Recovering the Salinity Distributed Sources Into River from Aquifer Using the Simulation-Optimization Method Fatemeh Yousofvand

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Online ISSN: 2382-9931

Journal of Water and Irrigation Management

Homepage: https://jwim.ut.ac.ir/

University of Tehran Press

Recovering the Salinity Distributed Sources Into River from Aquifer Using the Simulation-Optimization Method

Fatemeh Yousofvand¹ | Jamal Mohammad Vali Samani²^ | Hossein Mohammad Vali Samani³ | Mehdi Mazaheri⁴

1. Engineering and Water Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. E-mail: [email protected]

2. Corresponding Author, Engineering and Water Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. E-mail: [email protected]

3. Civil Engineering Department, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Iran. E-mail: [email protected]

4. Engineering and Water Management Department, Faculty of Agriculture, Tarbiat Modares University, Tehran, Iran. E-mail: [email protected]

Article Info ABSTRACT

Article type:

Research Article

Article history:

Received: 30 January 2023 Received in revised form:

3 April 2023

Accepted: 6 June 2023 Published online: 2 July 2023

Keywords:

Advection-Dispersion Equation,

Backward Model, Forward Model, Genetic Algorithm, MIKE11 Software, Numerical Simulation.

Due to the increase in population and the need for water supply, preservation and protection of surface water and groundwater resources has been considered by governments. One of the pollutant sources in rivers is entering salinity from groundwater into the river, that in this research is considered as distributed (non- point) sources. The goal is to identify the salinity intensity, location and length of sources by measuring the temporal distribution of concentration in one observation point. For this purpose, the inverse solution of advection-dispersion equation in the river was employed using the simulation-optimization approach. MIKE11 numerical model was used to simulate flow and transfer of salinity in the river, and genetic algorithm was employed for optimization. In the proposed model, considering only one observation point with some measured intensity data for recovering several sources, unknown location and length of the sources, in addition to their intensities is the most significant advantage of the present study.

The model verified by using hypothetical examples, 40 km section of the Karun River and also by applying five and 15 percent noise to the observation data. The results confirm the ability of the model to recover the specifications of several distributed sources using only one observation point. With five percent of noise in the observation data, all three specifications of sources can be recovered with the desired accuracy. While at 15 percent of noise, the accuracy of the model in recovering the location and length of sources was decreased. Also, to recover the specifications of each source, employing only three points of the measured data in the ascending part are sufficient.

Cite this article: Yousofvand, F., Mohammad Vali Samani, J., Mohammad Vali Samani, H., & Mazaheri, M. (2023).

Recovering the Salinity Distributed Sources Into River from Aquifer Using the Simulation-Optimization Method. Journal of Water and Irrigation Management, 13 (2), 471-486. DOI: https://doi.org/10.22059/jwim.2023.355061.1051

DOI: https://doi.org/ 10.22059/jwim.2023.355061.1051

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Figure 1. Location of distributed source (S1) and observation point (p)

(9)

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Figure 2. Measured concentration versus time at observation point

Figure 3. Measured (exact) and computed concentration versus time at observation point (p=6.5 km).

Table 1. Exact and computed specifications of S1

REL (%) REW (%)

L (m) End chainage (m)

Start chainage (m) W (kg/s)

- -

1000 3000

2000 15

Exact

0.001 0.0001

1000.01 3000.01

1999.99 14.99

Computed

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C (mg/l)

T (hr)

exact

0 5 10 15 20 25 30 35 40 45 50 55 60

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

C (mg/l)

T (hr)

exact computed

(10)

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Table 2. Computed specifications of S1 using different numbers of data

REL

(%) REW

(%) L

(m) End

chainage (m) Start

chainage (m) W (kg/s) Explanation

180.19 356.84 2801.91 2975.42

155.51 68.53

Using the first 3 data (1 non-zero data) 1

188.52 323.98 2885.2 2905.9

20.7 63.6

Using the first 4 data 2

3.76 0.5 1037.57 3037.59

2000.02 15.08

0.18 1.2 1012.04 3000.05

1988.01 15.03

1.15 0.07 1011.49 3004.53

1993.05 15.01

0.004 0.06 1000.04 3000.01

1999.98 15.008

0.003 0.05 1000.04 3000.04

2000.001 15.007

0.003 0.04 1000.03 3000.03

2000.002 15.005

0.002 0.01 1000.02 3000.02

2000.002 15.002

0.001 0.004 1000.01 3000.01

1999.99 14.99

Using all 25 data 10

0.12 0.1 1001.02 3000.4

1999.38 15.02

Using data from 5 to 7 (located on the ascending part of the graph) 11

57.23 0.001 1572.33 6079.39

4507.06 14.99

Using data from 11 to 13 (located at the end of the graph) 12

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(11)

Figure 4. Location of distributed sources (S1 and S2) and observation point (p)

Table 3. The specifications of S1 and S2

L(m) End chainage(m)

Start chainage(m) W (kg/s)

Source Number

1500 3000

1500 30

S1

2000 13000

11000 52.5

S2

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Figure 5. Concentration versus time at observation point; with 0, 5 and 15 percent of noise

0 30 60 90 120 150 180 210 240 270 300

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6

C (mg/l)

T (hr)

exact 5% Noise 15% Noise

(12)

Figure 6. Measured and calculated concentration versus time at the observation point (p=17 km); without noise

Table 4. Computed specifications of S1 and S2

REL (%) REw (%)

L(m) End chainage (m)

Start chainage (m) W (kg/s)

Source Number

0.006 0.008

1499.91 3000.13

1500.22 30.002

S1

0.001 0.004

1999.98 12999.99

11000.02 52.498

S2

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Table 5. Computed specifications of S1 and S2, with 0, 5 and 15 percent of noise and error indices REL(%) RMSEL(m)

REW(%) RMSEW(kg/s)

L(m) W (kg/s)

Source Number

- -

1500 30

Exact

S1 Noise (5%) 30.25 1551.44 0.26 0.85 51.44 3.43

26.67 400

4.28 1.29

1900 28.72

Noise(15%)

- -

2000 52.5

Exact

S2 Noise (5%) 52.41 1962.56 0.09 0.17 37.44 1.87

12.04 240.86

2.54 1.29

2240.87 53.79

Noise (15%)

3 - 3 . 546 7

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C (mg/l)

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(13)

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Figure 7. The picture of study area in Karun River and salinity source locations and measurement station

Figure 8. Concentration versus time at the measurement station (p=37.543 km) 0

100 200 300 400 500 600

1/1/90 0:00 1/1/90 4:00 1/1/90 8:00 1/1/90 12:00 1/1/90 16:00 1/1/90 20:00 1/2/90 0:00 1/2/90 4:00 1/2/90 8:00 1/2/90 12:00 1/2/90 16:00 1/2/90 20:00 1/3/90 0:00 1/3/90 4:00 1/3/90 8:00 1/3/90 12:00 1/3/90 16:00 1/3/90 20:00 1/4/90 0:00 1/4/90 4:00 1/4/90 8:00 1/4/90 12:00 1/4/90 16:00 1/4/90 20:00 1/5/90 0:00 1/5/90 4:00 1/5/90 8:00 1/5/90 12:00 1/5/90 16:00 1/5/90 20:00

C (mg/l)

T (hr)

exact