1.Time Series Forecasting Using A.N.N. at Selective Stations of Brahmaputra Presentation of Work Done For Defense of Doctoral Thesis By Aniruddha Gopal Banhatti Research Scholar at N.I.T.K. Surathkal Guide Dr. Paresh Chandra Deka 1

2. INTRODUCTION Forecasting- A Major Requirement For Water Resources Planning Using Past Records of Daily Streamflow At Gauging Stations Pandu and Pancharatna, a Model for Forecasting Is Proposed Artificial Neural Network (ANN) Model Long Term Extrapolation Method Is Suggested 2 3. Problem Background Brahmaputra : A Himalayan River Snowmelt and Monsoons Both Sources of Streamflow Large Variation of Gradient Daily Streamflow Data Treated as Time Series The Data Show Large Range of Variation Non-Linear Data Non-Stationary Data 3 4. Longitudinal Section of Brahmaputra4 5. Artificial Neural Networks Soft Computing Technique Data Driven Model Black Box Model Successfully Used In Time Series Prediction Non Stationary Nature of Data Causes Loss of Accuracy of Prediction Pre Processing of Data Improves Prediction Accuracy 5 6. Study Area The Gauging Stations at Pancharatna and Pancharatna Near Guwahati are Taken as Study Area6 7. Research Objectives Study Effect of Preprocessing Techniques on Prediction Accuracy of ANNsStudy Forecasting for Time Series Using ANNs7 8. Specific Objectives Constructing ANNs Using Different Network Architectures Varying Number of Neurons Different Datasets Conducting Training of Networks Prediction by Networks for Unseen Datasets Performance Evaluation for Different Network-Dataset Combinations Outlining Procedure for Effective Long Term Extrapolation 8 9. Data Availability The data was available from Water Resources Department, Government of Assam, India, With help from Dr. P. C. Deka At Pandu station Daily Stream flow in m3/s Starting from 1/1/1980 to 30/12/1998 Total of 6939 data points. At Pancharatna station Daily Stream flow in m3/s Starting from 1/1/1980 to 31/12/1999 Total of 7305 data points. 9 10. Classification and Selection of Data Data points are arranged for one day, two day and three day lagged dataBeginning 2/3rd for Training and Validation DatasetRemaining 1/3rd for Testing Dataset 10 11. Classification and Selection of Data At Pancharatna 4624 Data Points for Training and Validation 2312 Data Points for Testing At Pancharatna 4868 Data Points for Training and Validation 2434 data points for Testing 11 12. Model Evaluation Criteria Root Mean Square Error (R.M.S.E.)Mean Absolute Percentage Error (M.A.P.E.)Where X = Observed Value Y = Computed Value N = No. of Observations 12 13. Results and Discussion PANDU STATION Raw Data One day lag Two day lag Three day lagLog Transformed Data One day lag Two day lag Three day lagLog plus First Difference Data One day lag Two day lag Three day lag 13 14. Log-Transformed Dataset One Day Lag Analysis (Pandu) ANN ARCHITECTURES Number of NeuronsLOGSIGPURELINTANSIGRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST11495.29.511834.726.152121.2519.352261.019.6111869.0252.5212969.9661.08217100.5370.8217645.5177.732121.1619.22270.029.566859.2674.895968.0540.6531185.893.491725.934.932121.7619.032283.469.521459.069.371878.936.1541199.293.461765.994.872121.4719.082279.119.531310.133.911817.125.0251186.973.621731.274.942126.2619.092294.699.6313629.0655.8814259.8363.3661198.93.471759.524.912121.5419.362258.499.61189.073.71729.664.9871184.173.531728.814.952121.7719.112273.89.521195.893.811749.934.9881184.923.41735.994.892121.2219.152274.739.552607.343.652420.875.0291188.393.581744.734.912122.0919.332268.229.641185.713.51738.334.91101179.853.421743.164.898718.450.419973.2155.91200.983.521725.064.9814 15. One day lag Raw (Training) RMSE Pandu15 16. One day lag Raw (Testing) RMSE Pandu16 17. One day lag Raw (Training) MAPE Pandu17 18. One day lag Raw (Testing) MAPE Pandu18 19. Raw Dataset Two Day Lag Analysis (Pandu) ANN ARCHITECTURES LOGSIGPURELINTANSIGNumber of NeuronsRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST11373.879.501138.974.542057.5619.241658.458.649389.1751.649307.1534.68216182.1968.6116882.9475.942057.5619.131668.598.621371.089.281143.964.50318544.3571.9517355.8860.072058.2219.221660.158.641245.724.701121.783.8141830.703.751970.693.832057.8119.201671.518.6918601.8775.3719125.2881.66511815.7155.7413204.2763.662057.5519.291658.038.6716572.5273.6017369.6179.87616298.1466.5416643.7171.452058.6919.211667.488.6516951.4675.7616989.0955.8571190.643.321115.433.3913101.4855.5913678.9062.2118116.0775.9118488.2681.5781023.704.471411.734.512058.0019.191663.988.6416540.3672.4517176.0071.02918635.9276.6219245.8982.6716105.4371.0716783.3477.0318714.4676.7119245.8982.67101178.513.931101.593.752057.5819.221663.758.6618426.6576.3619114.4382.5019 20. Two day lag Raw (Training) RMSE Pandu20 21. Two day lag Raw (Testing) RMSE Pandu21 22. Two day lag Raw (Training) MAPE Pandu22 23. Two day lag Raw (Testing) MAPE Pandu23 24. Raw Dataset Three Day Lag Analysis (Pandu) ANN ARCHITECTURES LOGSIGPURELINTANSIGNumber of NeuronsRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAP E TST11373.479.511133.814.492050.8619.091661.788.571373.009.241139.994.3821151.463.451117.383.392053.0419.151671.528.6318724.8576.7219227.0782.66317562.7570.0919018.6078.372056.6119.191661.148.631144.513.801923.724.6049324.8245.5010609.7353.672050.2919.131667.628.6417703.8164.1218130.9872.48518460.1776.3919071.0182.452050.6419.001672.278.581063.263.991208.564.3261408.393.881394.843.382052.1619.021679.828.6128020.72265.1718338.0578.1171210.523.141527.474.442051.5319.321650.268.6612335.1662.1213726.9269.48814373.3246.8714095.9637.472050.5719.051669.468.5917478.6974.8417897.9480.59913768.7664.9314307.8769.552050.7319.091671.468.6418820.9077.3019266.9082.83104387.285.533230.113.482051.8218.991682.638.6118089.2375.8318476.8781.5524 25. Three day lag Raw (Training) RMSE Pandu25 26. Three day lag Raw (Testing) RMSE Pandu26 27. Three day lag Raw (Training) MAPE Pandu27 28. Three day lag Raw (Testing) MAPE Pandu28 29. Log-Transformed Dataset One Day Lag Analysis (Pandu) ANN ARCHITECTURES Number of NeuronsLOGSIGPURELINTANSIGRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST11611.525.121500.024.481586.655.111474.694.471601.375.131492.774.5321221.373.381049.172.761598.825.091478.734.431213.963.401040.942.7931212.183.321038.682.671592.895.101479.154.411230.653.431057.882.8141189.713.291020.612.671582.565.121473.884.501184.983.361018.502.7651209.063.351035.882.721589.995.111477.614.441224.273.421053.242.8161187.203.301023.332.721588.025.101473.784.471185.373.321019.772.7171188.613.311023.862.731597.215.091481.414.391184.033.291022.062.7281185.883.311020.122.711588.685.111475.914.451197.683.351043.202.7591184.383.351019.932.751583.425.121472.424.501185.603.291019.032.74101192.513.311020.012.731585.465.111473.924.481192.173.361022.922.7729 30. One day lag Log RMSE (Training) Pandu30 31. One day lag Log RMSE (Testing) Pandu31 32. One day lag Log MAPE (Training) Pandu32 33. One day lag Log MAPE (Testing) Pandu33 34. Log-Transformed Dataset Two Day Lag Analysis (Pandu) ANN ARCHITECTURES Number of NeuronsLOGSIGPURELINTANSIGRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST11485.484.761405.314.191468.464.751384.484.131490.144.751407.074.1321070.152.81943.522.221471.634.721386.234.051079.512.82946.732.2831085.262.86954.602.321464.984.751381.314.141066.992.78908.122.2241032.692.71909.222.171467.764.751383.704.14996.862.68946.192.2351029.312.73913.832.201478.214.751388.904.111046.422.78900.272.186986.182.66912.292.191460.834.741378.944.121052.022.75918.132.3271452.793.811277.853.081461.194.741381.284.09982.642.63927.722.2481045.462.82924.872.261469.004.801387.344.23985.212.67890.292.1191461.394.74906.172.141461.394.741380.754.11985.212.67902.552.17101051.772.75916.452.231464.624.731379.914.111031.932.74909.012.2134 35. Two day lag Log RMSE (Training) Pandu35 36. Two day lag Log RMSE (Testing) Pandu36 37. Two day lag Log MAPE (Training) Pandu37 38. Two day lag Log MAPE (Testing) Pandu38 39. Log-Transformed Dataset Three Day Lag Analysis (Pandu) ANN ARCHITECTURES Number of NeuronsLOGSIGPURELINTANSIGRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST11475.854.761385.534.131468.404.751378.164.111484.224.741391.114.1021076.272.87951.262.3171465.094.731374.494.071074.332.82945.982.2831087.982.90962.652.351466.164.741374.844.091046.252.78927.202.2441045.262.76939.672.251472.954.731381.044.061055.012.75937.612.2351046.952.76910.652.231469.614.741375.604.081015.002.68909.302.1961002.802.67907.952.121471.214.741379.974.091031.302.73913.662.2171036.142.75922.492.271476.244.741379.544.101074.702.82949.312.2781002.562.66900.172.131461.044.761375.354.171042.682.74930.892.2291031.352.71919.902.211461.364.751374.944.131069.752.76946.622.23101475.854.761385.534.131479.534.711382.934.01992.522.64907.852.1439 40. Three day lag Log RMSE (Training) Pandu40 41. Three day lag Log RMSE (Testing) Pandu41 42. Three day lag Log MAPE (Training) Pandu42 43. Three day lag Log MAPE (Testing) Pandu43 44. Log plus First Difference One Day Lag Analysis (Pandu) ANN ARCHITECTURES Number of NeuronsLOGSIGRMSE TRPURELINTANSIGRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TR6.481MAPE TR1983.115.412170.676.491955.345.362208.2646.502197.80 25.451810.944.372170.426.511957.005.392058.5111811.754.352171.676.501956.935.352053.8215.445.411809.784.322170.486.511957.015.392059.4995.495.521815.204.382181.446.491962.195.202056.0625.435.481815.424.462174.036.521960.125.332055.4285.465.501812.624.452167.886.521956.135.462057.1255.445.451812.854.272168.636.511956.575.442060.0255.444.37 1816.485.491827.224.442171.606.511958.405.372066.2175.442061.774.41 1815.575.49 2054.884.33 1812.062049.92104.36 1811.752049.1594.35 1812.622050.9684.44 1818.142059.9174.37 1810.212054.1564.55 1816.555.4255.375.562055.57 4MAPE TST1994.322053.54 3RMSE TST1807.534.432169.236.511956.385.422058.1175.454.38 1821.1944 45. One day lag Log+FD RMSE (Training) Pandu45 46. One day lag Log+FD RMSE (Testing) Pandu46 47. One day lag Log+FD MAPE (Training) Pandu47 48. One day lag Log+FD MAPE (Testing) Pandu48 49. Log plus First Difference Two Day Lag Analysis (Pandu) ANN ARCHITECTURES LOGSIGNumber of NeuronsPURELINTANSIGRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST12214.826.541985.455.432181.166.531951.955.452214.106.531985.905.4722226.026.581998.135.532171.136.511952.185.422055.155.471814.824.4232034.495.431808.944.332191.576.581960.975.412029.045.611820.994.4042070.485.641923.524.712169.256.531957.125.432035.505.361818.274.3051974.525.341783.654.362180.056.531952.395.442062.955.381831.024.3161945.045.391814.414.452181.226.551957.125.381981.955.441813.344.4872049.915.501835.124.412170.956.491950.775.361960.075.381827.304.3882050.235.421828.254.312176.166.521951.465.412044.625.421828.714.4092039.735.401812.584.352174.866.521951.785.441979.245.381798.374.39101975.615.271793.764.312172.566.521953.575.422047.785.431808.244.4249 50. Two day lag Log+FD RMSE (Training) Pandu50 51. Two day lag Log+FD RMSE (Testing) Pandu51 52. Two day lag Log+FD MAPE (Training) Pandu52 53. Two day lag Log+FD MAPE (Testing) Pandu53 54. Log plus First Difference Three Day Lag Analysis (Pandu) ANN ARCHITECTURES Number of NeuronsLOGSIGPURELINTANSIGRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TSTRMSE TRMAPE TRRMSE TSTMAPE TST12202.256.551972.205.472180.146.551953.855.452207.086.541979.055.4522085.555.941854.054.722174.526.551954.515.392054.215.521813.294.4132051.845.521817.474.472177.756.511947.615.362055.015.521822.524.4242024.775.471792.774.412174.316.501950.675.372054.755.441808.934.3852050.575.471822.844.382180.786.531952.015.372045.115.741809.024.4261985.295.401793.804.352185.426.521949.185.421937.475.351827.984.3672036.425.441826.544.412172.866.521953.175.432060.735.471815.974.4481984.565.371774.854.342174.346.511951.855.302050.485.481863.134.4992026.955.371826.184.322175.066.521946.675.451931.185.301797.644.36102042.285.531847.304.452182.356.531951.415.381966.275.361836.484.4354 55. Three day lag Log+FD RMSE (Training) Pandu55 56. Three day lag Log+FD RMSE (Testing) Pandu56 57. Three day lag Log+FD MAPE (Training) Pandu57 58. Three day lag Log+FD MAPE (Testing) Pandu58 59. Comparison of Network Performance Testing Datasets (Pandu) LOGSIG Best Network Structure1DatasetNo. of Lagged TermsPURELINTANSIGMAPE (%)RMSE (m3/s)MAPE (%)1-4-11765.994.872279.119.531817.125.0222-7-11115.433.3918116.0775.9118488.2681.573-2-11117.383.391671.528.6319227.0782.661-4-11020.612.671473.884.501018.502.7622-4-1909.222.171383.704.14946.192.2333-6-1907.952.121379.974.09913.662.211 Log + First DifferenceRMSE (m3/s)1 Log Transforme dMAPE (%)3RawRMSE (m3/s)1-8-11812.854.271956.575.441816.484.3722-4-11923.524.711957.125.431818.274.3033-8-11774.854.341951.855.301863.134.49 59 60. Comparison of Network Performance Training Datasets (Pandu) LOGSIG Best Network Structure1DatasetNo. of Lagged TermsPURELINTANSIGMAPE (%)RMSE (m3/s)MAPE (%)1-4-11199.293.462121.4719.081310.133.9122-7-11190.643.3213101.4855.5913678.9062.213-2-11151.463.452053.0419.1518724.8576.721-4-11189.713.291582.565.121184.983.3622-4-11032.692.711467.764.75996.862.6833-6-11002.802.671471.214.741031.302.731 Log + First DifferenceRMSE (m3/s)1 Log Transforme dMAPE (%)3RawRMSE (m3/s)1-8-12049.925.452168.636.512060.025.4422-4-12070.485.642169.256.532035.505.3633-8-11984.565.372174.346.512050.485.4860 61. FINAL CHOICE at PANDU Dataset : Log Transformed Dataset Three Days LagNetwork Architecture : LOGSIGNetwork Structure : 3 6 1 61 62. Performance (Pandu) High and Low ValuesHIGH VALUES (>30000)LOW VALUES (35000)LOW VALUES (