PREDICTION OF MONTHLY AVERAGE RIVER DISCHARGE USING GENE EXPRESSION PROGRAMMING

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1 International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Iue 9, September 2017, pp , Article ID: IJCIET_08_09_110 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopu Indexed PREDICTION OF MONTHLY AVERAGE RIVER DISCHARGE USING GENE EXPRESSION PROGRAMMING Huham T. Ibrahim Lecturer, Civil Engineering Department, College of Engineering, Univerity of Barah, Barah, Iraq ABSTRACT Predicting of the river dicharge i important for convenient water reource planning and management epecially in warm and dry region. Thi tudy preented the application of Gene Expreion Programming (GEP) for predicting the monthly average dicharge of Euphrate River in Nairiyah city, outh of Iraq. The overall trategy in thi tudy i to contruct and develop three model for forecating the monthly average dicharge baed on antecedent dicharge value. Three tatitical parameter (Root Mean Square Error (RME), Mean Abolute Error (MAE), and Coefficient of Correlation (R)) were ued to evaluate the performance of thee model. The tatitical parameter illutrated that the forecated dicharge i highly influenced by the antecedent input dicharge value. The reult howed that GEP i a powerful and efficient technique to predict the monthly average dicharge in an arid region. Key word: Prediction, Euphrate River, Dicharge, Gene Expreion Programming, Nairiyah city Cite thi Article: Huham T. Ibrahim, Prediction of Monthly Average River Dicharge Uing Gene Expreion Programming, International Journal of Civil Engineering and Technology, 8(9), 2017, pp INTRODUCTION Accurate prediction of river flow i neceary for many purpoe uch a appropriate management of drought, reervoir operation, environmental protection, and water upply operation. Many of the activitie linked with the operation and planning of the component of a water reource ytem require predict of future event. For the hydrologic component, there i a requirement for both long term and hort term forecat of tream flow event, dicharge in order to optimize the ytem or to plan for future development. Many of thee ytem are large in patial extent and available hydrodynamic data i very little. Thee condition can generate in coniderable uncertainty in the information of hydrologic that i available [1]. Moreover, the deciion maker and river management for operation are trongly depend on accurate and reliable flow forecat [2] editor@iaeme.com

2 Huham T. Ibrahim There are a large number of mathematical hydrologic model that are developed to be ued for river flow imulation. Thee model can be claified a either phyically baed or ytem theoretic model [3]. Phyically baed model involve a detailed decription of variou phyical procee that control the hydrologic behavior of a ytem. Phyically baed model uch a, Areal Nonpoint Source Waterhed Environment Repone Simulation (ANSWERS) [4], Agricultural Non-Point Source Pollution Model (AnnAGNPS) [5], and Soil and Water Aement Tool (SWAT) [6], often need a large number of input parameter that are not eaily available in all region. Sytem theoretic model a an alternate method for runoff etimation are particularly ueful in area uch a the Gharau Waterhed, where there i a lack of environmental data. Overall, the major difference in the two type of model are the repreentation of the governing procee and their data need [7]. For real-time forecating, data-driven model might be favorable, a ophiticated phyical model often need tremendou amount of data and long computational time for model calibration [8]. A data-driven model might be preferable when underlying phyical mechanim are not fully undertood and if data of both input() and output() are ufficiently available to determine/etablih the input output relationhip while bypaing the phyical explanation of their dependence. Recently, data-driven model have been extenively ued in tream flow forecating [9-15]. Although of requiring to large amount of data and effort of human to calibrate, validate and tet the model, phyic baed method are ueful to undertand the entire of underlying proce [16]. In the other hand, good advantage of predicting model i that they jut require ome amount of data, but their drawback i that they appear like a black box in ue and alo require lengthy parameterization. Multi Linear Regreion (MLR), ANFIS, Fuzzy Logic Baed Sytem and Artificial Neural Network are mot common ued of predicting method [17]. Recently, gene expreion programming (GEP) ha been employed by reearcher in many important topic of hydrologic and water reource model [18-19]. Unlike mot of the emerging complicated technique, GEP i not a black-box model failing to deliver model formulation. The main objective of thi reearch i to predicting the monthly dicharge of Euphrate River in Nairiyah city, outh of Iraq by uing Gene Expreion Programming (GEP) and invetigate applicability and performance of thi technique for application in water reource engineering. 2. STUDY AREA AND DATA SET The Euphrate River i the longet river in Wetern Aia. The river ha three riparian countrie, Iraq, Syria and Turkey, and it bain i ditributed among five countrie with a total etimated population of 23 million. Mot of the Euphrate tream-flow originate from precipitation in the Armenian Highland; contribution by the remaining riparian countrie are generally mall. In addition to ome intermittent tream, the Sajur, Balikh and Khabour are the main contributor to Euphrate flow in Syria. Hitorically, the natural annual flow of the Euphrate at the Syrian-Turkih border wa around 30 BCM. Lat 70 year how a negative trend, indicating a decreae in mean annual flow to about 25 BCM. The regulation of the Euphrate River i an extreme example of how human intervention can impact a river regime. With the contruction of large water engineering tructure in uptream Turkey and Syria, the Euphrate flow regime ha hifted toward le pronounced eaonal variation. Water ue in the Euphrate Bain in Iraq, Syria and Turkey focue on irrigation, hydropower and drinking water upply, with agriculture conuming the larget hare of water (more than 70%). In accordance with the character of feeding and ditribution of precipitation, one can ditinguih three period in the annual cycle of the Euphrate river water regime. Flood period (February editor@iaeme.com

3 Prediction of Monthly Average River Dicharge Uing Gene Expreion Programming June) connected with now thawing in the mountain, ummer low-water period (July- October) and a period of rain flooding (November-February) within the flood period the Euphrate River convey about 75 percent of annual flow, in the dry period about 10 percent and in the period of autumn-winter flood about 15 percent [20]. Euphrate and Tigri meet near Qurnah, Barah, outh of Iraq. The combined flow, called Shatt Al Arab emptie in Arabian Gulf. Qurnah city i a pleaant little place 74 kilometer North Wet of Barah at the very tip of the point of Shatt Al Arab a hown in Figure 1. The monthly oberved meaurement of Euphrate River dicharge for the period ( ) in Nairiyah city were preented in thi reearch; thee data were obtained from water reource Directorate in Nairiyah city. Table 1 how ummary tatitic of the raw data. Table 1 Summary Statitic of the Raw Data. Statitic Parameter Value Average Standard Deviation Skew Exce Kurtoi Median Minimum 9 Maximum t Quartile rd Quartile 194 Figure 1 Study Area Location, howing Euphrate River in Nairiyah city, South of Iraq. 3. GENE EXPRESSION PROGRAMMING Gene Expreion Programming wa propoed by Ferreira [21], a an alternative or complement to other genetic baed computer programming technique like genetic programing (GP) and genetic algorithm (GA). Thi model work baed on two imple entitie: 1) Chromoome. 2) Expreion Tree editor@iaeme.com

4 Huham T. Ibrahim It tart with random generation of chromoome which are linear fixed tring of number defined by the gene. Moreover, uncontrained application of genetic operator (e.g. Replication, recombination, mutation, and etc.) are allowed on thee linear chromoome. Figure 2 how a imple tructure or expreion tree (ET) diagram of a ample candidate olution which how how the encoding differ from GP and GA. Such diagram hould be read from left to right. Thee model are baed on a training which enhance the algorithm to look for the optimum candidate olution or offpring/children among the generated population ubjected to a election environment. Becaue a random numerical contant (RNC) i a crucial part of any mathematical model, it mut be taken into account; however, Gene Expreion Programming ha the ability to handle RNC efficiently. In GEP, an extra terminal? and an extra domain Dc after tail of the each gene i introduced to handle RNC. In thi paper, the maximum fitne wa ued a topping condition of the developed GEP model. Many reearcher [22] depend on uggeted value by Ferreira [23], 30 chromoome, 8 head ize, and 3 gene were ued for model tructure. Figure 2 Structure of ame GEP candidate olution. 4. METHODOLOGY In thi tudy, three different model a hown in Table 1 were propoed by uing GEP. The oberved data for the period ( ) were ued in thi tudy. Hence 252 intance are available to forecat the monthly Euphrate river dicharge in Nairiyah city, outh of Iraq. A hown in Table 2, the firt model (M1) repreent the dicharge at time t (Q t ) a a function of antecedent dicharge (Q t-1 ) for lag one month. The econd model (M2) repreent the flow dicharge at time t a a function of two antecedent dicharge (Q t-1, Q t-2 ). Likewie, third model (M3) i propoed by conidering the integrated effect of three antecedent time tep. Table 3 illutrated the parameter etting were ued to develop the three model for predicting the monthly average dicharge of Euphrate River in Nairiyah city. Twelve mathematical function are elected for contructing the model a hown in Table 4. Table 2: Propoed Three Model for Prediction of Monthly Dicharge. Model Input Output M1 Q t-1 Q t M2 Q t-1, Q t-2 Q t M3 Q t-1, Q t-2, Q t-3 Q t editor@iaeme.com

5 Prediction of Monthly Average River Dicharge Uing Gene Expreion Programming Parameter Table 3: Model Building Parameter of GEP. Setting Value Number of chromoome 30 Head ize 8 Number of gene three expreion tree form the final mapping function Linking function Expreion tree function to be added to form the final mapping function Contant Two contant per gene with bound of ±10 Fitne function Root Mean quare error (RMSE) Genetic operator Default value of mutation, inverion, tranportation, and recombination and tranpoition Stopping criterion 100,000 generation Number of training ample 202 Number of teting ample 50 Function Table 4: Set of Available Function. Symbol Addition + + Subtraction - Multiplication * Diviion / Square root Sqrt Exponential Exp x to the power of 2 x 2 x to the power of 3 x 3 Cube root 3Rt Sine Sin Coine Co Arctangent Atan In GEP the oberved data are divided into two tatitically part: 80% for training, and 20% for teting. In the preent reearch, the root relative quared error (RRSE) (Eq. 1) i taken a fitne function. Thi i baed on the quare root of the reidual variance of the fitted model divided by the initial variance. Initial variance (Eq. 2) i the variance (Eq. 3) for the training data et uing the mean value of the target variable a the predicted value for all row. All fitne function compute fitne core that range from 0.0 to 1.0. A fitne of 0.0 mean the model fit very poorly; it i worthle or not viable. A fitne core of 1.0 mean the model fit the data perfectly. The firt tep in the GEP model building proce i to create an initial population with a random et of function and terminal. For each model, root mean quared error (RMSE) (Eq. 4), Mean Abolute Error (MAE) (Eq. 5), Correlation between actual and predicted value (R) (Eq. 6) are ued a evaluation criteria. = = ( ) (2) = (" ) # (3) (1) editor@iaeme.com

6 Huham T. Ibrahim Where: i 1 = = RMSE n = i n = = n i 1 Q m 2 Qm i MAE = 1 n (5) R = i= n i= n i= 1 Q Q m m Q 2 i= n m m i= 1 i= 1 Q _ _ 2 : The mean value of the target variable. " : The predicted value for row i. : The actual target value N: The number of row in the training data et. Q m : Meaured value. Q : Simulated value. Q _ Q _ m : Average of meaured value. : Average of imulated value. n: The number of obervation. 5. RESULTS AND DISCUSSION Three model are contructed for GEP to forecat a monthly dicharge in the Euphrate River. Thee model are decribed in Table 2. The performance of the model were aeed baed on the training and teting data et, a well a the overall performance of the data et. The bet fit model to predict monthly average dicharge according to the evaluation criteria (RMSE), (MAE), and (R) are hown in Table 5. Table 5: Formulae and Statitical Performance of Each GEP Model. Model No. Formulae RMSE MAE R M1 $ % =&$ %' $ %' M2 $ % =( $ %' ) co(&$ %' ) (4) (6) M3 $ % =6 $ %'# $ # %' $ %' $ %'7 +$ %' $ %' editor@iaeme.com

7 Prediction of Monthly Average River Dicharge Uing Gene Expreion Programming Depending on the reult of tatitical parameter a hown in Table (5), the tatitical criteria value give the impreion that GEP capable of predicting the monthly average dicharge of the river. The reult of M2 i better than the reult of M1, becaue M2 i a function of dicharge of a river releae at (t-1) and (t-2), while M1 i function to dicharge of a river releae at (t-1) only. On the other hand, M3 diplayed the highet R value (0.9239) and the lowet RMAE, MAE ( and ) repectively, in teting data et. The third model (M3) i bet performance than other two model (M1, M2), o the input combination (Q t-1, Q t-2, and Q t-3 ) leaded to ignificant improvement in the reult. The tatitical reult of performance model hown that the GPE technique i a powerful tool to obtain better predicating reult of monthly average dicharge of Euphrate River. The performance of all forecating model (M1, M2, and M3) developed during the teting period are hown in Figure 3, Figure 4, and Figure 5 repectively. A a final concluion, GEP and data driven model could be ued by Iraqi Minitry of Water Reource to obtain near-reality reult and provide an approximation of the expected flow value of the Euphrate River better than uing other high-cot model uch a conceptual model or phyically baed model [15]. 250 Oberved Predicted (GEP) Dicharge (m 3 /ec) Time (month) Figure 3 Comparion between Oberved and Predicted Monthly Average Dicharge in Teting Period for M1. Dicharge (m 3 /ec) Oberved Predicted (GEP) Time (month) Figure 4: Comparion between Oberved and Predicted Monthly Average Dicharge in Teting Period for M editor@iaeme.com

8 Huham T. Ibrahim Dicharge (m 3 /ec) Oberved Predicted (GEP) Time (month) Figure 5 Comparion between Oberved and Predicted Monthly Average Dicharge in Teting Period for M3. 6. CONCLUSIONS GEP technique i developed for predicting the monthly average dicharge of Euphrate River in Nairiyah city, outh of Iraq. Three tatitical parameter (RMSE, MAE and R) were ued to evaluate the performance of GEP model in thi tudy. The tatitical parameter illutrated that the forecated dicharge i highly influenced by the antecedent input data dicharge. It i found, the reult of the tatitical criteria for model M2 are better than model M1, in the ame direction, the performance of model M3 are bet than two other model (M1 and M2). The reult howed that GEP i a powerful and efficient technique to imulate the river flow dicharge in arid region. GEP model can be ued by Iraqi Minitry of Water Reource to obtain reult cloe to reality and give an approximate etimate of the expected flow value of Euphrate River, better than uing other high-cot model uch a conceptual model or phyically baed model. REFERENCES [1] Chowdhary and hrivatava R.K., (2010), River Dicharge uing artificial Neural Network, SENRA Academic Publiher, Burnaby, Britih Columbia,, Vol.4, No.3, PP [2] M. K. Akhtar, G. A. Corzo, S. J. van Andel, and A. Jonoki, (2009), River Flow Forecating with Artificial Neural Network Uing Satellite Oberved Precipitation Preproceed with Flow Length and Travel Time Information: Cae Study of the Gange River Bain, Hydrology and Earth Sytem Science, 13, PP [3] Mutlu, E., Chaubey, I., Hexmoor, H. and Bajwa, S.G., (2008), Comparion of artificial neural network model for hydrologic prediction at multiple gauging tation in an agricultural waterhed, Hydrol. Proce, 22(26): [4] Bouraoui, F. and Dillaha, T. ANSWERS-2000, (1996), runoff and ediment tranport model, J. Environ. Eng., 122(6): [5] Boch, D.D., Bingner, R.L. Theurer, F.D. Felton, G. and Chaubey, I., (1998), Evaluation of the AnnAGNPS water quality model. preented at the ASAE annual international meeting. Orlando, florida. Paper no: [6] Arnold, J.G., Srinivaan, R., Muttiah, R.S. and William, J.R., (1998), Large area hydrological modelling and aement Part I: M development, J. Am. Water Reour. Aoc., 34(1): editor@iaeme.com

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