Prediction of Municipal Solid Waste Generation by Use of Artificial Neural Network: A Case Study of Mashhad

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1 Int. J. Envirn. Res., 2(1): 13-22, Winter 2008 ISSN: Predictin f Municipal Slid Waste Generatin by Use f Artificial Neural Netwrk: A Case Study f Mashhad Jalili Ghazi Zade, M. 1 and Nri, R. 2* 1 Graduate Faculty f Envirnment, University f Tehran, Tehran, Iran 2 Department f Civil Engineering, University f Tarbiat Mdares,Tehran, Iran Received 15 Jan. 2007; Revised 20 July 2007; Accepted 30 July 2007 ABSTRACT: Accurate predictin f municipal slid waste s quality and quantity is crucial fr designing and prgramming municipal slid waste management system. But predicting the amunt f generated waste is difficult task because varius parameters affect it and its fluctuatin is high. In this research with applicatin f feed frward artificial neural netwrk, an apprpriate mdel fr predicting the weight f waste generatin in Mashhad, was prpsed. Fr this purpse, a time series f Mashhad s generated waste which have been arranged weekly, frm 2004 t 2007, was used. Als, fr recgnizing the effect f each input data n the waste generatin sensitive analysis was perfrmed. Finally, different structures f artificial netwrk were investigated and then the best mdel fr predicting Mashhad s waste generatin was chsen based n mean abslute errr (MAE), mean abslute relative errr (MARE), rt mean square errr (RMSE), crrelatin cefficient (R 2 ) and threshld statistics (TS) indexes. After perfrming f the mentined mdel, crrelatin cefficient (R 2 ) and mean abslute relative errr (MARE) in neural netwrk fr test have been achieved equal t and 3.18% respectively. Results pint that artificial neural netwrk mdel has mre advantages in cmparisn with traditinal methds in predicting the municipal slid waste generatin. Key Wrds: Waste Generatin, Artificial Neural Netwrk, Sensitive Analysis, Mashhad. INTRODUCTION Municipal slid waste (MSW) is the result f human activities. If an apprpriate management system isn t used fr this prblem, it may lead t envirnmental pllutin and jepardize the mankind s health. But it is t difficult t design such system because the nature f waste is quite cmplicated and hetergeneus. Recgnizing the quantity f generated waste is ne f the mst imprtant factrs fr perating the slid waste management system (SWMS), crrectly. Being aware f generatin quantity can be very effective fr estimating the amunt f investigatin in the field f machinery, nsite strage cntainers, transitin statins, dispsal capacity and prper rganizatin. There are different ways t estimate the waste generatin (WG) rates, which the mst prminent f them are lad-cunt analysis, weight-vlume analysis and materials-balance analysis. Hwever, these are the basic methds *Crrespnding authr: -rnri@mdares.ac.ir fr estimating the measure f generated waste, but they have sme disadvantages. Fr example lad-cunt analysis methd determines the rate f cllectin, nt the rate f prductin. Materialsbalance analysis methd als suffers frm many errrs if the surce f WG were in a giant size (like a city). On the ther part, traditinal methds fr estimating the amunt f generated slid waste are established, mstly, n the basis f sme elements such as ppulatin and scial-ecnmic factrs f ne sciety and they are cmputed accrding t generatin cefficient per persn. Since these cefficients change during the time, s they are useless devices fr ne dynamics SWMS. Beside in turisty cities like Mashhad, where fluctuatin f ppulatin and as its result, fluctuatin f WG is significant, they can t predict the amunt f generated waste, accurately. Fr these reasns, emplying new methds and advanced techniques can be useful fr cmputing 13

2 M. Jalili Ghazi Zade and R. Nri by means f this dynamic and nn-linear system. These methds mstly cnsist f sme mdels, classic statistics methds and many new techniques like time series methds and artificial neural netwrks. In this study, Artificial Neural Netwrk (ANN) was trained and tested t mdel weekly waste generatin (WWG) in Mashhad city f Iran. Input data, cnsist f WWG bservatin and the number f trucks which carry waste, were btained frm Mashhad s Recycling and Material Cnversin Organizatin. The ANN mdels are basically based n the perceived wrk f the human brain. The artificial mdel f the brain is knwn as ANN (Sahin, et al., 2005). ANNs were first intrduced in the 1940s (McCullch & Pitts, 1943). Interest grew in these tls until the 1960s when Minsky and Papert shwed that netwrks f any practical size culd nt be trained effectively (Minsky & Papert, 1969). It was nt until the mid-1980s that ANNs nce again became ppular with the research cmmunity when Rumelhart and McClelland rediscvered a calibratin algrithm that culd be used t train netwrks f sufficient sizes and cmplexities t be f practical benefit (Rumelhart & McClelland, 1986). Since that time research int ANNs has expanded and a number f different netwrk types, training algrithms and tls have evlved. Given sufficient data and cmplexity, ANNs can be trained t mdel any relatinship between a series f independent and dependent variables (inputs and utputs t the netwrk respectively). Fr this reasn, ANNs have been usefully applied t a wide variety f prblems that are difficult t understand, define, and quantify; fr example, in finance, medicine, engineering, etc. Recently, use f ANNs in management f MSW like a prpsed mdel based n ANN t predict rate f leachate flw rate in place f dispsal slid wastes in Istanbul, Turkey (Karaca & Ozkaya, 2006), predictin fr energy cntent f Taiwan MSW using multilayer perceptrn neural netwrks (Shu et al., 2006), HCl emissin characteristics and back prpagatin neural netwrks predictin in MSW/cal c-fired fluidized beds (Chi et al., 2005), recycling strategy and a recyclability assessment mdel based n an ANN (Liu et al., 2002) and predictin f heat prductin frm urban slid waste by ANN and multivariable linear regressin in the city f Nanjing, China (Dng, et al., 2003), have been becme in current. Als in the ther envirnmental prblems like air pllutin (Sahin, et al., 2005 ; Lu, et al., 2004 ; Lu, et al., 2006), surface water pllutin (Sah, et al., 2006 ; Shrestha & Kazama, 2007), the ANNs have been used. The results f these researches have shwn the high perfrmance f ANN in predictin f varius envirnmental parameters like prductin. MATERIALS & METHODS Accrding t final received reprts, Mashhad s ppulatin is abut 3 millin. In this city, municipal ministry is charged with the duty f MSW cllectin. In latest years, increasing f emigratin t this city has been caused in expanding the WG and as a result making a prblem fr the SWMS. Accrding t the Recycling and Material Cnversin Organizatin reprt, with prductin f 0.5 millin tns waste in 2006, Mashhad was ne f the biggest centers f WG in Iran. In the ther hand, the significant fluctuatins f WG as a result f high number f emigrants in this city have made many prblems fr SWMS. Accrding t Existed reprts the amunt f generated waste in Mashhad is between 1200 t 1900 tn/day, thus ffering an apprpriate mdel fr estimating the quantity f generated waste and its fluctuatin can be useful fr true prgramming and deciding which is made by related rganizatins. Effective factrs in the amunt f generated wastes are: gegraphical situatin, seasns, cllectin frequency, nsite prcess, peple s fd habits, ecnmic cnditin, recvery and reuse bundaries, existed law and peple s cultural cnditins. Since having seasnal patterns f generated waste can have an effective rle fr estimating the generated waste and its fluctuatin in ne city (especially in turisty city like Mashhad), s a time series mdel f WG has been made fr predicting the amunt f generated waste in Mashhad. In this mdel weight f waste in t+1 week (W t+1 ), is a functin f waste quantity in t (W t ), t-1 (W t-1 ),, t-11 (W t-11 ) weeks. The weekly fluctuatin f WG in Mashhad has been shwn in Figure 1. Anther input data, cnsist the number f trucks which carry waste in week f t (Tr t ). 14

3 Int. J. Envirn. Res., 2(1): 13-22, Winter Slid Waste (TON) Weeks Fig. 1. Weekly fluctuatin f waste generatin in Mashhad its wn. Specially, a signal x j at the input f synapse j cnnected t neurn k is multiplied by the synaptic weight w kj. Unlike a synapse in the brain, the synaptic weight f an artificial neurn may lie in a range that includes negative as well as psitive values. The neural mdels are basically based n the perceived wrk f the human brain. The artificial mdel f the brain is knwn as Artificial Neural Netwrk (ANN) r simply Neural Netwrks (NN). Neural Netwrks have many applicatins. Generally, hwever, the ANNs are a cellular infrmatin prcessing system designed and develped n the basis f the perceived ntin f the human brain and its neural system. Rapid, efficient prpagatin f electrical and chemical impulses is the distinctive characteristic f neurns and the nervus system in general. The neurns perate cllectively and simultaneusly n mst fr all data and inputs, which perfrms as summing and nnlinear mapping junctins. In sme cases they can be cnsidered as threshld units that fire when ttal input exceeds certain bias level. Neurns usually perate in parallel and are cnfigured in regular architectures. They are ften rganized in layers, and feedback cnnectins bth within the layer and tward adjacent layers are allwed. Strength f each cnnectin is expressed by a numerical value called a weight that can be updated. Als they are characterized by their time dmain behavir, which is ften referred as dynamics. In general, the neurn culd be mdeled as a nnlinear activated functin f which the ttal ptential inputs int synaptic weights are applied. It is assumed that synapses can impse excitatin r inhibitin but nt bth n the receptive neurn. The artificial mdel f neurn cnsists f three elements. These are: 1. A set f synapses r cnnectin links, each f which is characterized by a weight r strength f 2. An adder fr summing the input signals, weighted by the respective synapses f the neurn. 3. An activatin functin r transfer functins fr limiting the amplitude f the utput f a neurn. The neurn mdel can als include an externally applied bias, dented byb k. The bias has the effect f increasing r lwering the net input f the activatin functin depending n whether it is psitive r negative, respectively. Mathematically, the neurn k will be described by the fllwing equatins: m w = w. x (1) k kj j j Where are the input signals; are the synaptic weights f neurn. The activatin functin, dented by, defines the utput f a neurn which cnsiderably. Influences the behavir f the netwrk, net = u + b k k k ( ) (2) y = f net (3) Where is threshld value and is activatin functin. Three basic types f activatin functin are generally used in ANN. These are: 15

4 Predictin f Municipal Slid Waste Generatin Piecewise-linear functin 1 1, v f ( v ) = v, < v < , v 2 Threshld functin f ( v ) 0, v 0 = 1, v < 0 (4) (5) Sigmid functin 1 f ( v ) = av 1 + (6) e where is the slpe f the activatin functin. In this paper, neural netwrk is trained and tested using MATLAB 7.2. A three-layer neural netwrks that cnsist f an input layer, utput layer and ne hidden layer is used and structure f this netwrk is presented in Fig. 2. The mst ppular architecture fr a neural netwrk is a multilayer perceptrn (Bishp, 1995; Jain, et al., 2006). In this study, we used was the feed frward, multilayer perceptrn (MLP), which is cnsidered able t apprximate every measurable functin (Gardner and Drling, 1998). The main issue in training MLP fr predictin is the generalizatin perfrmance. MLP, like ther flexible nnlinear estimatin methds such as kernel regressin, smthing splines, can suffer frm either underfitting r verfitting (Culibaly, et al., 2000). In this situatin errr between training and testing results start t increase. Fr slving this prblem, Stp Training Apprach (STA) has been used. Data are divided int 3 parts in this methd. First part is related t netwrk training, secnd part fr stpping calculatins when errr f integrity start t increase and the third part that is used fr integrity f netwrk. In rder t evaluate the perfrmance f the ANN mdel fur statistical indices are used: the Mean Abslute Errr (MAE), the Mean Abslute Relative Errr (MARE), the Rt Mean Square Errr (RMSE) and crrelatin cefficient (R 2 ) values that are derived in statistical calculatin f bservatin in mdel utput predictins, defined as: 1 n p n i = 1 MAE = w w (8) Fig. 2. Structure f the three layers artificial neural netwrk In this Fig., the ellipse-shape prcessing units in all the layers represent artificial neurns. The mnitring data belnging t years is designed t meet the requirements f training and testing the neural netwrk. Varius ANN mdels are tested changing the number f neurns in the hidden layer between 4 and 26. All the data are nrmalized int the range{ 0.1,0.9 }. This is carried ut by determining the maximum and minimum values f each variable ver the whle data perid and calculating nrmalized variables using equatin (7). X nrm = ( X X ) + min ( X max X min ) (7) w w MARE = n w 1 n p i = 1 n 1 RMSE = ( w w ) R = 1 n 2 i = 1 n i = 1 n i = 1 ( w w ) p ( w w ) 2 ' 2 p 2 (9) (10) (11) Where w is the actual values f W t+1 with ' { i = 1, 2,..., n weeks} bservatins, w is the average f W t+1, n is the ttal bservatin number and w is the predicted W t+1 value. p 16

5 Int. J. Envirn. Res., 2(1): 13-22, Winter 2008 RESULTS & DISCUSSION Mashhad s Statistics Analysis f waste, during the different seasns between , is given in Table 1. Since the amunt f average and median is clse t each ther, s waste generatin in Mashhad has nrmal distributin amng the different seasns. Als significant amunt f Standard deviatin shws that generatin fluctuatin in different seasns f year. T knw the percentage f every independent variable s impact n generated MSW, the sensitivity analysis was perfrmed. Its results are shwn in Fig. 3. Accrding t Fig. 3, the generated waste in each week gt the mst effect frm W t, W t-3 and W t-6. This can be the result f peple s hbbies and ecnmic cnditins. T achieve the best ANN structure fr estimating generated waste, varius structures f feed frward ANN with three layers and different number f neurns in hidden layer was investigated. In this investigatin, Levenberg- Marquadte as a training functin and Tansig as a transfer functin were used. Finally, with cnsideratin n MAE, MARE, RMSE and R 2 apprpriate mdels were selected. The results f training and testing f ANN are given in Table 2. Accrding t Table 2 the best results were btained f ( ) and ( ) structures. These results are shwn in Figs. 4 t 11. Table 1. Statistics analysis f waste generatin in Mashhad (Tn) parameter Win.2004 Spr.2004 Sum.2004 Aut.2004 Win.2005 Spr.2005 Average St.dev. a Median Max Min Sum.2005 Aut.2005 Win.2006 Spr.2006 Sum.2006 Aut.2006 Average St.dev. a Median Max Min a Standard Deviatin 40.00% 35.00% 30.00% Impactin 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% W(t) W(t-3) W(t-6) W(t-11) Tr(t) W(t-4) W(t-5) W(t-9) W(t-2) W(t-1) W(t-10) W(t-7) W(t-8) Input Variables Fig. 3. Percentage f every input variables impact n generated municipal slid waste 17

6 M. Jalili Ghazi Zade and R. Nri ANN mdel structure Table 2. Results f training and testing steps f ANN TRAINING TESTING MAE MARE % RMSE R 2 MAE MARE % RMSE R Predicted Slid Waste (TON) R 2 = Observed Slid Waste (TON) Fig. 4. Scatter plt f bserved and predicted slid waste frm training f ANN mdel with structure ( ) Slid Waste (TON) Observatin Predictin (ANN-10) Weeks Fig. 5. Observed and predicted slid waste frm training f ANN Mdel with structure ( ) 18

7 Int. J. Envirn. Res., 2(1): 13-22, Winter Predicted Slid Waste (TON) R 2 = Observed Slid Waste (TON) Fig. 6. Scatter plt f bserved and predicted slid waste frm testing f ANN mdel with structure ( ) Slid Waste (TON) Observatin Predictin (ANN-10) Weeks Fig. 7. Observed and predicted slid waste frm testing f ANN Mdel with structure ( ) Predicted Slid Waste (TON) R 2 = Observed Slid Waste (TON) Fig. 8. Scatter plt f bserved and predicted slid waste frm training f ANN mdel with structure ( ) 19

8 Predictin f Municipal Slid Waste Generatin Slid Waste (TON) Observatin Predictin (ANN-16) Weeks Fig. 9. Observed and predicted slid waste frm training f ANN Mdel with structure ( ) Predicted Slid Waste (TON) R 2 = Observed Slid Waste (TON) Fig. 10. Scatter plt f bserved and predicted slid waste frm testing f ANN mdel with structure ( ) Slid Waste (TON) Observatin Predictin (ANN-16) Weeks Fig. 11. Observed and predicted slid waste frm testing f ANN Mdel with structure ( ) 20

9 Int. J. Envirn. Res., 2(1): 13-22, Winter 2008 As mentined befre, n the base f examined criteria (MAE, MARE, RMSE and R 2 ), mdels with the structures f ( ) and ( ), have better results in cmparisn with ther mdels. The first mdel ( ) shws the better results than the secnd mdel ( ) based n MAE and MARE. The R 2 index is same fr these mdels, but RMSE index fr the secnd mdel shws better results in cmparisn with the first mdel. Since these criteria shw the average f errr in mdel and dn t give any infrmatin abut the errr distributin, s t test the rbustness f the ANN mdel, it is imprtant t test the mdel using sme ther perfrmance evaluatin criterin such as threshld statistics (TS) (Jain and Indurthy, ). The TS nt nly give the perfrmance index in terms f predicting WG but als the distributin f the predictin errrs. The TS fr a level f x% is a measure f the cnsistency in frecasting errrs frm a particular mdel. The TS are represented as TS x and expressed as a percentage. This criterin can be expressed fr different levels f abslute relative errr frm the mdel. It is cmputed fr the x% level (TL) as: Yx TS x = 100 (12) n Where in equatin (12), Y x is the number f cmputed WG (ut f n ttal cmputed) fr which abslute relative errr is less than x% frm the mdel. Fig. 12 shws the distributin f errrs at different threshld levels fr first and secnd mdels. Cumulative Frequency (%) ANN-10 ANN Treshld Errr Level (%) Fig. 12. Abslute relative errr fr the structures ( ) and ( ) in the testing step f artificial neural netwrk Accrding t Figure 12, the maximum f abslute relative errr (ARE) fr fifty percentage f predicted W t+1 in the first mdel is less than 2.59%, hwever it is less than 2.45% fr the secnd mdel. The ARE fr ninety percentage f predicted Wt+1 in the first mdel is less than 7.69%, but fr the secnd mdel this value is less than 7.51%. S the mdel with the structure f ( ) has better results in cmparisn with the ther mdel ( ). CONCLUSION Accurate predictin f WG plays an imprtant rule in the MSWMS. Therefre the gal f this research is ffering a suitable mdel t predict this quantity. In this paper was used the feedfrward artificial neural netwrk fr the predictin f weekly waste generatin f Mashhad city. At the first, by using f ANN with the ne hidden layer and changing the number f neurns f the layer, different mdels were created and tested. Then accrding t applied index in this paper (MAE, MARE, RMSE and R 2 ), structures with 10 and 16 neurns in the hidden layer, were selected as the suitable mdels. Finally, based n TS index, structure with 16 neurns in the hidden layer was chsen fr the predictin f waste generatin in Mashhad. 21

10 M. Jalili Ghazi Zade and R. Nri REFERENCES Bishp, C. M., (1995). Neural netwrk fr pattern recgnitin. (Oxfrd University Press, New Yrk) Chi, Y., Wen, J. M., Zhang, D. P., Yan, J. H., Ni, M. J. and Cen, K. F., (2005). HCl emissin characteristics and BP neural netwrks predictin in MSW/cal c-fired fluidized beds. J. Envirn. Sci., 17, Culibaly, P., Anctil, F. and Bbee, B. (2000). Daily reservir inflw frecasting using artificial neural netwrks with stpped training apprach. J. Hydr., 230, Dng, C., Jin, B. and Li, D. (2003). Predicting the heating value f MSW with a feed frward neural netwrk. Waste Manag., 23, Gardner, M. W. and Drling, S. R., (1998). Artificial neural netwrks (the multilayer perceptrn)-a review f applicatins in the atmspheric sciences. Atms. Envirn., 32, (14), Jain, A. and Indurthy, S. K. V. P., (2003). Cmparative analysis f event based rainfall-runff mdeling techniques-deterministic, statistical, and artificial neural netwrks. J. Hydr. Eng. ASCE., 8 (2), Jain, A. and Srinivasulu, (2006). Integrated apprach t mdel decmpsed flw hydrgraph using artificial neural netwrk and cnceptual techniques. J. Hydr., 317, Karaca, F. and Özkaya, B., (2006). NN-LEAP: A neural netwrk-based mdel fr cntrlling leachate flw-rate in a municipal slid waste landfill site. Envirn. Mdel. Sftware, 21, Liu, Z. F., Liu, X. P., Wang, S. W. and Liu, G. F., (2002). Recycling strategy and a recyclability assessment mdel based n an artificial neural netwrk. J. Mater. Prces. Tech., 129, Lu, H. C., Hsieh, J. C. and Chang, T. S., (2006). Predictin f daily maximum zne cncentratins frm meterlgical cnditins using a tw-stage neural netwrk. Atms. Res., 81, Lu, W. Z., Wang, W. J., Wang, X. K, Yan, S. H., Lam, J.C., (2004). Ptential assessment f a neural netwrk mdel with PCA/RBF apprach fr frecasting pllutant trends in Mng Kk urban air, Hng Kng. Envirn. Res., 96, McCullch, W. S. and Pitts, W., (1943). A lgical calculus f the ideas imminent in nervus activity. Bull. Math. Biphys., 5, Minsky, M. L. and Papert, S. A., (1969). Perceptrns. (MIT Press, Cambridge, MA.) Rumelhart, D. E. and McClelland, J. L., (1986). Parallel Distributed Prcessing: Explratins in the Micrstructures f Cgnitin, Vl. 1. MIT Press, Cambridge. Sahin, U., Ucan, O. N., Bayat, C. and Oztrun, N., (2005). Mdeling f SO 2 distributin in Istanbul using artificial neural netwrks. Envirn. Mdel. Assess., 10, Sah, G. B., Ray, C. and De Carl, E. H., (2006). Use f neural netwrk t predict flash fld and attendant water qualities f a muntainus stream n Oahu, Hawaii. J. Hydr., 327, Shrestha, S. and Kazama, F., (2007). Assessment f surface water quality using multivariate statistical techniques: A case study f the Fuji river basin, Japan. Envirn. Mdel. Sftware., 22, Shu, H. Y., Lu, H. C., Fan, H. J., Chang, M. C. and Chen, J. C. (2006). Predictin fr energy cntent f Taiwan municipal slid waste using multilayer perceptrn neural netwrks. J. Air and Waste Manag. Assc., 56,

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