure 15.**Forecasting of yearly basis influential factors for the Tarbela reservoir: (

**A**) normalized values of rainfall; (

**B**) normalized inflow of water; (

**C**) normalized minimum level of reservoir; (

**D**) normalized storage capacity.

**Figure 16.**Sedimentation prediction for next 20 years using proposed ANN model based on the four forecasted input parameters for the Tarbela reservoir.

**Figure 17.**The relative significance of influencing parameters in predicting the sedimentation amount deposited inside the Tarbela reservoir assessed using Olden’s algorithm of connection weights.

Parameter | Value |
---|---|

Batch size | 100 |

Learning rate | 0.001 |

The number of hidden layers | 2 |

The number of neurons at kth hidden layer | 45 |

The number of neurons at input layer | 4 |

The number of neurons at output layer | 1 |

Activation function | Sigmoid |

Training Function | trainrp |

Optimizer | Adam |

Epoch | 20 |

Regularization | L1 (Lasso regression) |

Problem type | Time Series (Sequential) |

Ratio of training to test data (%) | 82:18 |

**Table 2.**Constants evaluated from multivariate regression analysis performed using various yearly basis inputs like rainfall only (R

_{a}), water inflow only (I

_{w}), minimum water level only (L

_{r}), capacity of the reservoir only (C

_{r}), and different combinations of these variables and all variables in multivariate regression.

Variables | p | q | r | s | Intercept |
---|---|---|---|---|---|

R_{a} only | 0.073212 | 0 | 0 | 0 | 84.17088 |

I_{w} only | 0 | 0.00522 | 0 | 0 | −218.58 |

L_{R} only | 0 | 0 | −0.10758 | 0 | 224.7589 |

C_{r} only | 0 | 0 | 0 | −0.00339 | 217.077 |

R_{a} and I_{w} | 0.00575 | −0.04186 | 0 | 0 | −199.299 |

I_{w} and L_{r} | 0 | 0.005683 | −1.32448 | 0 | 299.2509 |

L_{r} and C_{r} | 0 | 0 | −0.12451 | −0.00345 | 270.0721 |

I_{w} and C_{r} | 0 | 0.005295 | 0 | −0.00513 | −162.88 |

R_{a}, I_{w}, and L_{r} | −0.04103 | 0.006149 | 1.3160 | 0 | 314.409 |

R_{a}, L_{r}, and C_{r} | 0.0977 | 0 | 0.42128 | 0.01152 | 354.270 |

I_{w}, L_{r}, and C_{r} | 0 | 0.005721 | 1.36206 | 0.00601 | 378.5874 |

All Parameters | −0.033 | 0.6 | −1.34 | −0.34 | 356.96 |

**Table 3.**Performance metrics of the proposed neural network architecture for randomly selected data for 5 years and data selected for first and last 5 years.

Performance Metrics | Randomly Selected 5 Years Data Set | Initial 5 Years Data Set | Last 5 Years Data Set |
---|---|---|---|

MSE | 0.000529 | 0.00539 | 0.000166 |

MAE | 0.017604 | 0.019629 | 0.015607 |

R-Training | 0.99928 | 0.997 | 1 |

R-Validation | 0.99823 | 0.991 | 0.99902 |

NSE | 0.99436 | 0.98271 | 0.99965 |

Minimum Gradient | 9.79 × 10^{−7} | 9.18 × 10^{−7} | 9.97 × 10^{−7} |

**Table 4.**Comparison of results obtained by proposed ANN model with the actual amount of sediment quoted by WAPDA and obtained by multiple regression model using the randomly selected data set of 5 years.

Year | Randomly Selected 5 Years Data Set | Initial 5 Years Data Set [R.E. (%)] | Last 5 Years Data Set [R.E. (%)] |
---|---|---|---|

1987 | 111.25 | 155.45 [39.73] | 113.58 [2.10] |

1997 | 178.93 | 138.39 [22.66] | 178.36 [0.32] |

2002 | 176 | 133.84 [23.95] | 174.74 [0.71] |

2007 | 225 | 144.56 [35.74] | 231.76 [3.01] |

2012 | 114.01 | 121.29 [6.39] | 111.98 [1.77] |

**Table 5.**Comparison of results obtained by proposed ANN model with the actual amount of sediment quoted by WAPDA and obtained by multiple regression model using the data set of initial 5 years.

Year | Randomly Selected 5 Years Data Set | Initial 5 Years Data Set [R.E. (%)] | Last 5 Years Data Set [R.E. (%)] |
---|---|---|---|

1985 | 149.72 | 127.88 [14.59] | 150.39 [0.44] |

1986 | 163.21 | 157.49 [3.5] | 159.43 [2.31] |

1987 | 111.25 | 155.45 [39.73] | 116.16 [4.41] |

1988 | 189.28 | 257.32 [35.94] | 190 [0.38] |

1989 | 136.93 | 143.55 [4.84] | 132.56 [3.18] |

**Table 6.**Comparison of results obtained by proposed ANN model with the actual amount of sediment quoted by WAPDA and obtained by multiple regression model using the data set of last 5 years.

Year | WAPDA Data (MST) | Regression Model (MST) [R.E. (%)] | ANN Model (MST) [R.E. (%)] |
---|---|---|---|

2008 | 114.71 | 146.84 [36.04] | 112.95 [1.54] |

2009 | 69.52 | 163.93 [60.7] | 69.71 [0.27] |

2010 | 361.147 | 252.41 [20.34] | 362.75 [0.44] |

2011 | 173.8 | 157.29 [48.24] | 174.6 [0.46] |

2012 | 114.01 | 121.29 [17.44] | 110.6 [2.99] |

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## Share and Cite

**MDPI and ACS Style**

Hassan, S.; Shaukat, N.; Ahmad, A.; Abid, M.; Hashmi, A.; Shahid, M.L.U.R.; Rajabi, Z.; Tariq, M.A.U.R.
Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches. *Water* **2022**, *14*, 3098.
https://doi.org/10.3390/w14193098

**AMA Style**

Hassan S, Shaukat N, Ahmad A, Abid M, Hashmi A, Shahid MLUR, Rajabi Z, Tariq MAUR.
Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches. *Water*. 2022; 14(19):3098.
https://doi.org/10.3390/w14193098

**Chicago/Turabian Style**

Hassan, Shahzal, Nadeem Shaukat, Ammar Ahmad, Muhammad Abid, Abrar Hashmi, Muhammad Laiq Ur Rahman Shahid, Zohreh Rajabi, and Muhammad Atiq Ur Rehman Tariq.
2022. "Prediction of the Amount of Sediment Deposition in Tarbela Reservoir Using Machine Learning Approaches" *Water* 14, no. 19: 3098.
https://doi.org/10.3390/w14193098