Application of ANFIS and ANN based models for forecasting ... · Iyidere stream is located between cities of Rize and Trabzon (Figure. 4). The stream is about 160 km long and 20 m

Application of ANFIS and ANN based models forforecasting of

Iyidere River from Riza catchment.Chelang A.Arslan

Assis.profKirkuk University /Engineering College/Civil Engineering Department

Email:[email protected]

Abstract:Two different techniques for forecasting monthly streamflow of Iyidere river at Riza were investigated in this study. First of all the adaptiveneuro fuzzy interference ANFIS was applied to the monthly data of the iyidere stream then the artificial neural networks were applied also using radialbasis function networks RBNN and generalized regression neural networks GRNN. A combination of the streamflow data with the most importantclimatic factors nearby the stream station was investigated for both techniques . Coefficient of determination R 2 , Mean absolute error MAE, E nash andR bias statistics parameters are used to evaluate the models performance.Index terms: ANFIS, ANN, GRNN , RBNN, E nash , MAE, Rbias, Streamflow.

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—————————— u ——————————1. INTRODUCTIONStream flow forecasting is very important for many areas of waterengineering such as dam planning, flood prediction andestimation of domestic water supply . Using different forecastingmethods which are not based on physics equations, such as neuralnetworks and Fuzzy Logic are becoming very common in use invarious water resources engineering field. Artificial neuralnetworks have been shown to give successful results in manyfields of hydrology and water resources .Chen et al.,( 2006).Markus et al. (1995) used ANNs with back propagation algorithmto predict monthly stream flows at a gauging station in southernColorado by using snow water equivalent and temperature.Karunanithiet al. (1994) compared the Artificial neural networksANNs with traditional methods for modeling qualitative andquantitative water resource variables. Other researches Maierand Dandy (1996); Shamseldin (1997); Olsson et al. (2004) alsoused this technique successfully. Zealand et al. (1999) proved thesuccess of training the ANN with back-propagation algorithm for1-week-ahead streamflow forecasting. Imrie et al. (2000) appliedthe cascade correlation and back-propagation algorithms for shortterm streamflow forecasting . Actually Neural networks tries toemulate the operation of human brain the same is true for fuzzysystems. A neuro fuzzy system include ANN and fuzzy system ,Keskin et al., ( 2006). Neuro Fuzzy logic system is a hybridintelligent system that combines neural network with a fuzzysystem. Fuzzy logic and neural networks are naturalcomplementary tools in building intelligent systems. Nath.,(2007). A daptive Neuro Fuzzy based interference System(ANFIS) introduced in hydrologic forecasting by Jang (1993) forforecasting river flow . Zadeh (1965) produced Fuzzy set theorywith relative membership concept and proposed fuzzy optimumtheory. Tayfur et al. (2003). A model Based on simulating rainfall-stream flow using fuzzy logic and ANN was produced by Tayfurand Singh (2006). A neuro fuzzy network model for forecastingthe inflow of Brazilian hydroelectric plants was developed byBallini et. al (2001).In this research a comprehensive comparison of ANFIS modeland ANN two different types in Iyidere river forecasting from

Riza basin at Turkey was produced . The monthly data for thisriver was from 1995 to 2000. The effect of most effective climaticfactors on stream flow were also included in the appliedsimulating models.

2. ADAPTIVE NEURO-FUZZY INFERENCESYSTEM (ANFIS)

An adaptive neural network can be discribed as a network whichconsists a number of nodes connected through directional links.Each of these node is characterized by a node function with fixedor adjustable parameters. Learning or training phase of neuralnetworks is an operation which aims to determine parametervalues to fit the training data. The basic learning rule is the well-known back propagation method which seeks to minimize somemeasure of error, usually sum of squared differences betweennetwork’s outputs and desired outputs (Kaya et al., 2002), ( Alaboodi, 2014). Most fuzzy inference systems can be classified intothree types due to the types of inference operations upon “if-thenrules”. These types are Mamdani’s system, Sugeno’s system andTsukamoto’s system. The most commonly used one is Mamdani’ssystem. Sugeno’s system is more compact and computationallyefficient. The output is crisp, so, without the time consuming andmathematically intractable defuzzification operation, it is by farthe most popular candidate for sample-data based fuzzymodeling and it lends itself to the use of adaptive techniques(Takagi and Sugeno, 1985). In first-order Sugeno's system, atypical rule set with two fuzzy IF/THEN rules can be expressedas:Rule 1: If x is A1 and y is B1,

then f1= p1x+q1y+ r1………………….……………(1).

Rule 2: If x is A2 and y is B2, then

f2= p2x+q2y+ r2………………………………..……(2).

Figure(1) illustrates basic ANFIS structure

International Journal of Scientific & Engineering Research, Volume 7, Issue 5, May-2016 ISSN 2229-5518 632

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Each node i in the first layer which is shown in Figure(1) is anadaptive node that represents member ship functions which canbe explained by generalized bell functions :

, = ( ) =( )

… … … … (3).

where X=input to the node and a1, b1 and c1=adaptable variablesknown as premise parameters. The outputs of this layer are themembership values of the premise part. This product representsthe firing strength of a rule. The second layer consists of the nodeswhich multiply incoming signals and sending the product out.

, = = ( ) ( )……………………………..…………(4).In the 3rd layer, the nodes calculate the ratio of the ith rules firingstrength to the sum of all rules’ firing strengths.

, = = ………………………………………(5).

The nodes in the fourth layer are adaptive with node functions, = = ( + + )……… ………..(6).

where is the output of Layer 3 and { pi, qi, r1 } are theparameter set. Parameters of this layer are referred to asconsequent parameters.The single node in the fifth layer computes the final output as thesummation of all incoming signals

= ∑ ………………….……………………(7). Jang, (1993).

Figure(1) Structure of ANFIS Networks.

3. ARTIFICIAL NEURAL NETWORKS

ANNs can be considered as a powerful modeling Tool ifcompared with the statistical methods. Ceylan I , (2008). Thus,ANNs have been used in most engineering problems andapplications such as forecasting ,optimization, classification andpattern recognition .Canakci et. Al., (2012) . They are composed ofseveral highly interconnected computational units or nodes. Eachnode performs a simple operation on an input to generate an

output that is forwarded to next node in the sequence. Thisparallel processing allows for great advantages in data analysis.ANNs are widely used in various branches of hydraulicengineering and their property to approximate complex andnonlinear equations makes it useful tools in econometric analysis.Each network consists an input layer, an output layer and one ormore hidden layers . Hassan AM et.al., (2009).

3.1 RADIAL BASIS FUNCTION NUERAL NETWORKS(RBFNN).Radial Basis Function (RBF) is a powerful, fastlearning, and self-organized neural network. It is better than BPnetwork in approximation, classification and learning speed,especially in processing highly nonlinear problems . RBF neuralnetwork was proposed by Moody and Darken ( 1980 ). It includesthree layers: an input layer, a hidden radial basis neuron layerand a linear neuron output layer. Its structure is illustrated inFigure(2).

Figure(2) Structure of Radial Basis Function Neural Networks.

3.2 GENERLIZED REGRESSION NEURAL NETWORKS(GRNN).

A GRNN is a variation of the radial basis neural networks, whichis based on kernel regression networks . A GRNN does notrequire an iterative training procedure as back propagationnetworks. It approximates any arbitrary function between inputand output vectors, drawing the function estimate directly fromthe training data. In addition, it is consistent that as the trainingset size becomes large, the estimation error approaches zero, withonly mild restrictions on the function. Kim B, et. Al. , ( 2004).Figure (3) represents the basic structure of generlized regressionnetworks.



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Figure(3). General structure of GRNN

4. THE STUDY AREA :

Iyidere stream is located between cities of Rize and Trabzon(Figure. 4). The stream is about 160 km long and 20 m wide.Substrate of the stream consisted of rock and stone. The averageflow speeds are about is 2 and 4 msec in lower and upper basins,

respectively.

Figre(4) Location of the study area.

Rize state like most of the eastern Black Sea coast of Turkey, withwarm summers and cool winters. Snowfall is quite commonbetween the months of December and March, snowing for a weekor two, and it can be heavy once it snows. Rize and the easternpart of the Black Sea coast where it is situated has the highestprecipitation in western Asia, with an annual precipitationaveraging around 2,500 millimeters (100 in), with heavy rainfallyear round and a maximum in late autumn (October toDecember). The Black Sea coast receives the greatest amount ofprecipitation in Turkey and is the only region of Turkey that

receives high precipitation throughout the year. The watertemperature, as in the whole Turkish Black Sea coast, is alwayscool, fluctuating between 8 and 20 °C (46 and 68 °F) throughoutthe year.5. APPLICATION AND RESULTS:

The Monthly flow data which were taken from station no 2218 ofIyidere stream at Riza state in Eastern black sea basin , Turkeywith the climatic factors of Riza state around stream are used inthis study. The location of the stream is shown in Figure (4) . Theclimatic factors which were thought to be effective for describingthe streamflow are : Monthly mean rainfall, Monthly maximumrainfall, monthly mean temperature and monthly humidity. Thedata sets for monthly flow values for Iyidere stream and otherconsidered factors were taken from 1955-2000as monthly meanvalues . Four statistic parameters were used to evaluate themodels performance which are coefficient of determination R2 ,Mean absolute error , MAE, Nash-Sutcliffe efficiency E nash andpercent bias R bias .

These parameters are defined as:

= (∑ ( )( ))∑ ( ) ∑ ( )

………………………8.

= ∑ | − |....…..…………………………....…..9.

ℎ = 1 − ∑ ( )∑ ( )

………….…………….……10.

= 100 ∗ ∑ ( )∑ …….…………….…….……..11.

where At is the observed value and Ft is the forecasted value andFmean. Ameanare the mean value of the series(Chokmani et al., 2008;Tiyaki S et al., 2014. The percent bias expressed the degree towhich the estimation is under or over estimation. A positivepercent bias indicates overestimation, whereas a negative percentbias indicates underestimation (Salas et al. 2000). The optimumvalue of RBias is 0.0. A brief description of Rbias and ENash wasgiven also by Moriasi et al. (2007), this description can besummarized as:

25R50.0E(US)25R1565.0E0.50(S)

15R1075.0E0.65)(10R00.1E0.75(VG)

BiasNash

BiasNash

BiasNash

BiasNash

±³£±££±£<

±££±£<±<£<

toryUnsatisfacrySatisfacto

G

The analysis suggested that 6 antecedent flow values areadequate with the selected climatic factors as input vectors to theANFIS and ANN models with different combinations . DifferentANFIS architectures were tried to find optimum one. Using 2Gaussian membership function gave the best performance .Different ANN structures were also tried for both RBNN andGRNN . The data sets for all input variables were divided fortraining and test periods . Table (1) illustrates the results forANFIS model with investigated architectures. The best result was



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fond to be by using input combinations as Qt-1,Qt-1,Qt-3,Qt-4,Qt-5,monthly mean Rainfall Rm, monthly maximum rainfall Rmx,monthly mean temperature Tmean, monthly mean humidity H.

By using radial basis function networks RBFNN followingresults which are illustrated in Table (2)were found. The Resultsrepresents the best found one for each tried input combinationafter applying different numbers of neurons at the hidden layerand different spread values . The values of all statisticalparameters were better than ANFIS models . The best RBFNNmodel was by applying input combination which is consisting offlow values of the stream for lag 1,2 and 3 months with thevalues of monthly mean Rainfall Rm, monthly maximum rainfallRmx, monthly mean temperature Tmean, monthly humidity H byusing spread value =0.04 and 200 neurons at the hidden layerSince the value of coefficient of determination was found to be0.9866 , the same value was found for E nash . R bias value was =0.1576 and with mean absolute error =1.544.

Another ANN model was investigated which is generalizedregression neural networks GRNN with the same tried inputcombinations. The results of this trial are illustrated in theTable(3).It is clear from this table that the performance of theRBFNN was better than GRNN performance. By comparing theapplied methods on the Iyidere stream the high performance ofRBFNN models was concluded. As it is seen from the results thatthe RBFNN is slightly superior than GRNN and ANFIS models.

The best comparison can be represented by following Figure (5)which shows the monthly flow values which is resulted from theapplied models with the observed data .

Figure(5) Comparison between the best applied models with theobserved monthly flow values of Iyidere stream .

The detailed comparison can be also shown with scatter plot foreach applied model as shown in following figures.



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Figure(6) Scatter plot of ANFIS best model.

Figure(7) Scatter plot of GRNN best model.

Figure(8) Scatter plot of RBFNN best model

It is important to say that for all applied models the performancewas clearly improved by including the climatic factors withstreamflow values therefore the results of the models which areconsisted from stream flow values only were not produced.

6. CONCLUSIONSThe potential of ANFIS, RBGNN and GRNN for prediction offuture values of Iyidere streamflow at Riza region northernTurkey has been presented in this research . Different inputcombinations with different architectures were applied . It wasfound that the RBNN models showed better accuracy. .ANFISmodel performs better than GRNN models while the RBFNNmodels were better than ANFIS models . The performance of allthe applied models were evaluated by statistical indices such asR2, MAE, Enash and R bias . All the applied models were performingbetter with including the rainfall and humidity and temperature

factors, Comparatively,RBNN models performance was found tobe superior as compared to GRNN and ANIS models .

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Application of ANFIS and ANN based models for forecasting ... · Iyidere stream is located between cities of Rize and Trabzon (Figure. 4). The stream is about 160 km long and 20 m