Using Artificial Neural Networks for Modeling Suspended Sediment Concentration

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1 Sofa, Bulgara, May -4, 8 Usng Artfcal eural etworks for Modelng Suspended Sedment Concentraton YU-MI WAG *, SEYDOU TRAORE AD TIEFUA KERH Department of Cvl Engneerng, Department of Tropcal Agrculture and Internatonal Cooperaton atonal Pngtung Unversty of Scence and Technology, epu Hsang, Pngtung 9, TAIWA *Correspondng author: Wang Abstract: For contnuous montorng of rver water qualty, ths study assesses the potental of usng artfcal neural networks (As) for modelng the event-based suspended sedments concentraton (SSC) n Jasan dverson wer n southern Tawan. The hourly data collected nclude the water dscharge, turbdty and SSC durng the storm events. The feed forward backpropagaton network (BP), generalzed regresson neural network (GR), and classcal regresson were employed to test ther performances. The results showed that the performance of BP was slghtly better than GR model. In addton, the classcal regresson performance was nferor to As. Thus, usng As were more relable than the classcal method. Furthermore, the turbdty s a domnant varable over water dscharge for SSC estmaton n the wer. Key-Words: Artfcal neural networks, suspended sedments concentraton, turbdty, water dscharge, modelng. Introducton Rver suspended sedment modelng s requred to provde basc nformaton for montorng the water qualty related to the rver management problems. The sedments transportaton montorng requred a good sample technque whch s very lengthy and expensve []. In the recent past, several studes focused on the understandng of sedment transport dynamcs [, 3]. It has been demonstrated the how percentages of the dfferent partcle szes n suspended sedment vary accordng to the hydraulc characterstcs of the rver and the clmatc regme of the area [3]. It s therefore mportant to develop a model that can predct accurately the suspended sedments concentraton from contnuous water data set where typhoon and tropcal storms exst. Accordng to [4] the relatonshp between water dscharge, turbdty and suspended sedments concentraton can be used for estmatng contnuously the water qualty durng the storm events. Artfcal neural network (A) s a technque wth flexble mathematcal structure whch s capable of dentfyng complex non-lnear relatonshp between nput and output data wthout detaled the nature of the nternal structure of the physcal process. The A s capable to model any arbtrarly complex nonlnear process that relates sedments load to contnuous water dscharge. Accordng to [5], A s a massvely parallel dstrbuted nformaton processng system based on concepts derved from research on the nature of human brans, and has many dstnct advantages for hydrologcal modelng. As are very common n hydrology scence. The emergence of A technology has provded many promsng results n the feld of hydrology and water resources smulaton [6-8]. Study reported that, the A better performs the sedments yeld loaded [9]. The above revews modeled the sedment processes by usng dfferent neural networks. In ths study, the generalzed regresson neural network (GR) and feed forward backpropagaton network (BP) algorthms were employed. Study reported by [] consders the GR as a worth technque n sedment modelng, whch s one of the most challengng works n water resources engneerng. GR approxmates any arbtrary functon between nput and output vectors, drawng the functon estmate drectly from the tranng data. The employment of GR n rver sedment load has been carred out n recent year. As well, the BP used n ths study s one of the most popular and tradtonal feed forward networks whch has been wdely used n rver sedment yeld modelng [9, ]. Accordng to [], As generally were found to be superor to conventonal statstcal technques n suspended sedment estmaton. The water dscharge, turbdty and SSC data collected for most of the papers were daly or monthly tme scaled. In ths research, A was appled to hourly suspended sedments concentraton collected manually durng the storm events from July to October. The man objectve of ths ISB: ISS: 79-57

2 Sofa, Bulgara, May -4, 8 study s to evaluate the potental of As model for the wer suspended sedment concentraton estmaton. The present study compares the performance of As model for SSC estmaton by usng contnuous hourly turbdty and water dscharge as nput data set collected from the Jasan dverson wer n southern Tawan. Materal and Methods. Study Area Jasan dverson wer s located n Chshan Rver, southern part of Tawan at 57 3 orth lattude and East longtudes (Fgure ). The Chshan Rver s a trbutary of the Kaopng Rver whch s a major rver n Tawan. The wer s bult for supplyng.3 mllon tons of water per day averagely for cvl and ndustral use. As ndustry and commerce n southern Tawan are developng by leaps and bounds n recent years, demand for water s rsng. Also, the wer s a contnuaton of the anhua reservor whch provdes.8 mllons tons of water per day. Durng the wet perod, the surplus water of the Kaopng Rver s channeled nto the Tsengwen Reservor for allocaton and storage. In ths locaton, the average annual ranfall s mm wth an abundance ranfall occurrng n the wet season (May to October), conversely to the dry season (ovember to Aprl). In the last ffty years, the total ranfall averages n dry and wet seasons were 35.9 and mm, respectvely. It can be seen that the ranfall dstrbuton at the locaton s unevenly dstrbuted between the two seasons. Tsengwen Reservor typcal ranfall pattern and topography of the study area where most of the SSC s due to the typhoon storms. The water samples were analyzed by turbdmeter whch apples a nephelometry technque that measures the level of lght scattered by partcles at rght angles (9 o ) to the ncdent lght beam. The data set had a total of 39 patterns and was dvded between tranng, valdaton and testng to reach the best generalzaton. For preventng an overcome problem assocated to the extreme values, the nput and output data set were scaled n the range of [ ] usng the followng equaton [3]. Y Ymn Ynorm = () Ymax Ymn where, Y norm s the normalzed dmensonless varable; Y s the observed value of varable; Y mn s the mnmum value of the varable; and Y max s the maxmum value of the varable..3 Artfcal eural etworks and Model Evaluaton The most commonly used A n hydrologcal predctons s the feed forward network wth the BP tranng algorthm [4]. Feed forward backpropagaton s a supervsed learnng technque used for tranng artfcal neural networks. BP has been wdely used n approxmatng a complcated nonlnear functon. The neural network structure n ths study possessed a three-layer learnng network consstng of an nput layer, a hdden layer and an output layer. Fgure shows the typcal confguraton for a BP used n ths study. Kaopng Rver basn X X Y anhua Reservor Jasan dverson wer Transbasn Dverson Tunnel Fgure. Sketch of the study area.. Data Collected In ths study, the hourly water dscharge (cms), turbdty (TU) and suspended sedments concentraton (ppm) collected from July 8, to October,. These hourly data were manually obtaned durng the storm events. The hourly sedment data have been collected because of the Input layer Hdden layer Output layer Fgure. Structure of BP neural network Selected. The mathematcal equaton of each layer may be wrtten as followng: Yo = Φ( WoX θo) () where Y o s the output of the neuron o, W o s the weght ncrements between and o, X s the nput sgnal generated for neuron, θ o s the bas term assocated wth neuron o, and the nonlnear ISB: ISS: 79-57

3 Sofa, Bulgara, May -4, 8 actvaton functon Φ s assumed to be a sgmod x functon as Φ(x) = /(+ e ) for the contnuous and dfferental process. GR can be treated as a normalzed radal basc functon network n whch there s a hdden unt centered at every tranng case. These radal basc functon unts are usually probablty densty functons such as the Gaussan. By defnton, the regresson of a dependent varable Y on an ndependent X estmates the most probable value for Y, gven X and a tranng set. The regresson method wll produce the estmated value of Y wth a mnmzed root mean square error (RMSE). Fgure 3 shows a schematc dagram of generalzed regresson neural network archtecture. In Fgure and 3, X, X and Y represent the turbdty (T), water dscharge (Q) and suspended sedment concentraton (SSC), respectvely. X X Input layer Pattern layer Summaton layer Output Fgure 3. Schematc dagram of GR archtecture. The classcal procedure between water dscharge, turbdty and SSC reported by several studes [, 5-7] may be wrtten as followng: b Y s = ax (3) where Y s represents suspended sedment concentraton, X s turbdty or water dscharge, and a and b are the constants. The performances evaluatons were based on the root mean square errors and the square value of coeffcent of correlaton ( r ) between estmated and observed SSC. The root mean square error was used to test the statstcal sgnfcant between estmates and observed SSC whch can be expressed as: d = RMSE = (4) where d s the dfference between th estmated and th SSC observed values and s the number of observatons. The coeffcent of correlaton has been used for further analyss to evaluate the performance of Y estmaton model. It s defned as follows: (X X)(Y Y) r = (5) (Y Y) = (X X) = = Where X and X are the observed and ts average values; Y and Y are the estmated and ts average values; s the number of observatons. 3 Dscusson of Results The neural networks were fed wth turbdty (T) and water dscharge (Q) data selected as the ndependent nput varables. The suspended sedment concentraton (SSC) was used as a dependent output varable for the networks. In general, the tranng, valdaton and testng are the fundamental steps of neural network process. The tranng data set s used to tran a neural network by mnmzng the error of the data set durng the tranng. The valdaton data set s used to fnd the neural network performance. Then, the test set s used for checkng the overall performance of a traned and valdated network. The networks were tested usng dfferent nput and output values that were not gven for tranng prevously. For the Feed forward back propagaton (BP) the data have been dvded n three sets, tranng (6%), valdaton (3%) and testng (%). The determnaton of the number of nodes n the hdden layers provdng the best tranng results was the ntal process of the tranng procedure. Hence, varous numbers of nodes n a hdden layer were tred for the BP algorthm. However, the generalzed regresson neural network (GR) does not requre an teratve tranng procedure as the BP model. GR was carred out by tryng dfferent smoothng parameters n order to obtan the best performance. Table shows the networks performance durng the tranng stage for BP and GR. The confguraton wth nputs (turbdty and water dscharge), 4 hdden nodes and unque output (SSC) denoted as BP ( 4 ) provded the best performance durng the tranng stage,.e. hghest r (.977). For the generalzed regresson neural network, the structure GR (,., ) wth nputs, smoothng parameter. and nput gave the hghest r (.958) durng the tranng stage. For the testng perod, the network performances comparson results were gven n Table. BP ( 4 ) confguraton for the testng perod compared wth the observed SSC gave better 3 ISB: ISS: 79-57

4 Sofa, Bulgara, May -4, 8 Table : Performances of BP and GR durng the tranng perod. A Model odes n confguraton nput hdden layer r FFBP ( 4 ) Q, T FFBP ( ) T.99 FFBP ( ) Q.883 GR (,., ) Q, T GR (,., ) T GR (,., ) Q Table : Performance of BP and GR durng the testng perod. A Model confguraton nput RMSE r FFBP ( 4 ) Q, T.7.93 FFBP ( ) T FFBP ( ) Q GR (,., ) Q, T.5.97 GR (,., ) T GR (,., ) Q estmates results by ts lowest RMSE (.5) and hghest r (.93). In ths confguraton the network has two nputs, hourly turbdty and water dscharge for estmatng the event-based SSC. Usng ths confguraton, t can be seen from Fgures 4a (plot) and b (scatter) a good agreement between estmated and observed SSC when turbdty and water dscharge are used together as nput. Conversely, usng only one nput n the same confguraton, the performance of BP was reduced as shown n the Table for both tranng and testng perod. It could be observed that, usng a sngle nput wth BP algorthm less performs the suspended sedment concentraton estmaton. BP algorthm may not lead to good generalzaton propertes for the network when the nput data are lmted [6]. Although the sngle nput less performs, t has been observed that, the performances were hgher for turbdty (RMSE=.45, r =.95) than water dscharge (RMSE=.65, r =.54) durng the testng perod. Accordngly, the turbdty seems to be a domnant varable over the water dscharge for the suspended sedment concentraton estmaton for Jasan dverson wer. For GR, durng the testng perod, the confguraton GR (,., ) provded the best performances (RMSE=.5, r =.97) as shown n Table. The GR network performng comparsons between observed and estmated suspended sedment concentraton durng the testng perod are presented n Fgures 5. Fgures 5a and b show the plot and scatters of estmated and observed SSC durng the testng perod, respectvely when T 4 and Q are used as the network nput. Smlarly to BP, usng a sngle turbdty or water dscharge as nput varable wth GR, decrease the performance of the neural network model. The performances evaluated durng the testng perod were RMSE=.37, r =.99 when the turbdty was used as a sngle nput, and RMSE=.597, r =.558 for the water dscharge. Further observaton showed that the turbdty s a domnant parameter over the water dscharge for the event-based SSC estmaton n the wer. Accordng to [5], other factors whch are not ncluded as nputs n the networks could explan ths poor performance of water dscharge. Studes done by [8, 9] denoted that human actvty related to land surface dsturbance ncrease the suspended sedment flux. Data analyss of hydrologcal processes of the watershed reveals that the water qualty parameters are mostly affected by weather forces and land use of the watershed [] The human actvty could ncrease the suspended flux ndependently to the water dscharge. Ths could explan the poor relatonshp between water dscharge and suspended sedments concentraton recorded at the wer. Estmated Suspended Sedmen Concentraton (ppm) estmated Suspended Sedmen Concentraton (ppm) (a) Observed BP Observed Suspended Sedment Concentraton (ppm) (b) y =.989x r = Observed Suspended Sedment Concentraton (ppm) Fgure 4. Suspended sedment concentraton estmated by BP for the testng perod usng T and Q as nput varables. ISB: ISS: 79-57

5 Sofa, Bulgara, May -4, 8 Estmated Suspended Sedmen Concentraton (ppm) Estmated Suspended Sedmen Concentraton (ppm) (a) Observed GR Observed Suspended Sedment Concentraton (ppm) (b) y =.9555x r = Observed Suspended Sedment Concentraton (ppm) Fgure 5. Suspended sedment concentraton estmated by GR for the testng perod usng T and Q as nput varables. It could be conclude that from ths study, by usng a sngle nput varable decrease the performance of the neural networks. By comparng the performance of A wth the classcal lnear regresson method, A could provde the hghest performance for event-based suspended sedment concentraton estmaton. The relatonshp from classcal method for turbdty versus SSC, and water dscharge versus SSC were r (.89) and r (.455), respectvely. Studes reported that, A could provde an estmate closer to observed suspended sedment concentraton than the classcal lnear regresson method [, ]. Prevous Report done by [3] demonstrated from the daly suspended sedment concentraton smulaton that, the modelng of sedment concentraton n a rver s possble through the use of A. The predctve accuracy of the A model was found to be better for modelng sedment transport [4]. Accordng to [5], the performance of the BP was found to be superor to conventonal statstcal and stochastc methods n contnuous flow seres forecastng. The superorty of As over the conventonal method n the revewed predcton study can be attrbuted to ther capablty to capture the non-lnear dynamcs and generalze the structure of the whole data set [6]. Clearly, usng the As for sedment modelng s more relable than the other methods n the wer studed heren. 4 Conclusons Ths study showed the ablty of the feed forward back propagaton (BP) and the generalzed regresson neural networks to model the event-based suspended sedment concentraton n Jasan dverson wer. Both BP and GR perform better than the conventonal lnear regresson method. It was observed from the results of ths study that, the performances of the networks were hgher when turbdty and water dscharge were used together as an assocate nput. Usng a sngle nput decreases the network performances. In addton, the turbdty seems to be a domnant varable on water dscharge for the event-based suspended sedment concentraton estmaton for Ja Xan dverson wer. It could be conclude clearly that by usng the As for modelng the sedment n the wer studed heren s more relable than the other methods. References [] D. Pavanell, and A. Palglaran. Montorng water flow, turbdty and suspended Sedment load, from an Apennne catchment basn, Italy,, pp [] Lews, J. and R. Eads. Turbdty-controlled suspended sedment samplng. Management councl net-worker, Vol.6, o.4, 996, pp-3. [3] D. Pavanell; and A. Bg. A new ndrect method to estmate suspended sedment concentraton n a rver montorng programme. Bosystems Engneerng, Vol.9, o.4, 5, pp [4] J.U. Ktheka, M. Obero, and P. thenge. Rver dscharge, sedment transport and exchange n the Tana Estuary, Kenya, Estuarne. Coastal and Shelf Scence, Vol.63, 5, pp [5] Y.M. Zhu, X.X. Lu and Y. Zhou. Suspended sedment flux modelng wth artfcal neural network: An example of the Longchuanjang Rver n the Upper Yangtze Catchment, Chna. Geomorphology, Vol.84, 7, pp. -5. [8] K.P. Sudheer, A.K. Gosan, and K.S. Ramasastr. Estmatng actual evapotranspraton from lmted clmatc data, 5 ISB: ISS: 79-57

6 Sofa, Bulgara, May -4, 8 usng neural computng technque. Journal of Irrıgaton and Dranage Engneerng ASCE, Vol.9, o.3, 3, pp [] Ö. Ks. Suspended sedment estmaton usng neuro-fuzzy and neural network approaches. Journal of Hydrologcal Scences, Vol.5, o.4, 5, pp [] A.J. Adeloye, and A.D. Munar. Artfcal neural network based generalzed storage yeld relablty models usng the Levenberg Marquardt algorthm. Journal of Hydrology, Vol.36, 6, pp [] R.K. Ra, and B.S. Mathur. Event-based sedment yeld modelng usng artfcal neural network. Water Resource Management, DOI.7/s , 7. [3] H.K. Cgzoglu, and M. Alp. Generalzed regresson neural network n modellng rver sedment yeld. Advances n Engneerng Software, Vol.37, 6, pp [4] A. Agarwal, R.D. Sngh, S.K Mshra, and P.K Bhunya. A-based sedment yeld models for Vamsadhara rver basn (Inda). Water SA, Vol.3, o., 5, pp [6] H.K. Cgzoglu, and M. Alp. Ranfall-runoff modellng usng three neural network methods. Lecture notes n artfcal ntellgence (Lecture notes n computer scence): Sprnger-Verlag; 4, pp [8] Y.C. Yeh. Appled neural networks. Ruln Publshng Co, Tawan, 997. [9] R.S. Govndaraju, and A.R. Rao. Introducton n artfcal neural networks n hydrology. Kluwer, Dordrecht, etherlands,, pp.-7. [] M.D. Morehead, J.P. Syvtsk, E.W.H. Hutton, and S.D. Peckham. Modelng the temporal varablty n the flux of sedment from ungauged rver basns. Global and Planetary Change, Vol.39, 3, pp [] Y.M. Wang, S.C. Juaug, C.C. La, and T. Kerh. Estmaton of suspended sedment dscharge for a storm. Journal of Unversty of Scence and Technology Bejng, Vol.8, o., 6, pp [] O. Tzorak, and.p. kolads. A generalzed framework for modelng the hydrologc and bogeochemcal response of a Medterranean temporary rver basn. Journal of Hydrology, Vol.346, 7, pp. -. [3] Y. Zhou, X.X. Lu, Y. Huang and Y.M. Zhu. Anthropogenc mpact on the sedment flux n the dry-hot valleys of Southwest Chna an example of the Longchuan Rver. Journal of Mountan Scence, Vol., 4, pp [4] X.X. Lu. Spatal varablty and temporal changes of water dscharge and sedment flux n the lower Jnsha trbutary: mpact of envronmental changes. Rver Research and Applcatons, Vol., 5, pp [5] G.B. Sahoo, C. Ray, and E.H. DeCarlo. Use of neural network to predct flash flood and attendant water qualtes of a mountanous stream on Oahu, Hawa. Journal of Hydrology, Vol.37, 6, pp [6] S.K. Jan. Development of ntegrated sedment ratng curves usng As. Journal of Hydraulc Engneerng, Vol.7,, pp [7] A. Sarang, and A.K. Bhattacharya. Comparson of Artfcal eural etwork and regresson models for sedment loss predcton from Banha watershed n Inda. Agrcultural Water Management, Vol.78, 5,pp [8] Ö. Ks. Mult-layer perceptons wth Levenberg Marquardt tranng algorthm for suspended sedment concentraton predcton and estmaton. Hydrologcal Scences Journal, Vol.49, 4, pp.5-4. [9] B. Bhattacharya, R.K. Prce, and D.P. Solomatne. Data-drven modellng n the context of sedment transport. Physcs and Chemstry of the Earth, Vol.3, 5, pp [3] S. Brkundavy, R. Labb, H.T. Trung, and J. Rousselle. Performance of neural networks n daly streamflow forecastng. Journal of Hydrologc Engneerng, Vol.7, o.5,, pp [3] H.B. Celkoglu, and H. K Cgzoglu. Publc transportaton trp flow modelng wth generalzed regresson neural networks. Advances n Engneerng Software, Vol.38, 7, pp ISB: ISS: 79-57