Application of Non-dominated Sorting Genetic Algorithm ...twc.bse.ntu.edu.tw/upload/ckfinder/files/68-1-86-99_com.pdf · and pollution: water released from near the bottom of a stratified

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Application of Non-dominated Sorting Genetic Algorithm (NSGA) for Optimization of Flushing Operation

Considering Water Quantity and Quality

非支配型排序基因演算法於水量水質於排砂運作優化之應用

Hamid Khakzad*

Department of Engineering,

RusPers Group Company,

Project Manager

ABSTRACT

Water quantity and quality are considered to be the main driving forces for Flushing operation in dam reservoirs. In this study, we consider the knapsack problem based Non-dominated Sorting Genetic Algorithm (NSGA) optimization algorithm for the assessment of planning operations of a dam reservoir. The knapsack problem plays a significant role in the study of non-convex programming in discrete mathematics, and that calls for the optimal assignment of a set of items, each having a profit and a weight. The presented sediment-flushing model is developed to estimate the amount of the flushed sediment volume, water volume and the length of time of needed to complete flushing operation flushing, which can be taken into account in the reservoir simulation model. A study was conducted on Dez Hydropower reservoir in Iran, which is currently facing serious sedimentation problems. Total volume of water passed per flush (m3) and total volume of sediment passed per flush (m3) for scenario 1 (for protecting eggs & early life stage salmonids) and for scenario 2 (protecting adult non-salmonids) with acceptable sediment concentration downstream of the spillway= 8,000, 10,000, 15,000 (mg/l), are calculated. The results from this study help us identify a solution to satisfy water demand, and determine the appropriate hydraulic conditions to decrease negative environmental effects of Dez dam flushing operation on the downstream.

Keywords: Sediment management in reservoir, Non-dominated Sorting Genetic Algorithm (NSGA), Ecological flow, Dez Hydropower reservoir.

摘　　　　　要

水量及水質為水庫排砂之兩大重要驅力。本研究將非支配型排序基因演算法運用於背包問題，計算並評估水庫之最佳設計及運作模式。背包問題為離散數學中非凸性程式研究之重要一環，每一項目皆具其利益及重量，使得項目最佳分配成為必要。本研究建構並呈現一沈澱物-排砂模型，可用以預估排出之泥沙容積、水容積，以及完成排砂所需時間，

* Corresponding author: Project Manager/ RusPers Group Company, Department of Engineering, 109263, Moscow, Russia/ No. 25, Artyukhina St. Moscow, Russia/ [email protected]

臺灣水利　第 68 卷　第 1 期

民國 109 年 3 月出版

Taiwan Water ConservancyVol. 68, No. 1, March 2020DOI: 10.6937/TWC.202003/PP_68(1).0008

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1. INTRODUCTION

Sediment deposition in reservoirs causes

loss of capacity, increases flood risks, degradation

of water quality, leads to difficulty in reservoir

operation and maintenance and consequently, raise

the overall associated cost. Besides this, sediment

storage can have a significant impact on ecosystem downstream of large river systems. Given the

scarcity of undeveloped new dam sites in watersheds

where extensive dam construction has already

been undertaken, it will be increasingly necessary

in the future to focus on storage preservation. The

effects of reservoir sedimentation can be mitigated

through implementation of reservoir sedimentation

management techniques (Nikolaos, 2017).

Flushing is one of the most economical

methods for recovering lost storage without

incurring the cost of dredging. Flushing involves the

remobilizing of deposited sediments by increasing

the flow velocity in the reservoir. The entrained

deposits are discharged into the downstream

of the reservoir through low-level outlets. The

flow velocity is increased by drawing down the

reservoir water level through a suitably designed

outlet structure. On the other hand, flushing also

releases large volumes of sediment downstream

creating potentially serious problems. Scouring of

polluted sediments from the reservoir threatens the

downstream water quality and ecology (Sloff, 1991).

Also, releasing high sediment concentration water

may have a great impact on the downstream biota

(Morris and Fan, 1997). This led to the development

of “environmentally friendly flushing” technique,

detailed in particular, by Fruchard (2008).

In this method the hypolimnic water from the

bottom outlet, which is high in suspended sediment,

mixes with the water from the mid depth outlet,

which is low in suspended sediment. Thus, the

flushing flow created during the event will have

much lower concentrations of suspended sediment

than would otherwise be the case. Actually this

technique also improves downstream oxygen

concentration and minimizes temperature changes

and pollution: water released from near the bottom

of a stratified reservoir is usually cold, oxygen-

depleted, and high in hydrogen sulfide and other

pollutants, whereas water released from a mid-depth

is more oxygenated, warmer and less concentrated

in heavy metals and pollutants (McCartney et al.,

2001).

Over the last 20 years, many researchers,

including those in the environmental resources

management, have embarked on the implementation

of a wide range of various evolutionary algorithms:

Genetic algorithms, Ant colony optimization,

Par t ic le swarm opt imizat ion (PSO), mul t i

objective evolutionary algorithm (EA) based

on decomposition (MOEA/D), Non-dominated

Sorting Genetic Algorithm (NSGA), Ant Colony

optimization (ACO), simulated annealing, shuffled complex evolution and harmony search among

many others (Katsifarakis, 2012). Chen et al,

(1998) use a GA with an aggregating approach

(a multiplication of the aspiration levels of each

objective) for a water quality control problem. Three

objectives are considered: maximize the assimilative

capacity of the river, minimize the treatment cost for

water pollution control and maximize the economic

value of the river flow corresponding to recreational

其皆可納入水庫模擬模型之計算。本研究於伊朗的德茲水力發電水庫進行實驗，該地當前正經歷嚴重的泥砂沈積問題。情境一(為保護鮭科魚類的卵及幼魚)以每次排砂排出的水及沈積物總容積(立方公尺)計算，情境二(保護非鮭科成魚)則以溢道下游可接受的沉積物濃度8000、10,000、15,000 (毫克/公升)計算之。本研究成果有助計畫因應供水需求之方案，並擬定適合的水利設計，降低德茲水庫排洪對於下游環境造成之負面影響。

關鍵詞：水庫泥沙管理，非主導分類遺傳算法，生態流量，德茲水電站。

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aspects (Coello, 2007). Srigiriraju (2000) uses

the Noninferior Surface Tracing Evolutionary

Algorithm (NSTEA) to solve an estuary water

quality management problem. Two objectives are

considered: minimize cost of BOD control, and

maximize equity with respect to levels of treatment

among the different dischargers. About 2005 more

scientific impetus was added to water management models by the use of environmental variables to

indicate global climatic changes and environmental

pollution levels (Uslaender, 2010). Additionally,

the inclusion of socio-economic relationships and

the evaluation of ecosystem services has gradually

become the focal point in environmental informatics

and management (Rauschenbach, 2016).

This study presents the principles of Non-

dominated Sorting Genetic Algorithm (NSGA),

which is one of the evolutionary algorithms that

has shown great potential for the solution of

various optimization problems. By developing the

necessary elements, we then provide an adaptation

of this algorithm for multi-objective Optimization

for Eco-Friendly flushing in reservoirs. Finally,

we demonstrated the results of application of the

proposed methodology to selected case-study.

2. MATERIAL AND METHOD

2.1 Study area and data collection

This s tudy was carr ied out a t the Dez

Reservoir, which is located in southern Iran. The

Dez Dam is a large hydroelectric dam in Iran, which

was built in 1963 by an Italian consortium. At the

time of construction, the Dez Dam was Iran’s largest

development project. The Dez Dam is a 203 m-high

double curvature arch dam, and the level of its crest

is 352 m above sea level. The original reservoir

volume was 3,315 × 106 m3, and the estimated

volume of arrival sediment was 840 × 106 m3 for

a 50-year period. The minimum and maximum

operating water levels of the reservoir are 300 m and

352 m from the sea level, respectively. Although the

project has been well-preserved, the project is now

more than 40 years old, reaching its midlife period.

The useful life of the Dez Reservoir is threatened

by a sediment delta, which is approaching the

dam’s intake tunnels. A hydrographic study in 2002

showed that sedimentation reduced useful storage

of the Dez Reservoir from 3,315.6 × 106 m3 to 2,700

× 106 m3 (19% reduction). The difference between

levels of the inlet of turbine and bed surface of

deposited sediment is 14 m, and the sedimentation

rate near the inlet of turbine is 2 m/year. Therefore,

sediment management in the Dez Reservoir is of

considerable importance (Khakzad, 2014).

A f ie ld measurement program for the

measurement of the turbidity currents in the

Dez Reservoir commenced in December 2002

and finished in June 2003 (Dezab Consulting

Engineers, 2004). The measuring was performed on

a daily basis. The program consisted of a series of

measurements at various depths and locations across

seven cross-sections. The measurement station

locations are shown in Fig. 1. RCM9 and Valeport

108 MK II were used to measure the current velocity

and direction, electrical conductivity, temperature,

and pressure, and their specifications can be found in Dezab Consulting Engineers in Association with

ACTRES International (2004).

The first four months of data gathering were

done by Valeport 108 MK II. Data gathering is a

direct reading. After the fourth month, a RCM9

instrument was used to collect data. In addition to

previously mentioned parameters, it can measure

water turbidity as well. Also, it is a self-recording

equipment. Figs. 2 to 5 show a sample of field

measurement records on turbidity currents at

A2, B3, C3, E, and F stations, collected on April

24, 2003, where the water level is 351 m, the

maximum depth is 94 m; the reservoir inflow is

1,210.6 m3/s, and the reservoir outflow is 590.8

m3/s (KWEO 2003). With help from these field

measurement records, we estimated a flow regime

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and an annual inflow of turbidity currents in Dez

dam reservoir, then we studied and reviewed the

various environmental issues that may influence

the recommended hydraulic conditions enacted to

decrease the negative environmental impacts of Dez

Dam flushing operation (Khakzad, 2014, 2015).

According to the field studies over the period

of December 2002 to April 2003, there were only

two significant turbidity currents. The measurements of velocity and suspended sediment concentration

at section A indicated that the first turbidity current occurred on January 28 and 29, 2003. The thickness

Fig. 1. Sketch of measurement stations.

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of the turbid layer was about 15 m. The flow

direction of the water near the surface was upstream,

but the velocity was low. It goes without saying that

large slow-moving eddies were present above the

turbidity current. The magnitude of velocity and

its direction changed with time. The fine sediment

moved into the reservoir on January 29, 2003,

reaching a volume of 44,000 m3.

The second turbidity current occurred on

April 23 and 24, 2003. It is difficult to quantify the turbidity load based on the available measurements.

However, on the basis of the average reservoir

inflow values, the turbidity load value for rainstorm events rose to 1,188,475 m3 over the two days. Table

1 shows the range and values of different parameters

used in this study. The median particle size of all

samples was less than 0.01 mm, with the median

particle size of sediments taken upstream in the

reservoir slightly larger than those closer to the dam.

2.2 Non-dominated Sorting Genetic Algorithms

(NSGA) for Knapsack Problems

The knapsack problem (KP) can be formally

defined as follows: we are given an instance of the knapsack problem with item set N, consisting of n

items j with profit Pj and weight Wj, and the capacity

value c. (Usually, all these values are taken from

the positive integer numbers.) Then the objective

is to select a subset of N such that the total profit

of the selected items is maximized and the total

weight does not exceed c. Alternatively, a knapsack

problem can be formulated as a solution of the

following linear integer programming formulation

(Kellerer, 2004):

Fig. 2. Temperature-dimensionless water depth profiles at different stations.

Fig. 3. Current speed-dimensionless water depth profiles at different stations.

Fig. 4. Current direction-dimensionless water depth profiles at different stations.

Fig. 5. Turbidity-dimensionless water depth profiles at different station.

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maximize ∑ j = 1 pj xj (1)

subject to ∑ j = 1 wj xj ≤ c (2)

xj ∈{0, 1}, j = 1, …, n. (3)

We will denote the optimal solution vector by

x* = (x1*, ..., xn*) and the optimal solution value

by z*. The set X* denotes the optimal solution set,

i.e. the set of items corresponding to the optimal

solution vector.

The knapsack problem has been studied

for centuries as it is the simplest prototype of a

maximization problem. The set of all the Pareto-

optimal solutions forms the tradeoff surface in the

objective space. This tradeoff surface is called the

Pareto front. EMO algorithms are usually designed

to search for a set of well-distributed non-dominated

solutions that approximates the entire Pareto

front very well. In recent years knapsack problem

turned out to be one of the favorite playgrounds

for experiments with metaheuristics, in particular

Particle Swarm Optimization, Non-dominated

Sorting Genetic Algorithm (NSGA), Ant Colony

Optimization and Tabu search (Coello Coello,

2007).

NSGA algorithm is based on several layers of

classifications of the individuals. Before selection

is performed, the population is ranked on the basis

of nondomination: all nondominated individuals are

classified into one category (Fig. 6) (with a dummy

Table 1. Parameters of turbidity currents in Dez Reservoir from December 2002 to June 2003

Date StationWater depth

(m)

Observed deposit depth

(m)

Width of turbidity

current (m)

Mean velocity (m/s)

Direction of turbidity current (°)

Sediment transport rate

(m3/d)

Jan. 29, 2003 A2 58 15 300 0.17 163 to 231 44,000

Apr. 23, 2003 A2 94 20 300 0.8 180 1,014,120

Apr. 23, 2003 F 77 23.5 200 0.75 180 622,080

Apr. 24, 2003 A2 94 10 300 0.3 180 524,880

Apr. 24, 2003 B3 92 12 700 0.1 90 145,150

Apr. 24, 2003 C3 94 18.5 1,000 0.08 180 525,312

Fig. 6. NSGA algorithm

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fitness value, which is proportional to the population size, to provide an equal reproductive potential for

these individuals). To maintain the diversity of the

population, these classified individuals are shared

with their dummy fitness values. Then this group of classified individuals is ignored and another layer of nondominated individuals is considered. The process

continues until all individuals in the population

are classified. Stochastic remainder proportionate

selection is adopted for this technique. Since

individuals in the first front have the maximum

fitness value, they always get more copies than the rest of the population. This allows for a better search

of the PFknown regions and results in convergence

of the population toward such regions. Sharing, by

its part, helps to distribute the population over this

region (i.e., the Pareto front of the problem). The

NSGA was relatively successful over the course

of many years (Weile, 1996, Blumel, 2000, Reed,

2001), and the NSGA was also a highly inefficient algorithm because of the way in which it classified individuals.

3. RESULTS AND DISCUSSION

Optimization models for Eco-Friendly flushing operation generally consist of an objective function,

constraints and an optimization technique. In

this paper, the ecological flow constraint and its

improvement as compared to the previous studies

are highlighted. According to the core function

of a reservoir, a) Minimization of irrigation

deficits, b) Maximization of sediment evacuated,

c) Minimization of out flowing water volume d)

Maximization of power generated, could be used as

the objective function in the model. Assignment of

priority weight to individual objective depends on

policy of the reservoir operation. A multi-objective

optimal operation model of water-sedimentation

in Dez Hydropower reservoir is established to

coordinate Maximization of sediment evacuated

and Minimization of out flowing water volume and

improve the comprehensive benefits of reservoirs.

opt E = {Max∑t = 1 V(so)t , Min∑t = 1 V(o)t} (4)

where E is the comprehensive benefits; V(so)t is

outflowing sediment volume during flushing in time interval t ; V(o)t is outflowing water volume in time interval t ; T is the total number of time intervals.

The optimization is carried out under the following

two constraints which are incorporated in NSGA

by the penalty factor method. General descriptions

on constraints can be found in Atkinson (1996) and

Kawashima, (2003) for Existing flushing criteria

and Khakzad (2015) for Ecological flow constraints.Criteria for determining whether flushing at a

particular reservoir will be successful are required.

There are two key requirements for effective

flushing. First, the sediment quantities transported

through the low level outlets during flushing are

sufficient to enable a long term balance between

the sediment inflow and the sediment flushed, and

second, the volume of deposits remaining in the

reservoir after a sediment balance has been achieved

is sufficiently small to enable a specified storage

requirement to be met. These criteria depend on

the hydraulic efficiency of flushing. By applying

these criteria, reservoirs at which flushing might be viable can be identified. The hydraulic efficiency

of flushing can be defined in several ways. Some

definitions are shown in Table 2 ( Khakzad, 2014).In this paper, we concerned the sediment

balance and the ratio between useful storage

capacity that can be maintained in reservoir and a

substantial proportion of the original capacity, as

criteria to predict the feasibility of flushing sediment from reservoirs. For this purpose, the main criteria

such as the sediment balance ratio (SBR), the long

term capacity Ratio (LTCR), the draw down ratio

(DDR), flushing width ratio (FWR), reservoir top

width ratio (TWR), capacity inflow ratio (C/I) and sediment potential (SP) are used. These criteria

are defined as the following (Atkinson 1996,

Kawashima. S., 2003):

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SBR = ediment mass flushed annually

sediment mass depositing annually > 1

(5)

LTCR = sustainable capacity

original capacity > 0.35 (6)

DDR = 1 ‒ flow depth for the flushing water level

flow depth for the normal impounding level > 0.7 (7)

FWR = predicted flushing width

representative bottom width of reservoir > 1 (8)

TWR = top width of scoured valley

actual top width ~ 1 (9)

SP = Mean annual sediment inflow

Original storage capacity > 1 (10)

Following our previous research (Khakzad,

2015), which evaluated effect of sediment on

aquatic ecosystems using Decision tree forest (DTF)

and Group method of data handling (GMDH) for

198 data about aquatic ecosystem, we propose,

in this study, scale of the severity (SEV) of ill

effect for fishes for Ecological flow constraints.

This is more complicated than the conventional

reservoir operation that does not consider ecological

flow requirement or only considers a constant

minimum flow. Equation 11-15 show SEV base on concentration of suspended sediment, species, life

stage and duration of exposure.

SEV (For Adult salmonids and rainbow smelt) = Log concentration (mg/L) × ‒0.8697 + Log concentration (mg/L) × Log Exposure duration (h) × 0.4377 + Log Exposure duration

(h) × 3.886 (11)

MAE = 0.3781 RMSE = 0.4465 R2 = 0.9883

SEV (For Juvenile salmonids) = 15.28 + Log concentration (mg/L) × ‒2.415 + Log

concentration (mg/L) × Log Exposure duration

(h) × 0.0543 + Log concentration2 (mg/L) ×

0.2024 + Log Exposure duration (h) × ‒0.6366 + Log Exposure duration2 (h) × 0.0442 (12)

MAE = 0.7787 RMSE = 0.9875 R2 = 0.8214

SEV (For Salmonid eggs and larvae) = 4.665

+ Log concentration (mg/L) × 0.7655 + Log

Exposure duration (h) × 0.7376 (13)

Table 2. Different definitions of flushing efficiency

Efficiency expression Author

E = Vo / Vd Qian (1982)

E = Lo / Li Ackers et al. (1987)

E = (V2 ‒ V1) / Vo Mahmood (1987)

E = (V2 ‒ V1) / Vori Mahmood (1987)

E = Tr / (1 ‒ Tf ) Mahmood (1987)

E = Lo / Ld Atkinson (1996)

E = (Vso - Vsi) / Vo Lai et al. (1996)

E = (VoCo ‒ ViCi) / (ρVo) Morris et al. (1997)

Ci is total sediment concentration of inflow [kg m‒3] Vd is volume of deposit flushed out [m3] Co is total sediment concentration of outflow [kg m‒3] Vi is inflowing water volume [m3]E is flushing efficiency Vo is outflowing water volume [m3]Ld is annual quantity of sediment deposited [kg] Vori is original live capacity of the reservoir [m3]Li is annual quantity of sediment inflow [kg] Vso is outflowing sediment volume during flushing [m3]Lo is annual quantity of sediment flushed out [kg] Ssi is inflowing sediment volume during flushing [m3]Tf is fraction of year used for flushing V1 is storage capacity of reservoir before flushing [m3]Tr is fraction of year that the river’s sediment load will V2 is storage capacity of reservoir after flushing [m3]take to refill V2

‒ V1 ρ is bulk density of deposit [kg m‒3]

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MAE = 0.4412 RMSE = 0.5634 R2 = 0.9246

SEV (For Juvenile salmonids) = ‒12.81 + Log concentration (mg/L) × 9.677 + Log


(h) × 0.4975 + Log concentration2 (mg/L) ×

‒1.006 + Log Exposure duration (h) × ‒2.402 (14)

MAE = 0.6866 RMSE = 0.7934 R2 = 0.9620

SEV (For Adult nonsalinonids) = 15.94 + Log concentration‒1 (mg/L) × ‒108.1 + Log


(h) × ‒0.0694 + Log concentration‒1 (mg/

L) × Log Exposure duration (h) × 8.871 + Log concentration‒1 (mg/L) × Log Exposure duration‒1 (h) × 195.7 (15)

MAE = 0.9787 RMSE = 1.273 R2 = 0.6398

The Dez river provides fish habitat and supports commercial (market), subsistence and recreational

fisheries. A list of economically important fish

species found along the Dez river downstream of Dez

dam are listed in Table 3. Local fishermen indicate that high suspended sediment during flood seasons reduces a catch substantially because fish abandon their usual locations and cannot be found and also

that the likelihood of damaging nets is higher during

high turbidity events. This is consistent with the

responses from fishermen the day following flushing is that the fish was not biting or that only small fish was being caught in the nets.

Turbidity current sediments accumulate at the

dam could be flushed downstream during operations of the existing powerhouse, spillway facilities and

low-level outlets (Figure 7). The historical outflows experienced from powerhouse, spillway facilities

and low-level outlets are 200 (m3/s), 80 (m3/s) and

150 (m3/s) respectively. In Dez Hydropower, it is

provided with three low-level outlets within the dam

body. The original purpose of the low-level outlets

was to provide irrigation releases downstream

during periods of low flow through the turbines. In addition, these outlets were to provide a means of

Table 3. Fish species in the downstream of Dez dam

Scientific Name English Name Farsi Name

Aspius vorax Mesopotamian asp Shelej

Barbus esonicus Tigris Salmon Ghonreh, Bej, Song

Barbus grypus Large-scaled Barb Shirbot, Shebbot, Sorkheh

Barbus luteus Golden Barb Hamri, Zardak, Orange Zangool

Barbus pectoralis Levantine barbel Barzam, Nabash

Barbus sharpeyi Binni Benni

Barbus subquincunciatus Black Spot Barb Soleimani, Barzam-e-Khaldar

Capoeta damansara n/a Toini, Gel Khorak

Capoeta trutta Long-spine scraper Toini

Carassius auratus Goldfish Kapourche, Mahie Dehghan

Chalacaburuns mossulensis n/a Shah Kolie Jonoubi

Chondrostoma regium Mesopotamian nase Nazok

Cyprinion kais Kais kingfish, smallmouth lotak Botak-e-Dahan Koochak

Cyprinion Macrostomum Large-mouthed Barb Botak-e-Dahan Bazorg

Silurus triostegus Mesopotamian Catfish Esbele, Yari

Heteropneustes fossilis Stinging catfish Shlambo, Doodeh

Liza abu Abu Mullet Biah, Zoory, Shouchi

Tenualosa ilisha Ilisha, Hilsa Shad Shobour, Zabour

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emergency release from the reservoir as required.

Based on recent studies, the acceptable sediment

concentrations downstream of the spillway can be

maximum 15,000 (mg/L) (Khakzad, 2015).

The parameters of NSGA for optimization of

flushing operation were set as follows: Maximum

Number of Iterations = 100, Population Size = 50,

Crossover Percentage = 0.7, Mutation Percentage

= 0.4 and Mutation Rate = 0.02. These parameter

settings are regarded as optimal to the standard

NSGA algorithms. Figure 8 and 9, present the

Pareto-optimal front of NSGA for Scenario 1 (for

Fig. 7. General layout of powerhouse, spillway facilities and low-level outlets.

Fig. 8. Pareto-optimal front for Scenario 1.

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protecting Eggs & early life stages salmonids) and

Scenario 2 (for protecting Adult non-salmonids).

With help of Figure 8 and 9, assuming of a given

defined total volume of water passed per flush (m3),

total volume of sediment passed per flush (m3)

could be determined in Dez Hydropower reservoir.

Table 4, 5 present the results of optimization of

Eco-friendly flushing operation in Dez Hydropower reservoir for acceptable sediment concentrations

downstream of the spillway 8,000 (mg/L), 1,000

(mg/L), 15,000 (mg/L). Total volume of water

passed per flush (m3) were considered maximum

30000000 (m3) for Scenario 1 (for protecting Eggs

& early life stages salmonids) and 45000000 (m3)

for Scenario 2 (for protecting Adult non-salmonids).

As most fish in the Dez River spawn in March or April, the most conservative of the eggs and

larvae or the adult graphs should be employed from

March through June (Scenario 1) and the adult

graphs throughout the rest of the year (Scenario 2).

Results of the proposed framework to optimize of

flushing operation showed that this method provides a powerful tool for the selection of optimum

response scenario against the identified risks and

can be an important instrument for change in quality

of sediment management.

4. SUMMARY AND CONCLUSIONS

There is an increasing attention turning towards

river ecosystem conservation in Iran due to the

massive dam constructions and reservoir regulations.

Releasing ecological flow to the downstream

has gradually reached a consensus in reservoir

operations. In this study, Non-dominated Sorting

Genetic Algorithm (NSGA) was used successfully

to optimize flushing operation in Dez Hydropower reservoir and to evaluate the derived prediction

model. This study is based on Maximization of

sediment evacuated and Minimization of out flowing water as objectives and Existing flushing criteria and Ecological flow as constraints. All of the scenarios proposed in this study improve sediment evacuation

and thus the enhanced sustainability of the reservoir.

Computational results show that NSGA algorithm is

capable of quickly obtaining high-quality solutions

for multiple objectives knapsack problems and

that allowed establishing an objective quantitative

Fig. 9. Pareto-optimal front for Scenario 2.

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Table 4. Results of Acceptable single flushing time (hrs) for Scenario 1

SenariosAcceptable Single Flushing Time (hrs)

for different outflow (m3/s)Total volume of water passed

per flush (m3)Total volume of sediment

passed per flush (m3)

Scenario 1 (for protecting Eggs & early life stages

salmonids)

Acceptable sediment concentration downstream of the spillway =

8,000 (mg/L)Day 1 (T = 2.90 (h), Q = 350 (m3/s))Day 2 (T = 2.90 (h), Q = 280 (m3/s))Day 3 (T = 3.15 (h), Q = 350 (m3/s))Day 4 (T = 3.15 (h), Q = 350 (m3/s))Day 5 (T = 3.15 (h), Q = 280 (m3/s))Day 6 (T = 3.15 (h), Q = 200 (m3/s))

19967239 474894


10,000 (mg/L)Day 1 (T = 2.90 (h), Q = 430 (m3/s))Day 2 (T = 2.90 (h), Q = 350 (m3/s))Day 3 (T = 2.90 (h), Q = 200 (m3/s))Day 4 (T = 3.15 (h), Q = 350 (m3/s))Day 5 (T = 3.15 (h), Q = 280 (m3/s))Day 6 (T = 3.15 (h), Q = 200 (m3/s))Day 7 (T = 3.15 (h), Q = 200 (m3/s))Day 8 (T = 3.54 (h), Q = 200 (m3/s))

24469490 579309


15,000 (mg/L)Day 1 (T = 2.90 (h), Q = 280 (m3/s))Day 2 (T = 2.90 (h), Q = 200 (m3/s))Day 3 (T = 3.15 (h), Q = 430 (m3/s))Day 4 (T = 3.15 (h), Q = 350 (m3/s))Day 5 (T = 3.15 (h), Q = 280 (m3/s))Day 6 (T = 3.15 (h), Q = 280 (m3/s))Day 7 (T = 3.15 (h), Q = 280 (m3/s))Day 8 (T = 3.54 (h), Q = 280 (m3/s))

29223193 636579

Table 5. Results of Acceptable single flushing time (hrs) for Scenario 2

SenariosAcceptable Single Flushing Time (hrs)

for different outflow (m3/s)Total volume of water passed

per flush (m3)Total volume of sediment

passed per flush (m3)

Scenario 2 (for protecting Eggs & early life stages

salmonids)



33603560 820255



39287341 886628



44200594 1141134

( 98 )

re la t ion be tween Technica l and execut ive

requirements and Environmental impacts. Although

in this study, the model has only been applied in Dez

dam Hydropower reservoir, its generic formulation

allows the application of which to a broad range of

reservoirs to extend their useful life.

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Received: 108/04/30

Revised: 108/05/14

Accepted: 108/06/17

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Application of Non-dominated Sorting Genetic Algorithm ...twc.bse.ntu.edu.tw/upload/ckfinder/files/68-1-86-99_com.pdf · and pollution: water released from near the bottom of a stratified