1. Surface Water Quality Prediction by Artificial Neural Network - Presented by AMIT KUMAR JHA Reg. No. - 2010EN07 Under the Supervision of: Prof. S. C. Prasad Dr. R. M. Singh Civil Engineering Dept. MNNIT Allahabad

2. OUTLINE Introduction Objectives Literature Review Materials and Methods – Methodology – Sampling & Data Collection ANN Modelling Comparison & Validation Conclusions References

3. Introduction Water quality modeling involves the prediction of water pollution of using mathematical simulation techniques. The present study of Water Quality Prediction is based on the analysis of samples of water collected from Ganga River from various locations in Allahabad city, India. The water quality parameters were analysed using statistical and soft computing techniques used to predict the Water Quality.

4. Cont….. Mathematical models are generally complex & pose difficulty in implementation in real time systems. Additionally, they fail to predict the future parameters from current & past measurements. The ANN based model can reveal hidden relationships in the historical data, thus facilitating the prediction and forecasting of water quality. Moreover, the soft computing techniques are flexible enough to accommodate additional constraints that may arise in the application.

5. Objectives Prime Objective : To develop water quality prediction model for Ganga river flow water. Specific Objectives : (i)To develop water quality prediction models using Artificial Neural Network. (ii)To compare Conventional & ANN models for water quality prediction. (iii)To evaluate the performance of the developed methodology.

6. LITERATURE REVIEW Banerjee et. al. (2008) done assessment on the water quality characteristics of River Ganga at Kolkata Region, India using Water Quality Index and ANN simulation method. M. Golabi et. al. (2006)used ANN in River Water quality Modelling of Karnoon river in Iran. Hafizan Juahir et. al. (2005) compared ANN models with conventional models for predicting Water Quality Index (WQI). Ali Najah et. al. (2001) predict the of Johor River Water Quality Parameters Using ANN.

7. Cont……. Huiqun M and Ling L, 2008. Water quality assessment using artificial neural network. International Conference on Computer Science and Software Engineering. Washington, DC, USA. Khazirpoor M, Hamzeh S, Jael A, 2011. Simulated EC values the Karun River using artificial neural networks, 3th National Conference on management of Irrigation and Drainage. Kunwar P, Singh AB, Amrita M and Gunja J, 2009. Artificial neural network modelling of the river water quality—A case study. Ecol Model 220:888–95. M. Abdul Zali et. al. (2005) developed a ANN based model to predict water quality of Kinta river in Malaysia.

8. Methodology Water Quality Prediction Model (i)Conventional Techniques  Correlation Analysis  Regression Analysis  Multi linear Regressive Models (ii)Soft Computing Techniques Artificial Neural Network (ANN) Models  Single Step Prediction Model  Multi Step Prediction Model

9. Artificial Neural Network (ANN) Models The most popular predictive model usually applied to model non-linear environmental relationship is the Artificial Neural Network (ANN). An ANN is composed of a large number of simple processing units, each interacting with others via excitatory or inhibitory connections. Three different layers can be distinguished: (i) An input layer (ii) Hidden layer (one or more) (iii) Output layer

10. Cont….. In an ANN, one of main tasks is to determine the model input variables that significantly affect the output variable(s). The choice of input variables for the present neural network modelling is based on a statistical correlation analysis of the field data, the prediction accuracy of water quality parameters, and the domain knowledge. The ANN technique is flexible enough to accommodate additional constraints that may arise in the application. Moreover, The ANN model can reveal hidden relationships in the historical data, thus facilitating the prediction and forecasting of water quality.

11. A typical multilayer perceptron ANN architecture

12. A typical ANN architecture

13. Sampling Strategies for Data Collection Data Collection for Model development Selection of Monitoring Stations Selection of Water quality parameters Sampling Period & Frequency of Sampling Statistics of Water quality parameters & their variation

14. Data Collection for Model development The data employed for proper model development is taken from PhD Thesis of Dr. Satendra Nath, who monitored the water quality of Ganga River for two years (Jan 2004 – Feb 2006).

15. Sampling Stations Mahaveerpuri (ST - 1) Dashawamedh Ghat (ST - 2) Ramghat (ST - 3) Sangam (ST - 4) Right bank of Sangam (ST - 5) Baksi Bandh (ST - 6)

17. Selection of Water Quality Parameters Physical Parameters :  Temperature  pH  Total Solids (TS)  Turbidity Chemical Parameters :  Alkalinity  Hardness  Chloride  Sulphate  Dissolved Oxygen (D. O.)  Bio-chemical Oxygen Demand (B. O. D.) Biological Parameters :  Fecal Coli-form Count (MPN)

18. Sampling Period & Frequency of Sampling MonthDate of SamplingDay of SamplingSampling Period December 201111 th DecSaturday- 26 th DecMonday15 days January 201210 th JanTuesday15 days 17 th JanTuesday07 days 25 th JanWednesday08 days February 201209 th FebThursday15 days 24 th FebFriday15 days March 201210 th MarSaturday15 days 25 th MarSunday15 days

19. Sampling Station - 1 Date of Sampli ng pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e D. O.BOD MPN/ 100ml 11 th Dec 7.6818.0015.00460.00210.00180.0048.0024.009.005.90 9500.0 0 26 th Dec 8.2022.0018.00325.00210.00260.0052.0026.008.106.10 4200.0 0 10 th Jan 7.7531.00260.00320.00175.00115.0088.0027.005.801.70 1800.0 0 17 th Jan 8.5825.0029.00440.00160.00152.0065.0054.006.704.30 2100.0 0 25 th Jan 8.4420.0013.00334.00242.00230.0054.0024.008.606.00 5000.0 0 09 th Feb 8.0024.806.00960.00194.00144.0036.0032.006.103.80 2900.0 0 24 th Feb 7.9830.4010.00442.00220.00152.0038.0024.006.404.20 4000.0 0 10 th Mar 7.9929.00380.00 216.00148.0056.0024.007.403.80 7000.0 0 25 th Mar 7.6222.008.00260.00238.00144.0064.0030.009.203.307000.0 0

20. Sampling Station - 2 Date of Sampli ng pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e D. O.BOD MPN/ 100ml 11 th Dec 8.0526.0028.00580.00284.00180.0052.0026.008.401.50900.00 26 th Dec 8.7614.0013.00500.00248.00184.0076.0024.008.905.90 9000.0 0 10 th Jan 8.3531.0010.00480.00190.00130.0048.0036.005.604.60 1500.0 0 17 th Jan 7.9530.6080.00540.00158.00110.0052.0029.006.404.00 5000.0 0 25 th Jan 8.1029.0020.00320.00180.00164.0058.0020.006.203.80 4000.0 0 09 th Feb 8.7621.0016.00410.00190.00164.0058.0014.008.104.90 1700.0 0 24 th Feb 8.2824.0010.00870.00178.00150.0028.0020.007.903.90 2100.0 0 10 th Mar 6.4430.00 320.00200.00144.0050.0012.006.402.40 1400.0 0 25 th Mar 8.6627.00343.00540.00198.00150.0055.0018.005.601.106000.0 0

21. Sampling Station - 3 Date of Sampli ng pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e D. O.BOD MPN/ 100ml 11 th Dec 8.4220.0010.00200.00236.00224.0052.0015.009.304.10 11000. 00 26 th Dec 8.3018.00 500.00200.00185.0080.0016.008.304.50 4000.0 0 10 th Jan 8.0127.00 320.00242.00202.0038.0040.007.505.30 1400.0 0 17 th Jan 8.1231.0060.00310.00148.00132.0082.0015.007.101.70900.00 25 th Jan 8.2022.0034.00280.00212.00150.0060.0028.008.702.40 1400.0 0 09 th Feb 8.7219.0022.00400.00284.00260.0060.0018.008.705.00 12000. 00 24 th Feb 8.7025.607.00800.00172.00138.0048.0038.005.90 2400.0 0 10 th Mar 8.1431.0016.00464.00194.00128.0032.0026.006.602.80 2700.0 0 25 th Mar 8.1428.00120.00378.00220.00144.0058.0022.007.003.204000.0 0

22. Sampling Station - 4 Date of Sampli ng pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e D. O.BOD MPN/ 100ml 11 th Dec 8.4420.0051.00460.00308.00220.0044.0016.008.804.30 9000.0 0 26 th Dec 8.1220.0015.00520.00198.00190.0055.0028.009.105.50 2100.0 0 10 th Jan 8.1230.0018.00340.00208.00186.0040.0036.007.203.80 1700.0 0 17 th Jan 8.1630.0014.00190.00 120.0038.0034.006.400.90 1200.0 0 25 th Jan 8.1022.005.00320.00252.00190.0070.0024.004.302.00 1200.0 0 09 th Feb 8.4319.007.00442.00170.00128.0070.0022.008.802.90 14000. 00 24 th Feb 8.6027.3012.00880.00180.00124.0036.0028.005.60 2100.0 0 10 th Mar 7.8530.00170.00620.00176.00120.0060.0011.005.803.80 5000.0 0 25 th Mar 8.0429.00100.00348.00198.00154.0060.0028.006.803.402700.0 0

23. Sampling Station - 5 Date of Sampli ng pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e D. O.BOD MPN/ 100ml 11 th Dec 8.1220.0080.00380.00230.00206.0056.0032.008.103.90 10000. 00 26 th Dec 8.3514.4016.00860.00202.00162.0040.0024.006.005.00 17000. 00 10 th Jan 8.0830.0050.00524.00172.00130.0056.0022.005.203.80 2100.0 0 17 th Jan 8.3430.50160.00520.00106.0088.0032.0060.006.605.00 7000.0 0 25 th Jan 8.3423.0014.00344.00190.00178.0060.0018.007.804.20 5000.0 0 09 th Feb 8.2822.0012.00400.00230.00180.0060.0018.008.804.80 1400.0 0 24 th Feb 8.6028.006.00580.00188.00120.0024.0016.006.603.00 1400.0 0 10 th Mar 7.4030.008.00440.00184.00134.0034.0020.007.203.00900.00 25 th Mar 7.8928.00380.00496.00180.00170.0030.00 7.803.403000.0 0

24. Sampling Station - 6 Date of Sampli ng pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e D. O.BOD MPN/ 100ml 11 th Dec 8.0421.0019.00 1280.0 0 170.00128.0040.0059.006.004.20 18000. 00 26 th Dec 7.8915.0021.00 1100.0 0 185.00165.0075.0020.009.206.70 13000. 00 10 th Jan 8.2427.0028.00380.00270.00224.0062.0020.007.506.00 5000.0 0 17 th Jan 7.8532.00350.00400.00182.00146.0072.0030.005.001.20 2700.0 0 25 th Jan 8.4626.0060.00460.00152.00126.0086.0032.007.504.60 3000.0 0 09 th Feb 8.3920.0022.00390.00220.00190.0080.0018.008.005.00 18000. 00 24 th Feb 8.5123.0011.00580.00220.00198.0072.0021.008.204.80 1300.0 0 10 th Mar 8.0131.0016.00300.00200.00168.0034.0016.006.103.80 1200.0 0 25 th Mar 7.9829.00400.00300.00204.00116.0052.0018.007.602.809000.0 0

25. ANN Modelling Correlation Matrix Parameters selected for Model Development ANN Water Quality Models (i)Hardness ANN Model (ii)Turbidity ANN Model (iii)D.O. ANN Model (iv)BOD ANN Model

26. XYXY pHTemp. Turbidit y Total Solids Alkalini ty Hardne ss Chlorid e Sulphat e DOBODMPN pH1 Temp. -0.2 1 Turbidit y -0.20.4 1 Total Solids -0.1 0.1 1 Alkalini ty 0.1-0.3-0.1-0.4 1 Hardne ss 0.2-0.4-0.3 0.7 1 Chlorid e 0.1-0.10.0-0.20.2 1 Sulphat e 0.1-0.20.0 0.2 0.0 1 DO 0.2-0.7-0.30.00.30.40.10.3 1 BOD 0.3-0.4-0.20.0 0.20.10.3 1 MPN 0.2-0.30.0 -0.10.0 0.10.3 0.51

27. Parameters selected for Model Development Output (Y)Input (X) HardnessTemp. (-)Alkalinity (+) TurbidityHardness (-)Temp. (+) D.O.Temp. (-)Alkalinity (+) BODTemp. (-)pH (+)D.O.(+)

28. ANN Models Output (Y)Input (X) Hardness Temp. (-) Alkalinity (+) Temp. (-)Alkalinity (+) Output (Y)Input (X) Turbidity Hardness (-) Temp. (+) Hardness (-)Temp. (+)

29. Output (Y) Input (X) BOD Temp. (-) pH (+) D.O.(+) Temp. (-)pH (+) Temp. (-)D.O.(+) pH (+)D.O.(+) Temp. (-)pH (+)D.O.(+) Output (Y)Input (X) D.O. Temp. (-) Alkalinity (+) Temp. (-)Alkalinity (+)

30. DO ANN Model STTrainingTesting N - nRRMSEAAREMEN - nRRMSEAAREME 1 3-50.950.440.0490.903-50.930.480.0480.85 2 3-80.920.410.0420.893-80.910.510.0390.86 3 3-60.960.440.0390.913-60.940.480.0360.87 4 1-80.920.510.0520.841-80.581.040.0990.20 5 3-6 0.940.470.0420.86 3-6 0.770.720.0750.62

31. BOD ANN Model STTrainingTesting N - nRRMSEAAREMEN - nRRMSEAAREME 1 7-5 0.921.340.3830.85 7-5 0.617.80.682-7.72 2 7-80.940.990.1750.877-80.724.10.591-2.31 37-6 0.961.120.2540.823-60.662.20.6520.29 47-9 0.911.270.3400.847-90.744.10.591-2.31 57-80.931.360.4290.797-80.693.70.5270.45

32. Hardness ANN Model STTrainingTesting N - nRRMSEAAREMEN - nRRMSEAAREME 1 3-8 0.7723.330.1310.59 3-8 0.6728.460.1630.47 2 3-90.8217.260.0980.693-90.7521.360.1210.58 3 3-5 0.7921.450.1130.64 3-5 0.7124.730.1450.67 4 3-7 0.8314.840.0890.73 3-7 0.7915.280.1030.69 5 3-8 0.7628.960.1420.55 3-8 0.6134.720.1820.42

33. Turbidity ANN Model STTrainingTesting N - nRRMSEAAREMEN - nRRMSEAAREME 1 3-50.62 23.330.1310.59 3-50.561.040.4390.20 2 3-70.7417.260.0980.693-70.754.150.591-2.31 3 3-6 0.6721.450.1130.64 3-60.723.850.623-2.31 4 3-5 0.5914.840.0890.73 3-50.642.20.6520.29 5 3-8 0.6428.960.1420.55 3-80.702.40.6440.35

34. Validation of ANN Models The best ANN Models developed from the historical data is evaluated with the data obtained during this year. For every ANN Model, a corresponding regressive model is developed & is being compared. It is found that ANN Models are better in predicting the water quality parameter than the conventional regressive models.

35. Regression Models D.O. STATION yx1x1 x2x2 Regression Equation 1D.O.Temp.Alkalinityy = 11.494 - (0.222 x 1 ) + (0.00781x 2 ) 2D.O.Temp.Alkalinityy = 7.977 - (0.123 x 1 ) + (0.0130x 2 ) 3D.O.Temp.Alkalinityy = 11.901 - (0.216x 1 ) + (0.00462 x 2 ) 4D.O.Temp.y = 13.435 - (0.226 x 1 ) 5D.O.Temp.Alkalinityy = 10.830 - (0.159 x 1 ) + (0.00449x 2 )

36. BOD STATION yx1x1 x2x2 x3x3 Regression Equation 1BODTemp.pHD.O.y = -12.405 + (0.00982x 1 ) + (1.397x 2 ) + (0.646x 3 ) 2BODTemp.pHD.O.y = 3.089 - (0.164x 1 ) + (0.860x 2 ) - (0.256x 3 ) 3BODTemp.pHD.O.y = 2.247 - (0.238x 1 ) + (0.950x 2 ) + (0.0122 x 3 ) 4BODTemp.pHD.O.y = -16.348 - (0.378x 1 ) + (4.577x 2 ) - (0.886x 3 ) 5BODTemp.pHD.O.y = -9.249 - (0.244 x 1 ) + (2.236 x 2 ) + (0.220x 3 )

37. Hardness STATION yx1x1 x2x2 Regression Equation 1HardnessTemp.Alkalinityy = 125.307 - (3.139x 1 ) + (0.553x 2 ) 2HardnessTemp.Alkalinityy = 101.521 - (2.429x 1 ) + (0.608x 2 ) 3HardnessTemp.Alkalinityy = 93.865 - (1.046x 1 ) + (0.464x 2 ) 4HardnessTemp.Alkalinityy = 53.442 - (1.397x 1 ) + (0.696x 2 ) 5HardnessTemp.Alkalinityy = 123.733 - (3.143x 1 ) + (0.563x 2 )

38. Turbidity STATION yx1x1 x2x2 Regression Equation 1TurbidityHardnessTemp.y = 97.218 - (0.989x 1 ) + (5.343x 2 ) 2TurbidityHardnessTemp.y = -44.030 - (0.144x 1 ) + (5.089x 2 ) 3TurbidityHardnessTemp.y = -70.318 - (0.0272x 1 ) + (5.556x 2 ) 4TurbidityHardnessTemp.y = -21.733 - (0.196x 1 ) + (4.896x 2 ) 5TurbidityHardnessTemp.y = 86.965 - (0.757x 1 ) + (3.626x 2 )

39. Evaluation of ANN Models

40. Cont……

41. Cont……..

42. Cont…….

43. Cont………

44. Cont………..

45. BOD

46. Cont…….

47. Turbidity

48. Cont…….

49. Cont………

50. Hardness

51. Conclusion ANN Models are better tools to predict the water quality parameters rather than conventional statistical models. D.O. ANN Model is the best model among the others because of higher value of Model Efficiency and lower value of RMSE, of predicted value predicted value. Stronger the correlation between the parameters, better will be the ANN Model developed and consequently better prediction of water quality parameter is obtained. The ANN models developed from the historical data is also better in predicting the present water quality data.

52. References (i ) Huiqun M and Ling L, 2008. Water quality assessment using artificial neural network. International Conference on Computer Science and Software Engineering. Washington, DC, USA. (ii) Khazirpoor M, Hamzeh S, Jael A, 2011. Simulated EC values the Karun River using artificial neural networks, 3th National Conference on management of Irrigation and Drainage. (iii) Kunwar P, Singh AB, Amrita M and Gunja J, 2009. Artificial neural network modeling of the river water quality—A case study. Ecol Model 220:888–95. (iv) Kuo Y, Liu C and Lin KH, 2004. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res 38: 148–58.

53. Cont…. (v) Lance E. Besaw, Donna M. Rizzo, Paul R. Bierman, William R. Hackett., (2010), “Advances in ungauged streamflow prediction using artificial neural networks”, Journal of Hydrology 386, 27–37. (vi) Menhaj MB. 2000. Fundamentals of neural networks. Amir Kabir University of Technology, Tehran. (vii) Michael AM, Kherpar SD, Sondhi SD, 2008. Water wells and pumps. McGraw-Hill, New Delhi (viii) Mahdizadeh M, 2004. Artificial Neural Networks and its application in civil engineering. Ebadi Publishing, 130 pp.

54. Thank You