APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR WATER QUALITY MANAGEMENT

Transcription

1 LOWLAND TECHNOLOGY INTERNATIONAL Vol. 5, No.2,10-15, December 2003 Internatonal Assocaton of Lowland Technology (IALT), ISSN APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR WATER QUALITY MANAGEMENT I. Zaheer 1 and C. - G. Ba 2 ABSTRACT: A new artfcal neural networ based on decson-mang approach for water qualty management to control envronmental polluton s presented. Prevous research on water qualty management problems has shown that tradtonal optmzaton technques and an expert-system approach do not provde an educated soluton comparng wth decson mang approach, whch s related to the nterpretaton of data based on certan set of rules. Under such condtons, the Artfcal Neural Networ (ANN) learns the rule governng the decson-mang through a seres of experments. In the present study, ANN was used to evaluate the relatve effects of varous polluton sources on the qualty of rver water. Usng a bacpropagaton algorthm of a feed forward neural networ, the relatve effects of polluton sources were evaluated for strategc plannng of water qualty management. The case study for the Hanjang Rver of Chna was selected to demonstrate the procedure and performance of a neural networ-based approach for analyss and dscusson. Key words: Bac propagaton, neural networ, polluton sources, water qualty management INTRODUCTION Artfcal Neural Networs are computatonal systems whose archtecture and operaton are nspred from our nowledge about bologcal neural cells (neurons) n the bran. A real neural networ s a collecton of neurons, the tny cells that our brans are composed of. A networ can consst of a few to a few bllon neurons connected n an array of dfferent methods. ANNs attempt to model these bologcal structures n terms of both ther archtecture and operaton. Ther functon can be descrbed ether as mathematcal computatonal models for non-lnear functon approxmaton, data classfcaton, and clusterng / nonparametrc regresson, or as a smulatons of the behavor of a collecton of model bologcal neurons n the human. Neural networs have provded a system that can relably perform the decson-mang n placng of the human bran and that can act as an alternatve to expert systems for the few decades. In such a system, the rules governng the decson-mang process related to data nterpretaton are learned ether expermentally or by smulaton usng a complcated non-lnear dynamc. The applcatons for neural networs have ncreased rapdly n the feld of water qualty management (Wen and Lee 1998), economc analyss, water resources plannng and hydrologc tme seres, as descrbed n Charaborty et al. (1992), Wndsor and Harer (1990), Lachtermacher and Fuller (1994) and Schzas et al. (1994). A typcal feed forward neural networ s shown n Fg. 1. In recent years, ANNs have been successfully used n the feld of water qualty management and plannng for water polluton control of rver systems, wetlands and low land areas. The neural networ approach to mult-objectve optmzaton for water qualty management has provded an educated soluton to ad n the decson-mang process for rver systems (Chng and Chh 1998). In other studes, t has provded a vable means of forecastng of water qualty parameters (Holger and Graeme 1996). Smlarly, Sovan et al. (1999) have descrbed the development and valdaton of an artfcal neutral networ for the purpose of estmatng and predctng the norganc total ntrogen (TN) concentraton from varous nonpont sources of watershed features for example agrculture and domestc land uses. In the present study, we too a new approach based on the methodology adopted by Karayanns and Venetasanopoulos et al. x 1j x 2j x nj Input W 1j W 2j W nj Σ Threshold θ Sgmod functon Output Fg. 1 Typcal feed forward neural networ wth synapses orented from left to rght 1 PhD Scholar, College of Water Resources and Envronment, Hoha Unversty, Nanjng , P. R. CHINA. 2 Professor, College of Water Resources and Envronment, Hoha Unversty, Nanjng , P. R. CHINA. Note: Dscusson on ths paper s open untl June 1, 2004.

2 Zaheer and Ba all polluton sources before mplementng a waste allocaton program for waste-load reducton and rver polluton control. Karayanns and Venetasanopoulos (1991) have utlzed a learnng algorthm to evaluate the performance of varous neural networ archtects. In the present study, a neural networ system was developed to provde nformaton mang t possble to transform a realworld problem nto a potental neural networ applcaton. MATERIAL AND METHODOLOGY Fg. 2 Schematc map of the Hanjang Rver (1990a) to control water envronmental polluton. A neural networ was used to solve the problems nvolvng decsonmang to evaluate the relatve effects of varous polluton sources on water qualty n addton of predctng water qualty parameters and carryng out mult-objectve optmzaton of water qualty management. TRADITIONAL APPROACHES VERSUS ARTIFICIAL NEURAL NETWORK Regardng tradtonal approaches to water qualty management and water polluton control of rver systems, methods le smulated, determnstc, and statstcal methods have all been used for the last two decades. The majorty of exstng water qualty models are the mechanstc and are based on dfferental equaton derved by applyng mass balance (O Connor et al. 1973) for the water qualty parameters of nterest such as chemcal oxygen demand (COD), bo-chemcal oxygen demand (BOD), sedment oxygen demand (SOD), ammona (NH 3 ), suspended solds (SS) and dssolved oxygen (DO). These equatons have analytcal solutons only for specfc boundary condtons as n the case of the determnstc model of Mller and Jennng (1979) and the stochastc models of Thayer and Krustschoff (1967). In most of the practcal cases, however an analytcal soluton does not exst and therefore t s necessary to solve the dfferental equaton numercally usng fnte dfference schemes (James 1984). Moreover, n all the above procedures, lttle research has been carred out regardng the relatve effects of varous polluton sources n rver systems. Because many rvers are polluted n dfferent ratos of waste materals, ncludng domestc, agrcultural and ndustral wastes, regulatory agences need to determne the relatve effects of The basc dea behnd our approach was to evaluate the relatve effects of the waste load from varous nds of domestc and ndustral sources n the water envronment. Tranng of the neural networ was acheved by usng the presences of certan chemcal pollutants exceedng the permssble lmt of class II accordng to Chnese natonal water qualty standards (GB ). The standards values are based on the classfcaton of water n accordance wth ts partcular uses. The man polluton sources for the Hanjang Rver (Fg. 2) are organc materal dscharged from the domestc sources (DS) and ndustral sources such as pulp and paper (PP), fertlzers (FL), dyes and chemcals (DC) and metals (MET). The daly levels of ten pollutants, ncludng BOD 5, COD mn, NH 3 -N, suspended solds (SS), total phosphorous (TP), chlordes (Cl -1 ), fluordes (F -1 ), cyande (CN -1 ) and sulphde (S -2 ) were determned for a perod of 10 days after the dscharge of effluents from each polluton sources every day usng a one-dmensonal water qualty model (Zaheer and Cu 2002). The ey ssue n ths study was the formaton of an nput and output set of data for the tranng and evaluaton of the relatve effects of each pollutants source. The avalable nformaton maes t an excellent data set for use wth artfcal neural networs. EXISTING WATER QUALITY OF THE HANJIANG RIVER The Hanjang Rver s one of the man water resources n the Hanjang Rver basn fed by the Danjangou Reservor n the north of Chna. It flows from northwest to southeast and supples ample amounts of water for domestc, ndustral, and agrcultural actvtes n the basn. The transfer of water from the Danjangou Reservor to the north may cause water-qualty problems n the downstream area, partcularly durng the mddle flows. Moreover, the economc development actvtes n the area may pose polluton problems downstream from the man stream. Presently, the water qualty falls n the class II category accordng to the Chnese Natonal Standard, and the qualty s expected to worsen due to the dscharge of

3 Applcaton of artfcal neural networ for water qualty management Table 1 Appearance of chemcal pollutants durng the perod of ten days after the dscharge of pollutants n the rver smulated through 1D-water qualty model Number of days Chemcal Pollutants (CP) BOD 5 COD mn NH 3 -N NO 3 -N SS TP Cl F CN S ndustral, domestc, and agrcultural wastewater from varous ponts and lateral sources (trbutares) along the man stream of the Hanjang Rver (Zaheer 2000). It s therefore necessary to evaluate the relatve effects of varous polluton sources on rver water qualty. Usng a bac-propagaton algorthm of a feed forward neural networ, evaluaton of the relatve effects of polluton sources was carred out for strategc plannng of water qualty management. The exstng water qualty of the Hanjang Rver was assessed based on Chnese Natonal water qualty Standards. Under the present condtons of flow, the water qualty at the downstream reaches of the Rver s categorzed as class II. Water qualty can satsfy the requrements of water for varous uses except n a few places where t falls to class III or IV. Table 1 shows the permssble lmts for the varous nds of water qualty parameters n fve dfferent classes (NWQS 1996). ANN Methodology A typcal neural networ s composed of a number of elements called nodes and the connected pathways lnng these nodes. The nodes are the computatonal element of the networ and are usually referred to as neurons, thus reflectng the orgn of the neural networ methodology n modelng the bologcal neural networ n the human bran. A neural networ s descrbed as a lnear relatonshp between a set of nput patterns (waste characterstcs or polluton sources) and the networ output (appearance of chemcal pollutants). Assume that C p chemcal pollutants or water qualty parameters, whch are detected n the rver. Specfc chemcal pollutants that exceed the permssble lmts of water qualty standards on a daly bass determne the polluton of the rver. The polluton of a rver durng the th days can be descrbed by a polluton pattern as Cp=[p1,p2,..pn], where each element of Cp s defned by p, for whch the value s taen as 1 f the th C p exceeds the permssble lmt durng th days otherwse 0 (Table 2). Smlarly, assume that the rver receve the waste of Ws polluton sources. The contrbuton of polluton source s determned by the presence of chemcal pollutants n the waste, whch can be descrbed by waste pattern as Wsj=[w1j, w2j, wnpj] where each element of Wsj s defned by Wsj, for whch the value s taen as 1 f th Cp s detected n the jth polluton source otherwse 0 (Table 3). Usng ths nput and output pattern, the neural networ s used to smulate the daly appearance of pollutants exceedng the permssble lmts. The polluton pattern of each correspondng day s formed by the combnaton of polluton patterns of the prevous day plus waste patterns for all sources. Ths mples the formaton of an nputoutput pattern used for tranng of the neural networ wth respect to set (x, y ), ( as no. of days), where nput 0 1 patterns x are, [ ] 2 wns x = x x x... x, = 1,2..., m 1, and Polluton Sources Pulp & paper Fertlzers Dyes & Chemcals Metals Domestc Sources Table 2 Presences of chemcal pollutants released from dfferent polluton sources PP FL DC MET DS Chemcal Pollutants BOD 5 COD mn NH 3 -N NO 3 -N SS TP Cl F CN S

4 Zaheer and Ba Table 3 Characterstcs matrx for the polluton of the rver by fve polluton sources Polluton sources PP FL DC MET DS PP FL DC MET DS smlarly the y patterns are y = Cp + 1, = 1,2..., m 1. After tranng, the networ s able to smulate the daly appearance of pollutants n the rver. Wth ths networ, the relatve effects of polluton sources are evaluated by the smple formaton of outputs when varous combnatons of polluton sources are actve. The nput pattern formaton for the neural networ provdes the rules for decson-mang. Based on the daly nput of waste patterns nto the neural networ, the tranng of the neural networ wll assgn a set of weghts to each of the polluton sources, whch then determnes the relatve effects of the polluton sources. Performance of the neural networ s acheved by gettng the output whle consderng the contrbuton of a sngle polluton source under the assumpton that all the other polluton sources are nactve. Ths output s generally dstrbuted to a number of lned pathways to provde nput to the other neurons, wth each of these lned pathways transmttng all nformaton from the contrbutng neuron output. In the present study, the neural networ contaned one layer of a hdden unt. Therefore, a feed forward neural networ a used. However, a mult-layer feed forward neural networ may also be used though t requres not only a large amount of computatonal tme but also results over tranng, whch s due to the avalablty of less amount of nput data. The performance of an over-traned neural networ s generally poor compared wth a well-traned neural networ (Masters 1993). Each neuron has a state or actvty level that s determned by the nput receved from the other unt n the networ. Each nput sgnal s weghted,.e. t s multpled by a weght value of the correspondng nput lne. In the hdden and output layer, the net nput to unt s wrtten n the followng form: x = n j= 1 y w j + b where b s the bas of the unt, y j s the output from unt j, ( w 1,w 2..w n ) s the synaptc weght vector of unt I, and n s the number of neurons n the layer precedng the layer ncludng unt. The bas of unts descrbes the systematc dstorton n the results that arses from neglectng certan factors. The weght sum x, whch s referred to as the ncomng sgnals of unt, s passed through a nonlnear actvaton functon or transfer functon to produce the output sgnals, y. The most commonly used actvaton functon n constructon of the ANN s the sgmod functon, whereas the sgmod functon s assumed as a contnuous range of values from 0 to 1 whle consderng the unt value of the shape factor, defned by y (1) = 1 1+ exp( x ) (2) Testng sample Tranng sample RMSE No. of traton (Epoch) Fg. 3 Root mean square error (RMSE) n bac propagaton learnng

5 Applcaton of artfcal neural networ for water qualty management Moreover, t s evdent from Equaton 2 that the output from the neural networ model s always between 0 and 1. Before tranng, the synaptc weghts w j are ntalzed by a small real number obtaned from a random number generator. Tranng s performed usng a bac-propagaton algorthm (Rumelhart et al. 1986). Durng tranng of the networ by the bac-propagaton algorthm, the error functon s calculated to produce the desred output y when startng wth nput x. Approxmately 100 epochs are tred for the systematc changes n the synaptc weght snce the desred output s acheved wthn a gven tolerance. The error functon s calculated as the sum of ndvdual RMS error for each pattern to modfy the weghts accordngly before the next teraton by 1 RMS ( ) 2 2 = ( yˆ( ) y( )) (3) where y ˆ( ) s the actual state of the output n response to the state of nput, x(), whle y() s the desred output unt. The sze of the step taen down the error surface s determned by the learnng rate, α. Learnng s thus reduced to a mnmzaton procedure of error measurement. After the presentaton of (+1)th nputs, each weght s computed usng the followng equaton: E w j ( + 1) = η α w j ( ) (4) w where η s a learnng rate,.e. the fracton by whch the global error s mnmzed durng each teraton, and α s a constant (momentum term) that determnes the effect of prevous weght change on the current weght change. Much teraton of data s requred to guarantee the convergence of nown values towards ther desred values. The ntal connecton of synaptc weghts and the basng threshold of the neural networ are generated to be randomly dstrbuted over the nterval (-0.4 to +0.4). After 100 cycles (no. of teraton), the root mean square tranng error s reduced to below 0.01 and the estmated values converge towards ther desred values. The appearance of chemcal pollutants n the rver s smulated by a feed forward neural networ wth n = n 1+ n ) nputs, p ( s n h = n = n p ( 1+ ns ) hdden unts and, n o =n p output unts because the number of nput and output unts are specfed by the applcaton and because the number of hdden unts s free choce allowed by neural networ structure. Fgure 3 shows the monotonc decrease n the RMS tranng error. Ths traned neural networ s used for evaluaton of the relatve effects of polluton sources n the rver system. The computatonal program was created n a FORTRAN envronment and computed wth an Intel Pentum processor. RESULT AND DISCUSSION The performance of the neural networ n the Hanjang Rver was evaluated assumng that the contrbuton of onepolluton sources was actve, whle all other were elmnated. Such an nput pattern s called a pseudo-nput pattern that results when one of the jth polluton sources s actve. The contrbuton of any partcular polluton source s evaluated by observng the dfference between ts correspondng pseudo-polluton pattern and the real one. A characterstcs number s assgned to each polluton source,.e. Nj, whch s obtaned by countng the outputs y (j) and the expected output y. If D (j) s the dstance between y (j) and y, then the characterstc numbers, N, are equvalent to the number of D (j) that are nonzero. Clearly, 0 <. N j < m-1 for all j=1,2,.,ns. The relatve effects of each polluton source can be determned by comparng the characterstc number of polluton sources N j,, j=1,2,..n s, whch ndcates that the smaller the characterstc number, the stronger the effects of the correspondng polluton source (Table 4). In the resultng characterstcs matrx, the dagonal element provdes the bass for evaluaton of the polluton sources under the condton where one polluton source s actve. Accordng to Table 3, DS > DC > MET = PP > FL, whch ndcates that DS s more responsble for rver polluton then DC, whle MET and PP are equally responsble for rver polluton. The polluton source FL shows the least effects on rver polluton compared wth the other sources. Moreover, a comparson of the results obtaned from the artfcal neural networ and those of actual observed data source of the Hanjang Rver show the relatve effects of varous polluton sources. From the results obtaned, the artfcal neural networ model appears to be a useful tool n the decson-mang process n water envronmental polluton control. The greatest dffculty s n determnng the approprate model nput and the relablty of data for problems of a smlar nature. The basc dea for usng the nformaton source of the Hanjang Rver basn n ths study was to explore the usefulness of an artfcal neural networ n a water envronmental applcaton. CONCLUSIONS In the present study, a new approach to an artfcal neural networ was appled to water qualty management for envronmental polluton control. The obtaned results suggest that ANN s a useful tool for evaluatng the relatve effects of pollutants n a rver system durng the decsonmang process. Data for the Hanjang Rver basn were used based on the hypothetcal assumpton of fndng a new approach for an artfcal neural networ applcaton. In the curve RMS error versus the number of teratons, a

6 Zaheer and Ba temporary mnmum can be recognzed as a phase n whch an error s vrtually constant for a long tranng tme after ntal learnng. Although ANN technques nvolve a datadrven approach, t s mportant to fnd the domnant model nputs that help to reduce the networ sze and ultmately reduce the tranng tme and ncrease the generalzaton ablty of a neural networ for a gven set of data. Moreover, ths study may also provde gudelnes for evaluatng the relatve effects of trbutary dscharges nto the man stream for rver basn plannng and water polluton control. The neural networ smulaton outcomes show the relatve effects of pollutants whle consderng two actve polluton sources. Ths wor may lead to the future applcaton of a artfcal neural networ requrng a more complex bacpropagaton learnng algorthm and even more tranng tme. It s hghly recommended that further resources be nvested n such an artfcal neural networ applcaton so as to develop a promsng decson-mang system for water qualty management. ACKNOWLEDGEMENT Support for ths wor s provded n the part by fundng from the Natonal Natural Scence Foundaton Commttee (NNSFC) for the project No REFERENCES Charaborty, K., Mehrotra, K., Mohan, C. K, and Rana S. (1992). Forecastng the behavor of multvarate tme seres usng neural networ. Neural Networs. 5: Chng, G. W. and Chh, S. L. (1998). A neural networ approach to mult-objectve optmzaton for water qualty management n a Rver Basn. Wat. Res. Res.. 34(3): James, A. (ed.) (1984). An ntroducton to water qualty modelng. John Wlly and Sons, NY. Holger, R. M. and Graeme, C. D. (1996). The use of artfcal neural networs for the predcton of water qualty parameters. Wat. Res. Res.. 32 (4): Karayanns, N. B. and Venetasanopoulos, A. N. (1990a). Artfcal Neural Networ: Learnng Algorthm, performance Evaluaton and Applcaton. Kluwer Academc Publshers, London. Karayanns, N. B. and Venetasanopoulos, A. N. (1990b). Applcaton of neural networ to envronmental protecton. Proceedng of Internatonal Neural Networ Conference, Pars, France, Karayanns, N. B. and Venetasanopoulos, A. N. (1991). Fast learnng algorthm for neural networ (ed) Elesver Publshers, Holland. Lachtermacher, G. and Fuller, J. D. (1994). Bac propagaton n hydrologcal tme seres forecastng n stochastc and statstcal methods n hydrology and envronmental engneerng. 3: Kluwer academy, Norwell Mass. Masters, T. (1993). Practcal neural networ recpes n C++, Academc press Inc. San Dego, CA. Mller, J. E. and Jennngs, M. E. (1979). Modelng ntrogen oxygen of Chattahoochee Rver. Journal of Envron. Engn. Dv., ASCE. 105: NWQS (1996). Natonal Water Qualty (GB ) and Integrated Waste Water Dscharge (GB 8978) standard. Envronmnetal Protecton Bureu of Chna. O Connor, D. J., Thomson, R. V. and Dtoro, D. M. (1973). Dynamc water qualty forecastng and management, EPA 660/ , Proj. No. R , USA. Rumelhart, D. E., Hnton, G. E. and Wllam, R. J. (1986). Learnng representaton by bac-propagatng error. Nature. 323: Schzas, C. N., Pattcjs, C. S. and Mchacldes, S. C. (1994). Forecastng mnmum temperature wth short tme length data usng artfcal neural networ, Neural Networ World, 4 (2): Sovan, L G., Martxu A. and Graudel, J. (1999). Predcton of stream ntrogen concentraton from watershed features usng neural networ, Wat. Res.. 33(16): Thayer, R. P. and Krutschof, G. (1967). Stochastc model for BOD and DO n stream. J. San. Engn. Dv., Proc. Am. Soc. Cv. Eng.. 93: Wen, C. G and Lee, C. S (1998). A neural networ approach to mult-objectve optmzaton for water qualty management n a rver basn. Wat. Resources Res.. 34(3): Wndsor, C. G. and Hacer, A. H (1990). Multvarate fnancal ndex predcton-a neural networ study, n Internatonal Neural Networ Conference, Vol 1, Kluwer academy, Norwell Mass. Zaheer, I. (2000). Development of One dmensonal water envronmental polluton control model usng numercal soluton of advecton dsperson equaton (a case study of Hanjang rver basn), M.Sc thess, Hoha Unversty Nanjng, Chna. Zaheer, I. and Cu, G. B. (2002). Smulaton of pollutants n the Rver system usng fnte dfference method. Journal of Sedment Research, 17(2):