application of trend analysis and arti cial neural...

Scientia Iranica A (2017) 24(3), 993{999

Sharif University of TechnologyScientia Iranica

Transactions A: Civil Engineeringwww.scientiairanica.com

Application of trend analysis and arti�cial neuralnetworks methods: The case of Sakarya River

G. Ceribasia;�, E. Doganb, U. Akkayac and U.E. Kocamazd

a. Sakarya University, Technology Faculty, Department of Civil Engineering, Sakarya, Turkey.b. Sakarya University, Faculty of Engineering, Department of Civil Engineering, Sakarya, Turkey.c. Sakarya University, Science Institute, Department of Civil Engineering, Bolu, Turkey.d. Uludag University, Vocational School of Karacabey, Department of Computer Technology, Bursa, Turkey.

Received 7 November 2015; received in revised form 1 February 2016; accepted 5 September 2016

KEYWORDSTrend analysis;Arti�cial neuralnetworks;Sakarya river;Rainfall;Stream ow;Suspended load.

Abstract. Various arti�cial intelligence techniques are used in order to make prospectiveestimations with available data. The most common and applied method among thesearti�cial intelligence techniques is Arti�cial Neural Networks (ANN). On the other hand,another method which is used in order to make prospective estimations with availabledata is Trend Analysis. When the relation of these two methods is analyzed, Arti�cialNeural Networks method can present the prospective estimation numerically, while thereis no such a case in Trend Analysis. Trend Analysis method presents result of prospectiveestimation as a decrease or increase in data. Therefore, it is quite important to make acomparison between these methods which brings about prospective estimation with theavailable data, because these two methods are used in most of these studies. In this study,annual average stream ow and suspended load measured in Sakarya River along withaverage annual rainfall trend were analyzed with trend analysis method. Daily, weekly,and monthly average stream ows and suspended loads measured in Sakarya River andaverage daily, weekly, and monthly rainfall data of Sakarya were all analyzed by ANNModel. Results of trend analysis method and ANN model were compared.© 2017 Sharif University of Technology. All rights reserved.

1. Introduction

Due to recent climate changes, hydraulic structures arebeing built in order to e�ectively use water resources,which are in danger of being consumed totally. Someimportant parameters are estimated properly in orderto complete economic life of hydraulic structures in theplanned time schedule. The most important parameteris the amount of solid material carried by the riverthroughout the planned life of the structure [1]. There-

*. Corresponding author. Tel.: +902642956510;Fax: +902642956424E-mail addresses: [email protected] (G. Ceribasi);[email protected] (E. Dogan); [email protected](U. Akkaya); [email protected] (U. Erkin Kocamaz)

fore, this study aims to make a prospective estimationof suspended load transported by the river.

In recent years, Arti�cial Neural Networks (ANN)have been used in various disciplines e�ectively andcommonly since they can form an easy, fast and moreaccurate model with low error margin on the basis ofnonlinear relations among parameters which in uencecases [1].

On the other hand, since hydrological sizes(rainfall, stream ow, suspended load, etc.) havea randomly changing characteristic in time, specialmethods are required to analyze a continuous decreaseor increase trend [2-6]. Basic assumptions, such asnormality, linearity, and independence, about classicalparametric tests are not practicalized yet in typical sur-face water quality. Therefore, it is suitable to use non-

994 G. Ceribasi et al./Scientia Iranica, Transactions A: Civil Engineering 24 (2017) 993{999

parametric tests rather than parametric ones. Thesenon-parametric tests are: Mann-Kendall, Spearman'sRho, and Mann-Kendall Rank-Correlations tests. Themethod which includes all these tests is Trend Analysismethod [4-6].

It is quite important to make a comparisonbetween these methods since both of them makeprospective estimation with available data due to thefact that these two methods are being used in mostof the studies that have been carried out in recentyears. Therefore, it is one of the most importantissues to analyze whether these two methods contradictor correspond to each other. Therefore, this studyaims to apply Trend Analysis Method and Arti�cialNeural Networks Method to rainfall, stream ow, andsuspended load of Sakarya river and Sakarya city.

2. Materials and methods

In order to apply Arti�cial Neural Networks Method,this paper considered daily, weekly, and monthly av-erage stream ows and suspended loads of StreamFlow Observation Station no. 1257 of Sakarya Riveras well as daily, weekly, and monthly average rainfallof State Meteorological Station no. 17069 of Sakaryacity [7,8]. In order to apply trend analysis method,annual average stream ow and suspended load ofStream Flow Observation Station no. 1257 of Sakaryacity and annual average rainfall of State Meteorologicalstation no. 17069 of Sakarya city were used.

2.1. Arti�cial neural networks methodMATLAB program was used while implementing Ar-ti�cial Neural Networks Method. ANN method wasapplied as three years of training, one year of valida-tion, and one year of testing. Data of daily stream ow,suspended load and rainfall of 33 years in total (1979-2011) were used which constituted 65% training, 18%validation, and 17% test, respectively.

One of the issues deserving attention in ANNstudies is the determination of number and charac-teristics of input. Determination of suitable inputcan be e�ective in problem-solving [9-13]. In thisstudy, various tests were made in order to determinecorrect input parameters. As a result of these tests,ANN model composed of 4 inputs and 1 output wasdetermined.

In ANN model: Day no, Water year, Rainfall(mm), and Stream ow (m3/s), were used as inputs,and the suspended load values (ton/day) were used asoutput.

2.2. Trend analysis method2.2.1. Spearman's Rho testThis is a quick and simple test to determine if thereis a correlation between two observation series. Rxi,i.e. rank statistic, was determined by ordering data in

increasing or decreasing order. Spearman's Rho teststatistics (rs) was calculated according to the followingequation [3,14]:

rs = 1� 6Pni=1(Rxi � i)2

(n3 � n): (1)

Since rs distribution was close to normal for n > 30,normal distribution tables were used. So, test statistics(Z) of rs is as follows:

Z = rspn� 1: (2)

It is concluded that if normal distribution correspond-ing to � signi�cance level, which is chosen as absolutevalue of Z, is smaller than Z�=2, then null hypothesis isaccepted. In addition, there is no trend in time seriesobserved if it is bigger. Consequently, there is a trend,and if Z value is positive, then there is an increasetrend; if it is negative, then there is a decrease trend.

2.2.2. Mann-Kendall testSince the Mann-Kendall test is a non-parametric test,it is independent from random variable distribution.With this test, the presence of a trend in a time seriesis checked with null hypothesis \H0: no trend" [15-20].In x1; x2; :::; xn time series to which the test will beapplied, xi, xj pairs are divided into two groups. Fori < j, if P represents the number of pairs xi < xj andnumber of pairs xi > xj , test statistics (S) is calculatedby the following relation:

S = P �M: (3)

Kendall correlation coe�cient is calculated as follows:

� =S

[n(n� 1)=2]: (4)

For n � 10:

�s =pn(n� 1)(2n+ 5)=18; (5)

Z =

8><>:(S � 1)=�s S > 00 S = 0(S + 1)=�s S < 0

(6)

It is concluded that if normal distribution corre-sponding to � signi�cance level, chosen as the absolutevalue of Z, is smaller than Z�=2, then null hypothesisis accepted. Moreover, there is no trend in time seriesobserved if it is bigger; there is a trend and if Z value ispositive, then there is an increase trend; if it is negative,then there is a decrease trend [4-6,21-24].

2.2.3. Mann-Kendall rank correlation testThis nonparametric test is used to determine whetheran increase or decrease trend is present in the appliedseries in time. The test graphically expresses the resultsand also determines starting point of the trend [3-5]. In

G. Ceribasi et al./Scientia Iranica, Transactions A: Civil Engineering 24 (2017) 993{999 995

Figure 1. u(t)� u0(t) graph of the annual ow of Sakarya River.

the hydro-meteorology time series, starting from theleft-hand side, the data xi are considered to be thelarger ones in the data that comes before itself insteadof the data. If this number is called ni, then xi datavalues are replaced with them and a whole numbersample function is obtained. If ti represents sequentialsums of these whole numbers, t, which is required bythe magnitude to test the method, becomes:

t =nXi=1

ni: (7)

Mean E(t) follows:

E(t) =n(n� 1)

4; (8)

variance follows:

var(t) =n(n� 1)(2n+ 5)

72: (9)

and u(t) function follows:

u(t) =[t� E(t)]p

var(t): (10)

Considering the assumption that there is no changeover time expressed by the near-zero values of u(t), thebigger values of u(t), surprisingly, show that a changeoccurs. On the other hand, graphical intersection pointof u(t) and u0(t) shows the starting time of the trend.

3. Results and discussion

3.1. Results of the trend analysis methodResults of Spearman's Rho and Mann-Kendall tests ofthe stream ow, suspended load, and rainfall data ofSakarya River and Sakarya are given in Table 1.

When Trend Analysis results were analyzed, itwas seen that there is a decrease trend in stream ow,suspended load, and rainfall for both tests (Spearman'sRho and Mann-Kendall tests).

Graphic results of Mann-Kendall Rank Correla-tion test of the stream ow and suspended load data ofSakarya River and rainfall data of Sakarya are given inFigures 1, 2 and 3.

According to Mann-Kendall rank correlation test,the starting years of a decrease trends are the year 1983for stream ow, 1983 for suspended load, and 1985 forrainfall. Therefore, these results were obtained whenaccuracy of the results of trend analysis was analyzed.

3.2. Results of arti�cial neural networksmethod

ANN model was applied as three years of training,one year of validation, and one year of testing. 65%of the whole 33 years of data was for training, 18%for validation, and 17% for testing. Daily testingperformance, weekly testing performance, and monthlytesting performance are given in In Figures 4, 5 and 6,respectively.

When graphics of Figures 4-6 are analyzed, thebest test performance is the graph of test performanceof suspended load predicted by ANN with the aid ofaverage daily data. But, in order to be certain of this,the decision can be made after analyzing validationperformances. On the other hand, results of daily,weekly, and monthly test scatter of real values andpredicted values are given in Figure 7.

R2 is close to the value of 1 as given in Figure 7,and so, we can discuss such a successful ANN. More-over, if an ANN model is trained with much data,

Table 1. Results of Spearman's Rho and Mann-Kendall tests of the stream ow, suspended load, and rainfall data.

The station Spearman's Rho test(rs)

Spearman's Rhotest (Z)

Mann-Kendalltest (�)

Mann-Kendalltest (Z)

Stream ow (m3/s) -0,39 -2,18 -0,27 -2,15Suspended load (tons/day) -0,50 -2,83 -0,36 -2,96Rainfall (mm) -0,55 -3,15 -0,40 -3,29


Figure 2. u(t)� u0(t) graph of the annual suspended load of Sakarya River.

Figure 3. u(t)� u0(t) graph of the annual rainfall of Sakarya.

Figure 4. Test performances of predicting suspendedload levels with ANNs using average daily data.

Figure 5. Test performances of predicting suspendedload levels with ANNs using average weekly data.

Figure 6. Test performances of predicting suspendedload levels with ANNs using average monthly data.

a much more successful ANN model will be formed.Therefore, when the graphics in Figure 7 are observed,the value of daily test determination coe�cient is theclosest to that of R2. The reason is that while 365data are used in daily data, 52 data are used in weeklydata and 12 data in annual data. Again, validationperformance should be considered in order to make adecision on daily data.

Real average stream ow and real average rainfallvalues were used while calculating validation perfor-mances. When Figures 8 to 10 are analyzed, it isseen that the best test performance is the graphs ofvalidation performance of suspended load predicted byANN with the aid of average daily data. On the otherhand, results of daily, weekly, and monthly test scatterof real and predicted values are given in Figure 11.

When the graphics in Figure 11 are analyzed,daily validation determination coe�cient value is theclosest to R2 value.

Afterwards, graphics of validation performancesof suspended load with ANN are analyzed by usingaverage monthly values in Figures 8 to 10. Graph-ics of average daily, weekly, and monthly validationcorrelations of real and predicted values are presentedin Figure 11. It is seen that the best validationperformance is the validation performance of averagedaily data. Therefore, the best test performance is theperformance of average daily data.

According to these statistics, prospective (be-


Figure 7. Comparison between observed and daily (a), weekly (b), monthly (c) simulated sediment level with ANNs fortest data.

Figure 8. Validation performances of predictingsuspended load levels with ANNs using average daily data.

Figure 9. Validation performances of predictingsuspended load levels with ANNs using average weeklydata.

Figure 10. Validation performances of predictingsuspended load levels with ANNs using average monthlydata.

tween 2012-2023) suspended load prediction of ANNmethod was done, trained with average daily rainfall,stream ow, and suspended load data (Figure 12).

Sakarya River suspended load, which was pre-dicted by ANN model, has a decrease trend comparedto every passing year.

Figure 12. Forecasting suspended load levels with ANNsusing daily data.

4. Conclusion

The factors causing decrease for hydraulic parameterswere analyzed. When the year 1972 is considered whichis the time G�ok�cekaya Dam on Sakarya River beganstoring water with storage volume of 910 � 106 m3,it is seen that the starting point of trends starts afterthis time. Therefore, since G�ok�cekaya Dam arrangedstream ows respectively and kept suspended load indead storage, it caused decrease both in stream owand suspended load. Moreover, when starting andcompletion dates of Sar�yar Dam building in the regionare considered and when the year 1956, which is thecompletion date of Sar�yar Dam building, is regardedas the year it began to store water, Sariyar Dam canbe seen as a factor in the decrease of stream ow andsuspended load [4,25-28].

With respect to climate, considering that waterresources can be a�ected by global warming, it isestimated that water resources in speci�c regions ofthe world will run out and be insu�cient in 50 years.As a result of this in uence, it is estimated that theamount of water per people would be nearly 40% in

Figure 11. Comparison between observed and daily (a), weekly (b), monthly (c) simulated sediment level with ANNs forvalidation data.


Turkey, which is a high value. Since Turkey is insemi-arid climate zone, it is clear that climate changewould surely be in uential. As it is understood fromthese results, it is seen that results of Trend Analysiscorrespond to the study that was carried out.

On the other hand, according to the results oftest analyses obtained after daily, weekly, and monthlytraining analyses, it is seen that the best result isobtained at daily test results. As a result of validationtest analysis carried out in order to assure the accuracyof test analysis result, it was again observed thatdaily validation test results present the best results.Therefore, Arti�cial Neural Networks, trained in thisway, would give more accurate results for the futurepredictions to be carried out for Sakarya River sus-pended load. As a result of ANN method applied inthis way, it is observed in Figure 12 as well that thesuspended load predicted between 2012-2023 continuesby decreasing every year. In other words, based onArti�cial Neural Network method, it is observed thatthere is a decrease trend in suspended load transportedby Sakarya River.

Therefore, it is observed that trend analysis andarti�cial neural network methods applied to the sus-pended load data of Sakarya River correspond, whichmeans that the results of both methods are similar inthe comparison.

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Biographies

Gokmen Ceribasi is an Assistant Professor in the De-partment of Technology Faculty at Sakarya University.He graduated with PhD from the University of Sakaryain 2014. He has published many works in di�erent �eldsof civil engineering.

Emrah Dogan is an Associate Professor in the De-partment of Civil Engineering in Sakarya University.He received a BS degree in Civil Engineering in 2001.He graduated with PhD from the University of Sakaryain 2008. He has published many works in di�erent �eldsof civil engineering.

Ugur Akkaya is a Lecturer in the Department ofArchitecture and urban planning in Abant Izzet BaysalUniversity. He is a PhD student in Sakarya University.His areas of expertise are ood risk management,hydraulic, uid mechanics, hydrology and meteorol-ogy.

Ugur Erkin Kocamaz is a Lecturer in the Depart-ment of Computer Programming at Uludag University.He is a PhD student at Sakarya University. His areasof expertise are arti�cial neural networks and computerand information systems.

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