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Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review

by Sanjeev Karmakar, Siddhartha Choubey, Pradeep Mishra
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 10
Year of Publication: 2016
Authors: Sanjeev Karmakar, Siddhartha Choubey, Pradeep Mishra
10.5120/ijais2016451552

Sanjeev Karmakar, Siddhartha Choubey, Pradeep Mishra . Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review. International Journal of Applied Information Systems. 10, 10 ( May 2016), 33-54. DOI=10.5120/ijais2016451552

@article{ 10.5120/ijais2016451552,
author = { Sanjeev Karmakar, Siddhartha Choubey, Pradeep Mishra },
title = { Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review },
journal = { International Journal of Applied Information Systems },
issue_date = { May 2016 },
volume = { 10 },
number = { 10 },
month = { May },
year = { 2016 },
issn = { 2249-0868 },
pages = { 33-54 },
numpages = {9},
url = { https://www.ijais.org/archives/volume10/number10/899-2016451552/ },
doi = { 10.5120/ijais2016451552 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:03:18.432757+05:30
%A Sanjeev Karmakar
%A Siddhartha Choubey
%A Pradeep Mishra
%T Appropriateness of Neural Networks in Climate Prediction and Interpolations: A Comprehensive Literature Review
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 10
%N 10
%P 33-54
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To be familiar with appropriateness of Neural Network in climate prediction and spatial interpolation, e comprehensive literature review of past 50 years is done and offered in this paper. And it is established that Neural Network such as BPN, RBF is best appropriate to be predicted chaotic behavior of climate variables like rainfall, rainfall runoff, and have efficient enough for prediction in long period. It is also found that Neural Network is significant for spatial interpolation of mean climate variables.

References
  1. Walker, G. T., 1923, “Correlation in Seasonal Variations of Weather, III. A Preliminary Study of World Weather”. Mem. India Meteorol. Dep., XXIV, 75–131.
  2. Walker, G. T., 1924, “Correlation in Seasonal Variations of Weather, IV. A Further Study of World Weather”. Mem. India Meteorol. Dep., XXIV, 275– 332.
  3. Gowariker, V., Thapliyal, V., Sarker, R. P., Mandal, G. S. and Sikka, D. R., 1989, “Parametric and Power Regression Models: New Approach to Long Range Forecasting of Monsoon Rainfall in India”. Mausam, 40, 115– 122.
  4. Gowariker, V., Thapliyal, V., Kulshrestha, S. M., Mandal, G. S., Sen Roy, N., and Sikka, D. R., 1991, “A Power Regression Model for Long Range Forecast of Southwest Monsoon Rainfall over India”. Mausam, 42, 125–130.
  5. Thapliyal, V., and Kulshrestha, S. M., 1992, “Recent Models for Long Range Forecasting of Southwest Monsoon Rainfall over India”. Mausam, 43, 239–248.
  6. Thapliyal, V., 1997, “Preliminary and Final Long Range Forecasts for Seasonal Monsoon Rainfall over India”. J. Arid Environ., 36, 385–403.
  7. Rajeevan, M., Guhathakurta, P. and Thapliyal, V., 2000, “New Models for Long Range Forecasting of Monsoon Rainfall over Northwest and Peninsular India”. Meteorol. Atmos. Phys., 73, 211–225.
  8. Rajeevan M., 2001, “Prediction of Indian Summer Monsoon: Status, Problems and Prospects”. Current Science, 81, 1451–1457.
  9. Thapliyal, V., and Rajeevan, M., 2003, “Updated Operational Models for Long-Range Forecasts of Indian Summer Monsoon Rainfall”. Mausam, 54, 495–504.
  10. Rajeevan, M., Pai, D.S., Dikshit, S.K., and Kelkar, R. R., 2004, “IMD’s New Operational Models for Long-Range Forecast of Southwest Monsoon Rainfall over India and Their Verification for 2003”, Current Science, 86 (3), 422-431.
  11. Guhathakurta, P., 2000, “New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India”, Meteor. & Atomos. Phys.,73 (3), 211-255.
  12. Guhathakurta, P., Rajeevan, M., and Thapliyal, V., 1999, “Long Range Forecasting Indian Summer Monsoon Rainfall by Hybrid Principal Component Neural Network Model”, Meteorology and Atmospheric Physics, Springer-Verlag, Austria.
  13. Parthasarathy, B., Rupa Kumar, K., and Munot, A. A., 1991, “Evidence of Secular Variations in Indian Summer Monsoon Rainfall Circulation Relationships”. J. Climate, 4, 927–938.
  14. Hastenrath, S., and Greischar, L., 1993, “Changing Predictability of Indian Monsoon Rainfall Anomalies”. Proc. Indian Acad. Sci. (Earth Planet. Sci.), 102, 35–47.
  15. Guhathakurta P., (2006), “Long-Range Monsoon Rainfall Prediction of 2005 for the Districts and Sub-Division Kerala with Artificial Neural Network”, Current Science, 90(6), pp-773-779.
  16. Krishnamurthy, V., and Kinter, J. L., 2002, “The Indian Monsoon and its Relation to Global Climate Variability”. Global Climate – Current Research and Uncertainties in the Climate System (eds Rodo, X. and Comin, F. A.), 186–236.
  17. Krishnamurthy, V., and Kirtman, B. P., 2003, “Variability of the Indian Ocean: Relation to Monsoon and ENSO". Q. J. R.” Meteorol. Soc., 129, 1623–1646
  18. Sahai, A. K., Grimm, A. M., Satyan. V. and Pant, G. B., 2002, “Prospects of Prediction of Indian Summer Monsoon Rainfall using Global SST Anomalies”. IITM Research Report No. RR-093.
  19. Guhathakurta, P., 1999, “Long Range Forecasting Indian Summer Monsoon Rainfall By Principle Component Neural Network Model”, Meteor. & Atomos. Phys., 71, 255-266.
  20. Guhathakurta, P., 1998, “A Hybrid Neural Network Model for Long Range Prediction of All India Summer Monsoon Rainfall”, Proceedings of WMO international workshop on dynamical extended range forecasting, Toulouse, France, November 17-21, 1997, PWPR No. 11, WMO/TD. 881, pp 157-161.
  21. Basu Sujit & Andharia H I, 1992, “The Chaotic time series of Indian Monsoon rainfall and its prediction”, Proc. Indian Acad. Sci., Vol. 101, No. 1, pp 27-34.
  22. Chow T. W. S. and Cho S. Y., 1997, “Development of a Recurrent Sigma-Pi Neural Network Rainfall Forecasting System in Hong Kong”, Springer-Verlag, pp. 66-75.
  23. Lee Sunyoung, Cho Sungzoon, Wong Patrick M., 1998, “Rainfall Prediction Using Artificial Neural Networks”, Journal of Geographic Information and Decision Analysis, vol. 2, no. 2, pp. 233 – 242.
  24. Hsieh, W.H., and Tang, B., 1998, “Applying Neural Network Models to Prediction and Analysis in Meteorology and Oceanography”. Bull. Amer. Met. Soc., 79, 855-1870.
  25. Dawson Christian W., Wilby Robert, “An artificial neural network approach to rainfall runoff modeling”, 1998, Hydrological Sciences—Journal, 43(1), pp. 47-66.
  26. Guhathakurta1 P., Rajeevan2 M., and Thapliyal2 V., 1999, “Long Range Forecasting Indian Summer Monsoon Rainfall by a Hybrid Principal Component Neural Network Model”, Meteorol. Atmos. Phys., 71, pp. 255-266.
  27. Ricardo, M., Trigo, Jean, P., Palutikof, 1999, “Simulation of Daily Temperatures for Climate Change Scenarios over Portugal: A Neural Network Model Approach”, University of East Anglia, Norwich, NR4 7TJ, United Kingdom, climate research (Clim., Res.), 13, 45–59, 1999.
  28. Jones, C., Peterson, P., 1999, “A New Method for Deriving Ocean Surface Specific Humidity and Air Temperature: An Artificial Neural Network Approach”, J. Applied Meteorology, American Meteorological Society, 38, 1229-1245.
  29. Guhathakurta, P., 1999, “A Short Term Prediction Model for Surface Ozone At Pune: Neural Network Approach”, Vayu mandal, Special issue on Asian monsoon and pollution over the monsoon environment, 29(1-4), 355-358.
  30. Guhathakurta, P., 1999, “A Neural Network Model for Short Term Prediction of Surface Ozone at Pune”, Mausam, 50(1), 91-98.
  31. Toth E. *, Brath A., Montanari A., 2000, “Comparison of short-term rainfall prediction models for real-time flood forecasting”, Journal of Hydrology, Elsevier, 239, pp. 132-147.
  32. Luk Kin C, Ball J. E. and Sharma A., 2001, “An Application of Artificial Neural Networks for Rainfall Forecasting”, 33, pp. 883-699.
  33. Michaelides Silas Chr,*, Pattichis Constantinos S. and Kleovouloub Georgia, 2001, “Classification of rainfall variability by using artificial neural networks”, International Journal Of Climatology, pp. 1401–1414.
  34. Chang Fi-John, Liang Jin-Ming, and Chen Yen-Chang,2001, “Flood Forecasting Using Radial Basis Function Neural Networks”, IEEE Transactions On Systems, Man, And Cybernetics, vol. 31, no. 4, pp. 530-535.
  35. Brath A., Montanari A. and Toth E., 2001, “Neural networks and non-parametric methods for improving ealtime flood forecasting through conceptual hydrological models”, Hydrology and Earth System Sciences, 6(4), pp. 627-640.
  36. Rajurkar M. P., Kothyari U. C., Chaube U. C., 2002, “Artificial neural networks for daily rainfall-runoff modelling”, Hydrologkal Sciences-Journals, 47(6), pp. 865-877.
  37. Harun Sobri, Nor Irwan Ahmat & Mohd. Kassim Amir Hashim, 2002, “Artificial neural network model for rainfall-runoff relationship”, Journal Technology, Malaysia, pp. 1-12.
  38. Iseri, Y. G. C., Dandy, R., Maier, A., Kawamura, and Jinno, K., 2002, “Medium Term Forecasting of Rainfall Using Artificial Neural Networks”, Part 1 background and methodology, Journal of Hydrology , 301 (1-4), 1834-1840.
  39. Silva, A. P., 2003, “Neural Networks Application to Spatial Interpolation of Climate Variables”, Carried Out By, STSM on the Framework of COST 719 ZAMG, Vienna 6-10 October 2003.
  40. Snell., Seth, E., Gopal, Sucharita, KaufmNeural Network, Robert, K., 2003, “Spatial Interpolation of Surface Air Temperatures Using Artificial Neural Networks: Evaluating Their Use for Downscaling GCMs”, Journal of Climate, 13 (5), 886-895.
  41. Blender, R., 2003, “Predictability Study of the Observed and Simulated European Climate using Linear Regression”, Q. J. R. Meteorol. Soc. (2003), 129, 2299–2313.
  42. Maqsood Imran, Khan Muhammad Riaz, Abraham Ajith, 2004, Neural Comput & Applic, 13, pp. 112–122.
  43. Pasero Eros, Moniaci Walter, 2004, “Artificial Neural Networks for Meteorological Nowcast”, IEEE international Conference,
  44. Lekkas D.F.,* Onof1 C., Lee1 M. J., Baltas2 E.A., 2004, “Application of artificial neural networks for flood forecasting”, Global Nest, Vol 6, No 3, pp 205-211.
  45. Shu Chang and Burn Donald H., 2004, “Artificial neural network ensembles and their application in pooled flood frequency analysis”, Water Resources Research, vol. 40.
  46. Nayaka P.C.,*, Sudheerb K.P.,1Ranganc, D.M.,2, Ramasastrid K.S.,3, 2004, “A neuro-fuzzy computing technique for modeling hydrological time series”, Journal of Hydrology, Elsevier, 291, pp. 52–66.
  47. Wu Jy S., P.E., ASCE1 M.; Han2 Jun; Neural Networkambhotla3 Shastri; and Scott Bryant4, 2004, “Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows”, Journal Of Hydrologic Engineering, 216.
  48. Abdel-Aal R.E. *, 2004, “Hourly temperature forecasting using abductive networks”, Elsevier, 17, pp. 543–556.
  49. Lee Tsong-Lin, 2004, “Back-propagation neural network for long-term tidal predictions”, Elsevier, 31, pp. 225–238.
  50. Chaudhuri Sutapa, Chattopadhyay Surajit, 2005, “ Neuro-computing based short range prediction of some meteorological parameters during the pre-monsoon season”, Springer-Verlag, 9, pp. 349–354.
  51. Lin Gwo-Fong * and Chen Lu-Hsien, 2005, “Application of an artificial neural network to typhoon rainfall forecasting”, Hydrological Processes, 19, pp. 1825–1837.
  52. Vandegriff Jon *, Wagstaff Kiri, George Ho, Plauger Janice, 2005, “Forecasting space weather: Predicting interplanetary shocks using neural networks”, Elsevier, vol 36, pp. 2323–2327.
  53. KISI Ozgur, 2005, “Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models”, Turkish J. Eng. Env. Sci., vol 29, pp. 9 - 20.
  54. Maqsood Imran, Khan Muhammad Riaz, Huang Guo H., Abdalla Rifaat, 2005, “Application of soft computing models to hourly weather analysis in southern Saskatchewan, Canada”, Elsevier, vol 18, pp. 115–125.
  55. Somvanshi V.K., Pandey O.P., Agrawal P.K., Kalanker1 N.V., Prakash M.Ravi and Chand Ramesh, 2006, “Modelling and prediction of rainfall using artificial neural network and ARIMA techniques”, J. Ind. Geophys. Union, Vol.10, No.2, pp.141-151.
  56. Srikalra Niravesh and Tanprasert Chularat, 2006, “Rainfall Prediction for Chao Phraya River using Neural Networks with Online Data Collection”, Malaysia, pp. 13-15.
  57. Kumarasiri A.D.and Sonnadara D.U.J., 2006, “Rainfall Forecasting: An Artificial Neural Network Approach”, Proceedings of the Technical Sessions, vol 22, pp. 1-13.
  58. Kumar D. Nagesh, Reddy M. Janga and Maity Rajib, 2006, “Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques”, Journal of Intelligent Systems, Vol. 16, No.4, pp. 307-322.
  59. Guhathakurta, P., 2006, “Long-Range Monsoon Rainfall Prediction of 2005 for the Districts and Sub-Division Kerala With Artificial Neural Network”, Current Science, 90 (6), pp-773-779.
  60. Bustami Rosmina, l BessaihNabi, Charles Bong, Suhaila Suhaili, 2007, “Artificial Neural Network for Precipitation and Water Level Predictions of Bedup River”, International Journal of Computer Science, vol 34:2.
  61. Paras, Mathur Sanjay, Kumar Avinash, and Chandra Mahesh, 2007, “A Feature Based Neural Network Model for Weather Forecasting”, World Academy of Science, Engineering and Technology, vol 34, pp. 66-73.
  62. Hayati Mohsen, and Mohebi Zahra, 2007, “Application of Artificial Neural Networks for Temperature Forecasting”, World Academy of Science, Engineering and Technology, vol 28, pp. 275-279.
  63. Morid Saeid, Smakhtin Vladimir and Bagherzadeh K., 2007, “Drought forecasting using artificial neural networks and time series of drought indices”, Royal Meteorological Society, vol. 27, pp. 2103–2111.
  64. Hayati Mohsen, and Shirvany Yazdan, 2007, “Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region”, World Academy of Science, Engineering and Technology, vol. 28, pp. 280-284.
  65. Lucio P. S., Conde F. C., Cavalcanti I. F. A., Serrano A. I., Ramos A. M., and Cardoso A. O., 2007, “Spatiotemporal monthly rainfall reconstruction via artificial neural network – case study: south of Brazil”, Advances in Geosciences, vol. 10, pp. 67–76.
  66. HartmNeural Network, Heikea * Becker Stefanb and Kinga Lorenz, 2007, “Predicting summer rainfall in the Yangtze River basin with neural networks”, Royal Meteorological Society.
  67. Aliev R. A., Fazlollahi B., Aliev R. R., Guirimov B., 2008, “Linguistic time series forecasting using fuzzy recurrent neural network”, Soft Comput, vol. 12, pp. 183–190.
  68. Chattopadhyay Surajit and Chattopadhyay Goutami, 2008, “Identification of the best hidden layer size for three layered neural net in predicting monsoon rainfall in India”, Journal of Hydroinformatics, vol. 10(2), pp. 181-188.
  69. Hung N. Q., Babel M. S., Weesakul S., and Tripathi N. K., 2008, “An artificial neural network model for rainfall forecasting in Bangkok, Thailand”, Hydrology and Earth System Sciences, vol. 5, pp. 183–218.
  70. Aytek Ali, Asce M and Alp Murat, 2008, “An application of artificial intelligence for rainfall–runoff modeling”, J. Earth Syst. Sci., vol. 117, No. 2, pp. 145–155.
  71. Chattopadhyay Goutami, Chattopadhyay Surajit, Jain Rajni, 2008, “Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: Neurocomputing and statistical approaches”,
  72. Win Khaing Mar and Thu Naing Thinn, 2008, “Optimum Neural Network Architecture for Precipitation Prediction of Myanmar”, World Academy of Science, Engineering and Technology, vol. 48, pp. 130-134.
  73. Karmakar Sanjeev et al., 2008, “Development of a Satellite Based Agriculture Meteorological Information System (AGRIMETCast) in the Context of Remote Places of Chhattisgarh Using Remote Sensing Data” , India Society of Remote Sencing, Ahmedabad, India., SAC (ISRO)
  74. Karmakar Sanjeev et al., 2008, “Development of Artificial Neural Network Models for Long-Range Meteorological Parameters Pattern Recognition over the Smaller Scale Geographical Region”, IEEE Computer Society, Washington, DC, USA., 8-10 Dec. 2008, pp.1 – 6.
  75. Hocaoglu Fatih O., Oysal Yusuf, Kurban Mehmet, 2009, “Missing wind data forecasting with adaptive neuro-fuzzy inference system”, Springer-Verlag London, vol. 18, pp. 207–212.
  76. Solaimani Karim, 2009, “Rainfall-runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed)”, American-Eurasian J. Agric. & Environ. Sci., vol. 5(6), pp. 856-865.
  77. KOŠCAK Juraj, JAKŠA Rudolf., SEPEŠI Rudolf, SINCÁK Peter., 2009, “Weather forecast using Neural Networks”, 9th Scientific Conference of Young Researchers
  78. Karamouz M. Fallahi M, Nazif S. and Farahani M. Rahimi, 2009, “Long Lead Rainfall Prediction Using Statistical Downscaling and Arti cial Neural Network Modeling”, Transaction A: Civil Engineering, Vol. 16, No. 2, pp. 165-172.
  79. Widjanarko Bambang Otok, Suhartono, 2009, “Development of Rainfall Forecasting Model in Indonesia by using ASTAR, Transfer Function, and ARIMA Methods”, European Journal of Scientific Research, Vol.38 No.3, pp.386-395.
  80. Nekoukar Vahab, Taghi Mohammad, Beheshti Hamidi, 2010, “A local linear radial basis function neural network for financial time-series forecasting”, Springer Science, vol. 23, pp. 352–356.
  81. Weerasinghe H.D.P.,. Premaratne H.L and Sonnadara D.U.J., 2010, “Performance of neural networks in forecasting daily precipitation using multiple sources”, J.Natn.Sci.Foundation Sri Lanka, vol. 38(3), pp. 163-170.
  82. Luenam Pramote, Ingsriswang Supawadee, Ingsrisawang Lily, Aungsuratana Prasert, and Khantiyanan Warawut, 2010, “A Neuro-Fuzzy Approach for Daily Rainfall Prediction over the Central Region of Thailand”, ISSN 2010, vol. 1.
  83. Wu C. L.,. Chau1 K. W, and Fan C., 2010, “Prediction of Rainfall Time Series Using Modular Artificial Neural Networks Coupled with Data Preprocessing Techniques”, Journal of Hydrology, Vol. 389, No. 1-2, pp. 146-167.
  84. Nastos Panagiotis, Moustris Kostas, Larissi IoNeural Networka, and Paliatsos Athanasios, 2010, “Rain intensity forecast using Artificial Neural Networks in Athens, Greece”, Geophysical Research Abstracts, Vol. 12.
  85. Patil C. Y. and Ghatol A. A., 2010, “Rainfall forecasting using local parameters over a meteorological station: an artificial neural network approach”, International J. of Engg. Research & Indu. Appls, Vol.3, No. II, pp 341-356.
  86. Tiron Gina, and Gosav Steluţa, 2010, “The july 2008 rainfall estimation from BARNOVA WSR-98 D Radar using artificial neural network”, Romanian Reports in Physics, Vol. 62, No. 2, pp. 405–413.
  87. Goyal Manish Kumar, Ojha Chandra Shekhar Prasad, 2010, “Analysis of Mean Monthly Rainfall Runoff Data of Indian Catchments Using Dimensionless Variables by Neural Network”, Journal of Environmental Protection, vol. 1, pp. 155-171.
  88. Vamsidhar Enireddy Varma K.V.S.R.P..Sankara Rao P satapati Ravikanth, 2010, “Prediction of Rainfall Using Backpropagation Neural Network Model”, International Journal on Computer Science and Engineering, Vol. 02, No. 04, pp. 1119-1121.
  89. Haghizadeh Ali, Teang shui Lee, Goudarzi Ehsan, 2010, “Estimation of Yield Sediment Using Artificial Neural Network at Basin Scale”, Australian Journal of Basic and Applied Sciences, vol. 4(7), pp. 1668-1675.
  90. Subhajini A . C. and Joseph Raj V., 2010, “Computational Analysis of Optical Neural Network Models to Weather Forecasting”, International Journal of Computer Science Issue, Vol.7, Issue 5, pp. 327-330.
  91. Omer Faruk Durdu, 2010, “A hybrid neural network and ARIMA model for water quality time series prediction.”, Elsevier, vol 23, pp. 586–594
  92. Soman Saurabh S, Zareipour Hamidreza, Malik Om, and Mandal Paras, 2010, “A Review of Wind Power and Wind Speed Forecasting Methods With Different Time Horizons”.
  93. Khalili Najmeh, Khodashenas Saeed Reza, Davary Kamran and Karimaldini Fatemeh, 2011, “Daily Rainfall Forecasting for Mashhad Synoptic Station using Artificial Neural Networks”, International Conference on Environmental and Computer Science, vol.19, pp. 118-123.
  94. Pan Tsung-Yi, Yang Yi-Ting, Kuo Hung-Chi, Tan Yih-Chi, Lai1 Jihn-Sung, Chang Tsang-Jung, Lee Cheng-Shang, and Hsu Kathryn Hua, 2011, “Improvement of Statistical Typhoon Rainfall Forecasting with NEURAL NETWORK-Based Southwest Monsoon Enhancement”, Terr. Atmos. Ocean. Sci, Vol. 22, No. 6, pp. 633-645.
  95. Joshi Jignesh, Patel Vinod M., 2011, “Rainfall-Runoff Modeling Using Artificial Neural Network (A Literature Review)”, National Conference on Recent Trends in Engineering & Technology.
  96. El-Shafie Amr H., Shafie A. El-, Mazoghi Hasan G. El, Shehata A. and Taha Mohd. R., 2011, “Artificial neural network technique for rainfall forecasting applied to Alexandria, Egypt”,
  97. Mekanik F., Lee T.S. and Imteaz M. A., 2011, “Rainfall modeling using Artificial Neural Network for a mountainous region in West Iran”.
  98. Kaur Amanpreet, Singh Harpreet, 2011, “Artificial Neural Networks in Forecasting Minimum Temperature”, International Journal of Electronics & Communication Technology, Vol. 2, Issue 3, pp. 101-105.
  99. El-Shafie A., Jaafer O. and Akrami Seyed Ahmad, 2011, “Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia”, International Journal of the Physical Sciences, Vol. 6(12), pp. 2875-2888.
  100. Tripathy Asis Kumar, Mohapatra Suvendu, Beura Shradhananda, Pradhan Gunanidhi, 2011. “Weather Forecasting using NEURAL NETWORK and PSO”, International Journal of Scientific & Engineering Research, Volume 2, Issue 7, pp.1-5.
  101. El-Shafie A, Noureldin A., Taha M. R., and Hussain A., 2011, “Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia”, Hydrol. Earth Syst. Sci., vol. 8, pp. 6489–6532.
  102. Geetha G.,. Selvaraj R Samuel, 2011, “Prediction of monthly rainfall in Chennai using back propagation neural network model”, International Journal of Engineering Science and Technology, Vol. 3 No. 1, pp. 211-213.
  103. Reshma T., Reddy K. Venkata, Pratap Deva, 2011, “Determination of Distributed Rainfall- Runoff Model Parameters Using Artificial Neural Network”, International Journal of Earth Sciences and Engineering, Volume 04, No 06, pp. 222-224
  104. Raju M.Mohan,. Srivastava R. K, Bisht Dinesh C. S., Sharma H. C., and Kumar Anil, 2011, “Development of Artificial Neural-Network-BasedModels for the Simulation of Spring Discharge”, Hindawi Publishing Corporation, Volume 2011, pp. 1-11.
  105. Kavitha M.Mayilvaganan,.Naidu K.B, 2011, “NEURAL NETWORK and Fuzzy Logic Models for the Prediction of groundwater level of a watershed”, International Journal on Computer Science and Engineering, Vol. 3 No. 6, pp. 2523-2530.
  106. El-shafie A., Mukhlisin M., Najah Ali A. and Taha M. R., 2011, “Performance of artificial neural network and regression techniques for rainfall-runoff prediction”, International Journal of the Physical Sciences, Vol. 6(8), pp. 1997-2003.
  107. Afshin Sarah, Fahmi Hedayat, Alizadeh Amin, Sedghi Hussein and Kaveh Fereidoon, 2011, “Long term rainfall forecasting by integrated artificial neural network-fuzzy logic-wavelet model in Karoon basin”, Scientific Research and Essays, Vol. 6(6), pp. 1200-1208.
  108. Siou1 Line Kong A, JohNeural Networket Neural Networke, Borrell Valérie, Pistre Séverin, 2011, “Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France)”, Journal of Hydrology, vol. 403, pp. 367-380.
  109. Saima H., Jaafar J., Belhaouari S., Jillani T.A., 2011, “Intelligent Methods for Weather Forecasting: A Review”, IEEE,
  110. Sawaitul Sanjay D., Prof. Wagh K. P., Dr. Chatur P. N., 2012, “Classification and Prediction of Future Weather by using Back Propagation Algorithm-An Approach”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 1, pp. 110-113.
  111. S Karmakar, M K Kowar, P Guhathakurta, Evolving and Evaluation of 3LP FFBP Deterministic NEURAL NETWORK Model for District Level Long Range Monsoon Rainfall Prediction, J. Environmental Science & Engineering, , National Environmental Engineering Research Institute, Nagpur, INDIA, ISSN 0367-827, Vol 51, No. 2, pp. 137-144, April 2009. URL: http://www.neeri.res.in/jese.html.
  112. S Karmakar, M K Kowar, P Guhathakurta, Development of an 8-Parameter Probabilistic Artificial Neural Network Model for Long-Range Monsoon Rainfall Pattern Recognition over the Smaller Scale Geographical Region –District, IEEE Computer Society, IEEE Xplore 2.0,, DC, USA, ISBN 978-0-7695-3267-7, pp. 569-574, July 2008. http://www2.computer.org/portal/web/csdl/doi/10.1109/ICETET.2008.225. http://portal.acm.org/citation.cfm?id=1445475
  113. S. Karmakar, M K Kowar, P Guhathakurta, Development of Artificial Neural Network Models for Long-Range Meteorological Parameters Pattern Recognition over the Smaller Scale Geographical Region-District, IEEE Xplore 2.0, IEEE Computer Society, Washington, DC, USA, ISBN 978-1-4244-2806-9, pp. 1-6, Dec. 2008. URL:http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=4798312&arnumber=4798370&count=184&index=54.
  114. S Karmakar, M K Kowar, P Guhathakurta, Long-Range Monsoon Rainfall Pattern Recognition & Prediction for the Subdivision ‘EPMB’ Chhattisgarh Using Deterministic & Probabilistic Neural Network, IEEE Xplore 2.0, IEEE Computer Society, Washington, DC, USA, ISBN 978-0-7695-3520-3, pp. 376-370, Feb. 2009. http://www2.computer.org/portal/web/csdl/doi/10.1109/ICAPR.2009.24.
  115. Peter et al.,(1979), Collinearity and Stability in the Estimation of Rainfall-Runoff model parameters, Journal of Hydrogy,42,91-108.
  116. Kitanidis.P.K., & Bras.R.L.,(1980),Adaptive Filtering through detection of isolated transient error in rainfall-runoff models,Water resources research ,16,740-748
  117. Martinec.J. (1982), Runoff Modeling from Snow Covered Area, IEEE Trans, 20(3), 259-262.
  118. Baumgartner,M.F., Seidel, K. and Martinec.J. (1987). Toward Snowmelt Runoff Forecast Based on Multisensor Remote-Sensing Information, IEEE Trans., 25(6), 746-750.
  119. Vandewiele.G.L., and Yu.C. (1992), Methodology and comparative study of monthly water balance models in Belgium, China and Burma, Journal of Hydrology, 134(1-4), 315–347.
  120. Kumar,V.S.,Paul,P.R.,Ramaana Rao, CH.L.V.,Haefner.H., and Seibel.K.,(1993), JAHS Publecation, 218, 315-347.
  121. Kember,G., and Flower,A.C. (1993), Forecasting river flow using nonlinear dynamics, Stochastic Hydrology and Hydraulics, 7, 205-212.
  122. Seidel, K., Brusch,W., and Steinmeier,C.(1994), Experiences from Real Time Runoff Forecasts by Snow Cover Remote Sensing, IEEE Trans., 2090-2093.
  123. Shi, J., and Dozier,J. (1995), Inferring Snow Wetness Using C-Band Data from SIR-C's Polarimetric Synthetic Aperture Radar, IEEE Trans., 33(4),905-914.
  124. Franchinia,M., Helmlinger,T.K.R.,Foufoula-Georgioub.E., and Todini,E. (1996), Stochastic storm transposition coupled with rainfall-runoff modeling for estimation of exceedance probabilities of design floods, Journal of Hydrology, 175,511-532.
  125. Xia,J., O'Connor,K.M., Kachroo,R.K., and Liang,G.C. (1997), A non-linear perturbation model considering catchment wetness and its application in fiver flow forecasting, Journal of Hydrology, 200, 164-178.
  126. Franchini,M., and Galeati,G., (1997), Comparing several genetic algorithm schemes for the calibration of conceptual rainfall-runoffmodels, Bydrological Sciences-Journal-des Sciences Hydrologiques, 42(3), 357-379.
  127. Bach,H., Lampart,G., Strasser.G., and Mauser,W., (1999), First Results of an Integrated Flood Forecast System Based on Remote Sensing Data, IEEE Trans., 6(99), 864-866
  128. Sivakumar, B., Phoon, K.K., and Liong S.Y., (1999), A systematic approach to noise reduction in chaotic hydrological time series, Journal of Hydrology, 219(3-4),103-135.
  129. Sajikumar, N., and Thandavewara, B.S., (1999), A non-linear rainfall–runoff modelusing an artificial neural network, Journal of Hydrology, 216(1-2), 32-55.
  130. Dibike.Y.B., and Solomatine.D.P. (1999), River Flow Forecasting Using Artificial Neural Networks, Elsevier, 26(1), 1-7.
  131. Gmez-Landesa,E., and Rango,A. (2000), Snow Mapping Technilque at Subpixel Level for Small Basins, IEEE Trans, 3,1140-1142.
  132. Schaper.J., and Seidel.K.,(2000) ,Modelling daily runoff from snow and glacier melt using remote sensing data,EARSeL-SIG-Workshop Land Ice and Snow, Dresden/FRG, June 16 – 17, 308-317
  133. Toth,E., Brath,A., and Montanari,A., (2000), Comparison of short-term rainfall prediction models for real-time flood forecasting, Journal of Hydrology, 239, 132-147.
  134. Mallor,D., Sheffield,J., O’Connel,P.E., and Metcalfe,A.V., (2000), A stochastic space-time rainfall forecasting II: Application of SHETRAN and ARNO rainfall runoff models to the Brue catchment, Hydrology & Earth System Sciences , 4(4), 617-626.
  135. Sivapragasam,C., Liong,S.Y., and Pasha,M.F.K.,(2001), Rainfall and runoff forecasting with SSA–SVM approach, Journal of Hydroinformatics, 141-152.
  136. Chang,F.J., Liang,J.M, and Chen,Y.C.,(2001), Flood Forecasting Using Radial Basis Function Neural Networks, IEEE Trans, 31(4), 530-535.
  137. G´omez-Landesa,E., and Rango,A.,(2002), Operational snowmelt runoff forecasting in the Spanish Pyrenees using the snowmelt runoff model, HYDROLOGICAL PROCESSES, 16, 1583–1591.
  138. Randall,W.A., and Tagliarini,G.A.,(2002), Using Feed Forward Neural Networks to Model the Effect of Precipitation on the Water Levels of the Northeast Cape Fear River, IEEE Trans, 338-342.
  139. CNeural Networkas,B.,FNeural Networki,A.,Pintusb,M., and Sechib,G.M.,(2002), Neural network models to forecast hydrological risk, IEEE Trans, 623-626.
  140. Karna,J.P., Pulliainen,J., Huttunen,M., and Koskinen,J.,(2002), Assimilation of SAR data to operational hydrological runoff and snow melt forecasting model, IEEE Trans, 1146-1148.
  141. Brath,A.,Montanari,A., and Toth,E.,(2002), Neural networks and non-parametric methods for improving realtime flood forecasting through conceptual hydrological models, Hydrology and Earth System Sciences, 6(4), 627-640
  142. Mahabir,C.,Hicks,F.E., and Fayek,A.R.,(2003), Application of fuzzy logic to forecast seasonal runoff, Hydrological Process, 17, 3749–3762.
  143. Slomatine,D.P., and Dulal.K.N.,(2003), Model trees as an alternative to neural networks in rainfall–runoff modelling, Hydrological Sciences–Journal–des Sciences Hydrologiques, 48(3), 399-411.
  144. Gaume,E., and Gosset,R.,(2003), Over-parameterisation, a major obstacle to the use of artificial neural networks in hydrology, Hydrol. Earth Syst. Sci., 7, 693-706.
  145. Hossain,F., Anagnostou,E.N., and Dinku.T.,(2004), Sensitivity Analyses of Satellite Rainfall Retrieval and Sampling Error on Flood Prediction Uncertainty, IEEE Trans, 42(1), 130-139.
  146. Tigkas,D., and Tsakiris,G.,(2004), Medbasin: Mediterranean rainfall-runoff software package, EW Publication, 5(6), 3-11.
  147. Murphy.C., Dr. Charlton.R., Dr. Sweeney.J., Dr.Fealy.R.,(2004),Catering for uncertainity in a conceptual Rainfall-Runoff model: Model preparation for climate change impact assessment and the application of GLUE using Latin Hypercube sampling, National Hydrology Seminar 64-74..
  148. Corani,G., and Guariso,G.,(2005), Coupling Fuzzy Modeling and Neural Networks for River Flood Prediction, IEEE Trans, 35(3), 382-390.
  149. Khan,M.S., and Coulibaly,P.,(2005), Streamflow Forecasting with Uncertainty Estimate Using Bayesian Learning for NEURAL NETWORK, IEEE Trans, 2680-2685.
  150. Valença,M., Ludermir,T., and Valença,A.,(2005), Modeling of the rainfall-runoff relationship with artificial neural network, IEEE Trans.
  151. Knebl,M.R., Yang,Z.L., Hutchison,K., and Maidment,D.R.,(2005), Regional scale flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event, Journal of Environmental Management, 75, 325-336.
  152. Nayak,P.C.,Sudheer,K.P., and Ramasastri,K.S.,(2005), Fuzzy computing based rainfall–runoff model for real time flood forecasting, , Hydrological Process, 19, 955-968.
  153. Chang,N.B., and Guo,D.H.,(2006), Urban Flash Flood Monitoring, Mapping, and Forecasting via a Tailored Sensor Network System, IEEE Trans, 757-761.
  154. Ghedira,H., Arevalo,J.C., Lakhankar,T., Azar.A., Khanbilvardi,R., and Romanov.P.,(2006), The Effect of Vegetation Cover on Snow Cover Mapping from Passive Microwave Data, IEEE Trans, 148-153.
  155. Liu,C.H., Chen,C.S., Su.H.C., and Chung.Y.D.,(2006), Forecasting Models for the Ten-day Streamflow of Kao-Ping River, IEEE Trans, 1527-1534.
  156. Li,Q., Chen,S., and Wang,D.,(2006), An Intelligent Runoff Forecasting Method Based on Fuzzy sets, Neural network and Genetic Algorithm, IEEE Trans.
  157. Huan,W.U., Xiuwan,C., and Xianfeng,Z.,(2006), A Cellular Automata Based Distributed Model for Simulating Runoff in LeAnhe Watershed, IEEE Trans, 1032-1035.
  158. Du,J., Xie,S., Xu,Y., Xie.H., Hu,Y., and Wang,P.,(2006), Flood Simulation with Distributed Hydrological Approach Using DEMs and Remotely Sensed Data, IEEE Trans, 1048-1051.
  159. Tayfur,G., Vijay and Singh,V.P., and Asce.F.,(2006), NEURAL NETWORK and Fuzzy Logic Models for Simulating Event-Based Rainfall-Runoff, Journal of Hydraulic Engineering,1321-1330.
  160. Cheng,C.T., Chau,C.W., and Li,X.Y.,(2007), Hydrologic Uncertainty for Bayesian Probabilistic Forecasting Model Based on BP NEURAL NETWORK, IEEE Trans.
  161. Jiang,G., Shen,B., and Li.Y.,(2007), On the Application of Improved Back Propagation NeuralNetwork in Real-Time Forecast. IEEE Trans.
  162. Ju,Q., Hao,Z., Zhu,C., and Liu,D.,(2007), Hydrologic Simulations with Artificial Neural Networks, IEEE Trans.
  163. Broersen,P.M.T.,(2007), Error Correction of Rainfall-Runoff Models With the ARMAsel Program, IEEE Trans, 56(6), 2212-2219.
  164. Ji,L., and Bende,W.,(2007), Parameters Selection for SVR based on the SCEM-UA Algorithmand Its Application on Monthly Runoff Prediction, IEEE Trans, 48-51.
  165. Moore,R.J.,(2007), The PDM rainfall-runoff model, Hydrology and Earth System Sciences, 11(1), 483-499.
  166. Lohani,A.K., Goel,N.K., and Bhatia,K.K.,(2007), Deriving stage–discharge–sediment concentration relationships using fuzzy logic, Hydrological Sciences Journal, 52(4), 793-807.
  167. Jenicek,M., (2007), Rainfall-runoff modelling in small and middle-large catchments- an overview, Hydrological Sciences Journal, 305-313.
  168. Jingbo,L., Zengchuan,D., Dezhi,W., and Shaohua.L.,(2008), Research on Runoff Forecast Model Based on Phase Space Reconstruction, IEEE Trans, 5339-5343.
  169. Lake,I.,(2008)., Operational forecasts of algae blooms in the Baltic Sea, IEEE Trans.
  170. Li,C., and Yuan,X.,(2008), Research and Application of Data Mining for Runoff Forecasting, IEEE Trans, 795-798.
  171. Liu,C.H., Chen,C.S., and Huang,C.H.,(2008), Revising One Time Lag Of Water Level Forecasting With Neural Fuzzy System, IEEE Trans, 617-621.
  172. Pei,W., and Zhu.Y.Y.,(2008), A Multi-Factor Classified Runoff Forecast Model Based on Rough Fuzzy Inference Method, IEEE Trans,221-225.
  173. Xu,Q., Ren,L., Yu,Z., Yang,B., and Wang,G.,(2008), Rainfall-runoff modeling at daily scale with artificial neural networks, IEEE Trans, 504-508.
  174. Sang,Y., and Wang,D.,(2008), A Stochastic Model for Mid-to-Long-Term Runoff Forecast, IEEE Trans, 44-48.
  175. Liu,F., and Jiang,D.,(2008), Nonlinear Forecast Modeling Based on Wavelet Analysis,IEEE Trans, 622-625.
  176. Sun,X.L.,Tan,Y.M.,and Xu.X.C.,(2008), BP Neural Network Model Based on Reconstruction Phase Space and Its Application in Runoff Forecasting, IEEE Trans, 794-797.
  177. Liu.F., Pan.H., and Jiang,D.,(2008), An Improved Markov Chain Monte Carlo Scheme for Parameter Estimation Analysis, IEEE Trans, 702-706.
  178. Wiriyarattanakul,S., Auephanwiriyakul,S., and Umpon.N.T.,(2008), Runoff Forecasting UsingFuzzy Support Vector Regression, IEEE Trans.
  179. Guo,H., Dong,G.Z., and Chen,X.,(2008), WNEURAL NETWORK Model for Monthly Runoff Forecast, IEEE Trans, 1087-1089.
  180. Remesan,R., Shamim,M.A.,Han,D. and Mathew,J.,(2008), ANFIS and NNARX based Rainfall-Runoff Modeling, IEEE Trans, 1454-1459.
  181. Archer,D.R., and Fowler,H.J.,(2008), Using meteorological data to forecast seasonal runoff on the River Jhelum, Pakistan, Journal of Hydrology, 361, 10-23.
  182. Aytek,A., Asce,M., and Alp,M.,(2008), An application of artificial intelligence for rainfall–runoff modeling, J. Earth Syst. Sci., 117(2), 145-155.
  183. Solaimani,K.,(2009), Rainfall-runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed), American-Eurasian J. Agric. & Environ. Sci, 5(6). 856-865.
  184. Zhang,L.P., Song,X., Sheng,T., and Peng,T.,(2009), The Neural Networkual runoff forecasting research based on the theory of cointegration and error correction model, IEEE Trans, 1-4.
  185. Ren,Z., and Hao,Z.C.,(2009), Application of Moving Windows Autoregressive Quadratic Model in Runoff Forecast, IEEE Trans, 200-203.
  186. Wang,H.F., Chen,W.Y., and Song,S.I.,(2009), Design of Jinan City Flood Prevention and Warning Decision-Making Support System based on SQL Server and GIS, IEEE Trans, 488-492.
  187. Ping,H.,(2009), Wavelet neural network based on BP algorithm and its application in flood forecasting, IEEE Trans.
  188. Zhu,Y.Y., and Pei.W.,(2009), Research of a Boundary Prolongation Method in RunoffForecast Based on Wavelet Transform,IEEE Trans, 1254-1258.
  189. Min,F., and Wu,X.,(2009), Local Semi-Linear Regression for River Runoff Forecasting, IEEE Trans, 556-561.
  190. Lu,Y., and Chen,X.,(2009), A Rapidly and Accurately Calculating Method of The Three Gorges Reservoir Dynamic Storage, IEEE Trans.
  191. Yan,J., Liu.Y.,Wang,J., Cao,H., and Zhao,H.,(2009), BP model applied to forecast the water and sediment fluxes in the Yellow River Mouth, IEEE Trans.
  192. Yan,J., Liu.Y.,Wang,J., Cao,H., and Zhao,H.,(2009), RBF model applied to forecast the water and sediment fluxes in Lijin section, IEEE Trans.
  193. Luna.I., Soares.S.,Lopes.J.E.G., and Ballini.R.,(2009),Verifying the use of evolving fuzzy systems for multi-step ahead daily inflow forecasting, IEEE Trans.
  194. Sihui.D.,(2009), A Forecast Model of Hydrologic Single Element Medium and Long-period Based on Rough Set Theory,IEEE Trans,19-25.
  195. Guo.J, Xiong.W., and Chen.H.,(2009),Application of Rough Set Theory to Multi-factor Medium and Long-period Runoff Prediction in Danjing Kou Reservoir,IEEE Trans,177-182.
  196. Acar.R., Şenocak.S., and Şengul.S.,(2009),Snow Hydrology Studies in the Mountainous Eastern Part of Turkey,IEEE Trans,1578-1582.
  197. Xu.J., Zha.J., Zhang.W., Hu.Z., and Zheng.Z.,(2009),Mid-Short-Term daily Runoff forecasting by NEURAL NETWORKs and multiple process-based Hydrological models IEEE Trans,526-529.
  198. Xui.J., Zhu.X., Zhang.W., Xui.X., and Xian.J.,(2009),Daily streamflow forecasting by Artificial Neural Network in a large-scale basin,IEEE Trans,487-490.
  199. Feng.L.H., and Zhang.J.Z.,(2009), Application of NEURAL NETWORK in Forecast of Surface Runoff, IEEE Trans.
  200. Hundecha.Y., Bardossy.A., and Werner.H.,(2009),Development of a fuzzy logic-based rainfall-runoff model, Hydrological Sciences Journal,46(3),363-376.
  201. Bulygina.N., McIntyre.N., and Wheater.H.,(2009),Conditioning rainfall-runoff model parameters for ungauged catchments and land management impacts analysis, Hydrol. Earth Syst. Sci.,13,893-904.
  202. Hung.N.Q., Babel.M.S., Weesakul.S., and Tripathi.N.K.,(2009),An artificial neural network model for rainfall forecasting in Bangkok,Thailand, Hydrol. Earth Syst. Sci,13,1413-1425.
  203. Dadhwala,V.K., Aggarwal,S.P., and Mishra,N.,(2010),Hydrological Simulation of Mahanadi River Basin and Impact of Land Use / Land Cover Change on Surface Runoff Using a Macro Scale Hydrological Model, ISPRS TC VII Symposium XXXVIII,165-169.
  204. Kafle.T.P., Hazarika.M.K., Karki.S., Shrestha.R.M., Sharma.R., and Samarkoon.L.,(2010), Basin scale Rainfall-Runoff modelling for flood forecasts, IEEE Trans.
  205. Changying.L.,and Huagui.H.,(2010),Application Research on Hydrological Forecasting Based on Grey Prediction Model,IEEE Trans,290-293.
  206. Longxi.H., and Hong.J.,(2010),Impact of Sand Excavation in the Pearl River System on Hydrology and Environment,IEEE Trans.
  207. Xu.J., Wei.J., and Liu.Y.,(2010),Modeling Daily Runoff in a Large-scale Basin based on Support Vector Machines,IEEE(International Conference on Computer and Communication Technologies in Agriculture Engineering), 601-604.
  208. Deshmukh.R.P., and Ghatol.A.,(2010),Comparative study of Jorden and Elman model of neural network for short term flood forecasting, IEEE Trans.,400-404.
  209. Li.X., Zhao.K.,, and Zheng.X.,(2010),An Error Analysis Method for Snow Depth Inversion Using Snow Emission Model,IEEE Trans.
  210. Wang.W., and Qiu.L.,(2010),Prediction of Neural Networkual Runoff Using Adaptive Network Based Fuzzy Inference System,IEEE Trans.
  211. Wang.W., Xu.D., and Qiu.L.,(2010),Support Vector Machine with Chaotic Genetic Algorithms for Neural Networkual Runoff Forecasting,IEEE Trans.,(671-675).
  212. Ding.Z., Zhang.J., and XIE.G.,(2010),LS-SVM Forecast Model of Precipitation and Runoff Based on EMD,IEEE Trans.,1721-1725.
  213. Liu.Y., Chen.Y., Hu.J., Huang.Q., and Wang.Y.,(2010),Long-term Prediction for Autumn Flood Season in Danjiangkou Reservoir Basin Based on OSR-BP Neural Network, IEEE Trans., 1717-1720.
  214. Yan.J., Chen.S., and Jiang.C.,(2010),The Application of BP and RBF Model in the Forecasting of the Runoff and the Sediment Transport Volume in Linjin Section,IEEE Trans.,1892-1896.
  215. Liu.J., Dong.X., and Li.Y.,(2010),Automatic calibration of hydrological model by shuffled complex evolution metropolis algorithm,IEEE Trans.,256-259.
  216. Huang.M., and Tian.Y.,(2010),Design and implementation of a visual modeling tool to support interactive runoff forecasting,IEEE Trans.,270-274.
  217. Jizhong.B., Biao.S., Minquan.F., Jianming.Y., and Likun.Z.,(2010),Adaptive regulation ant colony system algorithm - radial basis function neural network model and its application IEEE Trans.
  218. Huang.M., and Tian.Y.,(2010),SVM-Based Visual Modeling System for Enhancing the flexibility of Interactive Runoff Forecasting,IEEE .
  219. Pradhan.R., Mohan P., Pradhan.M.P., Ghose.M.K., Agarwal.V.S., and Agarwal.S.,(2010) Estimation of RainfallRunoff using Remote Sensing and GIS in and around Singtam, East Sikkim, International Journal of Geomatics and Geosciences,1(3), 466-476.
  220. Shengtang.Z., Xiaojia.G., and Yun.J.,(2011),Stormwater Utilization as an Enviromental-friendly method to Alleviate Urban water resources Crisis:Taking Qingdao as an Example, IEEE Trans.,1722-1725.
  221. Fu.Y.C., and Wei.C.J.,(2011),Method Study of Water Quality Control in Polluted Hun-Tai River Basin,IEEE Trans.
  222. Linke.H., Karimanzira.D., Rauschenbach.T., and Pfutzenreuter.T.,(2011),Flash flood prediction for small rivers,IEEE Trans.,86-91.
  223. Wang.X.,(2011),Application of Computer Simulation in the Reservoir Flood Control,IEEE Trans.
  224. Ma.X., Ping.J., Yang.L., Yan.M., and Mu.H.,(2011),Combined Model of Chaos Theory, Wavelet and Support Vector Machine for Forecasting Runoff Series and its Application, IEEE Trans.,842-845.
  225. Huaqil.W., Maosheng.Z., and Peicheng.,L.,(2011),Long-term Trend Analysis for the Runoff Series in Yulin,IEEE Trans.,1062-1065.
  226. Hu.C.H.., Wu.Z.N., Wang.J.J.,and Liu.L.,(2011),Application of the Support Vector Machine on Precipitation-Runoff Modelling in Fenhe River,IEEE Trans.,1099-1103.
  227. Ilker.A., Kose.M., Ergin.G., and Terzi.O.,(2011)An Artificial Neural Networks Approach to Monthly Flow Estimation,IEEE Trans.,325-328.
  228. Huang.G., and Wang.L.,(2011),Hybrid Neural Network Models for Hydrologic Time Series Forecasting Based on Genetic Algorithm,IEEE Trans.,1347-1350.
  229. Du.D., and Zhu.Z.Y.,(2011),The Neural Networkual Runoff Simulation Based on Pan-Kriging Method of Time Domain,IEEE Trans.,5469-5471.
  230. Zhen-min.Z., Xuechao.W., and Ke.Z.,(2011),Rainfall-Runoff Forcast Method Base on GIS, IEEE Trans.,2406-2409.
  231. Zhang.R., and Wang.Y.,(2011),Research on daily runoff forecasting model of lake IEEE Trans.,1648-1650.
  232. Yan.Q., Wei.J., Lin.L., Yang.Y., Zhu.X., and Shao.J.,(2011),Predicting Soil Erosion in Sloping Hilly Areas of Central Sichuan Based on SVM Model,IEEE Trans.,313-317.
  233. Yanxun.S., Jianhua.W.,and Peiyue.L.,(2011),Application of System Theory in the Calculation of Groundwater Resources Availability in Balasu Water Source,IEEE Trans.,707-710.
  234. Weilin.L.,Lina.L.,and Zengchuan.D.,(2011),Neural network model for hydrological forecasting based on multivariate phase space reconstruction,IEEE Trans.,663-667.
  235. Aiyun.L., and Jiahai.L.,(2011),Forecasting Monthly Runoff Using Wavelet Neural Network Model,IEEE Trans.,2177-2180.
  236. Zhang.N.,and Lai.S.,(2011),Water Quantity Prediction Based on Particle Swarm Optimization and Evolutionary Algorithm Using Recurrent Neural Networks,IEEE Trans.,2172-2176.
  237. Chen.Z., Li.L., and Bakir.M.,(2011),Application of improved TOPMODEL in rainstorm region IEEE Trans.,2901-2906.
  238. Chen.Z., and Li.L.,(2011),Advanced HBV Model Research Based on GIS Applying in Partial Rainstorm Area,IEEE Trans.,5733-5737.
  239. Liang.J., Yang.Q., Liao.R., Han.C., Jing.G., and He.X.,(2011),Web-based System for Rainfall-runoff Forecast in Beijing Metropolis,IEEE Trans.,6201-6204.
  240. Limlahapun.P., Fukui.H., Yan.W., and Ichinose.T.,(2011),Integration of flood forecasting model with the web-based system for improving flood monitoring, and the alert system,IEEE Trans., 767-772.
  241. Wu.C.L., and Chau.K.W.,(2011),Rainfall-Runoff Modelling Using Artificial Neural Network Coupled with Singular Spectrum Analysis, Journal of Hydrology,399(3-4),394-409.
  242. Wanga.E., Zhenga.H., Chiewa.F., Shaob.Q., Luo.J., and Wang.Q.J.,(2011),Monthly and seasonal streamflow forecasts using rainfall-runoff modeling and POAMA predictions,IEEE Trans., 3441-3447.
  243. Patil. S.,Patil.S., and Valunjkar.S.,(2012),Study of Different Rainfall-Runoff Forecasting Algorithms for Better Water Consumption, International Conference on Computational Techniques and Artificial Intelligence,327-330.
  244. Brocca.L., Moramarco.T., Melone.F., and Wagner.W.,(2012),Assimilation of Surface- and Root-Zone ASCAT Soil Moisture Products Into Rainfall–Runoff Modeling,IEEE Trans., 50(7),2542-2555.
  245. Shengtang.Z., Peng.C., and Miaomiao.L.,(2012),Discussion on Soil and Water Conservation Activities Hydrological Effects Simulation in the Loess Plateau,IEEE Trans.,241-244.
  246. Shah.H., Jaafar.J., Ibrahim.R., Saima.H., and Maymunah.H.,(2012),A Hybrid System Using Possibilistic Fuzzy C-Mean and Interval Type-2 Fuzzy Logic for Forecasting: A Review IEEE Trans.,532-537.
  247. Xiao-lin.G.,Li-zi.Z., Jun.S. and Ning-ning.F.,(2012),A Multi-scenario Model for Mid-long Term Hydro-Thermal Optimal Scheduling,IEEE Trans.
  248. Bell.B., Wallace.B., and Zhang.D.,(2012),Forecasting River Runoff through Support Vector Machines,IEEE Trans.,58-64.
  249. Li.K., Ji.C., Zhang.Y., Xie.W., and Zhang.X.,(2012),Study of mid and long-term runoff forecast based on back-propagation neural network,IEEE Trans.,188-191.
  250. Zhang.N., Yao.L., and Wang.Q.,(2012),Study on One-Dimensional Movement Model of Maliu Gully Debris Flow.
  251. Mittal.P., Chowdhury.S., Roy.S., Bhatia.N., and Srivastav.R.,(2012),Dual Artificial Neural Network for Rainfall-Runoff Forecasting,Journal of Water Resource and Protection,4,1024-1028.
  252. Jingwen.X., Wanchang.Z., Ziyan.Z., Jing.C., and Meiyan.J.,(2012),Establishment of a Hybrid Rainfall-Runoff Model for Use in the Noah LSM, Chinese Meteorological Society and Springer,26,85-92.
  253. Chen.S.M., Wang.Y.M., and Tsou.I.,(2013),Using artificial neural network approach for modelling rainfall–runoff due to typhoon, J. Earth Syst. Sci.,122(2),399-405.
  254. Gebregiorgis.A.S., and Hossain.F.,(2013),Understanding the Dependence of Satellite Rainfall Uncertainty on Topography and Climate for Hydrologic Model Simulation, IEEE Trans., 51(1),704-718.
  255. Phuphong.S., and Surussavadee.C.,(2013),An Artificial Neural Network Based Runoff Forecasting Model in the Absence of Precipitation Data: A Case Study of Khlong U-Tapao River Basin, Songkhla Province,Thailand,IEEE Trans.,73-77.
  256. Zhang.Y., Hong.Y., Wang.X.G., Gourley.J.J., Gao.J.D., Vergara.H.J. and Yong.B.,(2013) Assimilation of Passive Microwave Streamflow Signals for Improving Flood Forecasting: A First Study in Cubango River Basin, Africa,IEEE Trans.,1-16.
  257. Patil.S., and Walunjkar.,(2013),Rainfall-Runoff Forecasting Techniques For Avoiding Global Warming.Robertson.
  258. Robertson.D.E.,Pokhrel.P., & Wang.Q.J.,(2013),Improving statistical forecasts of seasonal streamflows using hydrological model output,Hydrology and Earth System Sciences,17,579-593
  259. Vleeschouwer.N.D., and Pauwels.V.R.N.,(2013),Assessment of the indirect calibration of a rainfall-runoff model for ungauged catchments in Flanders,Hydrology and Earth System Sciences,17,2001-2016.
  260. Ramana.R.V., Krishna.B., Kumar.S.R., and Pandey.N.G.,(2013),Monthly Rainfall Prediction Using Wavelet Neural Network Analysis,Springer(Water Resour Manage),27,3697-3711.
  261. Karmakar.S.,Shrivastava.G., and Kowar.M.K.,(2014), Impact of Learning Rate and Momentum Factor in the Performance of Back-Propagation Neural Network to Identify Internal Dynamics of Chaotic Motion,Kuwait J.Sci,41(2),151-174.
  262. Journel, A. G., Huijbregts, C. J. ,1978: Mining geostatistics. Academic Press, New York
  263. Hornik, K., Stinchcombe, M., White, H., 1989: Multilayer feedforward networks are universal approximators. Neural Networks, 2, 359-366.
  264. Goovaerts, P., 1997: Geostatistics for natural resources evaluation, Oxford University Press, New York,
  265. Demyanov, V., 1998: Neural Network Residual Kriging Application for Climatic Data”, J. Geographic Information and Decision Analysis, 2 (2), 215-232.
  266. Huang, Y., Wong, P. M. Gedeon, T. D., 1998: Prediction of reservoir permeability using genetic algorithms, AI Applications, 12(1-3), pp. 67-75.
  267. Huang, Y., Wong, P.M., Gedeon, T.D., 1998: Spatial Interpolation Using Fuzzy Reasoning and Genetic Algorithms, J. Geographic Information and Decision Analysis, 2(2), 204 -214
  268. Ricardo, M. T., Palutikof, J.P., 1999: Simulation of Daily Temperatures for Climate Change Scenarios over Portugal: A Neural Network Model Approach, Climate research (Clim., Res.), 13, 45–59
  269. Guhathakurta, P., 1998: A Hybrid Neural Network Model for Long Range Prediction of All India Summer Monsoon Rainfall, Proc. WMO international workshop on dynamical extended range forecasting, Toulouse, France, November 17-21, 1997, PWPR No. 11, WMO/TD. 881, 157-161.
  270. Guhathakurta, P., 1999: Long Range Forecasting Indian Summer Monsoon Rainfall By Principle Component Neural Network Model, J. Meteor. & Atomos. Phys., 71, 255-266.
  271. Guhathakurta, P., 1999: A Short Term Prediction Model for Surface Ozone At Pune: Neural Network Approach, Vayu mandal, 29(1-4), 355-358.
  272. Guhathakurta, P., 1999 : A Neural Network Model for Short Term Prediction of Surface Ozone at Pune, Mausam, 50(1), 91-98.
  273. Guhathakurta, P., Rajeevan, M., Thapliyal, V., 1999: Long Range Forecasting Indian Summer Monsoon Rainfall by Hybrid Principal Component Neural Network Model, Meteorology and Atmospheric Physics, Springer-Verlag, Austria.
  274. Guhathakurta, P., 2000: New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India, Meteorology and Atmospheric Physics,73 (3), 211-255.
  275. Rajeevan, M., Guhathakurta, P., Thapliyal, V., 2000, “New Models for Long Range Forecasting of a Monsoon Rainfall over Northwest and Peninsular India”. Meteorology and Atmospheric Physics, 73, 211–225.
  276. Karmakar, S., Kowar, M. K., Guhathakurta, P., 2009: Evolving and Evaluation of 3LP FFBP Deterministic NEURAL NETWORK Model for District Level Long Range Monsoon Rainfall Prediction”, J. Environmental Science & Engineering, 51(2), 137-144
  277. Karmakar, S., Kowar, M. K., Guhathakurta, P., 2008: Development of Artificial Neural Network Models for Long-Range Meteorological Parameters Pattern Recognition over the Smaller Scale Geographical Region-District”, IEEE Xplore, IEEE Computer Society. Washington, DC, USA.
  278. Karmakar, S., Kowar, M. K., Guhathakurta, P., 2008: Development of an 8-Parameter Probabilistic Artificial Neural Network Model for Long-Range Monsoon Rainfall Pattern Recognition over the Smaller Scale Geographical Region –District, IEEE Xplore, IEEE Computer Society, Washington, DC, USA.
  279. Karmakar, S., Kowar, M. K., Guhathakurta, P., Development of a 6 Parameter Artificial Neural Network Model for Long-Range July Rainfall Pattern Recognition over the Smaller Scale Geographical Region District, CSVTU Research Journal, pp. 36-40.
  280. Karmakar, S., Kowar, M. K., Guhathakurta, P., 2009: Long-Range Monsoon Rainfall Pattern Recognition & Prediction for the Subdivision ‘EPMB’ Chhattisgarh Using Deterministic & Probabilistic Neural Network”, IEEE Xplore, IEEE Computer Society, Washington, DC, USA.
  281. Karmakar, S., Kowar M. K., Guhathakurta P., 2009, Spatial Interpolation of Rainfall Variables using Artificial Neural Network”, ACM DL, 547-552
  282. Snell, S. E., 2000, Spatial Interpolation of Surface Air Temperatures Using Artificial Neural Networks: Evaluating Their Use for Downscaling GCMs, Journal of Climate, 13, 886-895
  283. Antonić, O., Križan, J., Marki, A., Bukovec, D., 2001: Spatio-temporal interpolation of climatic variables over large region of complex terrain using neural networks, Ecological Modelling, 138, 255-263
  284. Rigol, J.P., Jarvis, C.H., Stuart, N., 2001: Artificial neural networks as a tool for spatial interpolation, International Journal of Geographical Information Science, 15, 323-343
  285. Koike, K., Matsuda, S., Gu, B., 2001: Evaluation of Interpolation Accuracy of Neural Kriging with Application to Temperature-Distribution Analysis. Mathematical Geology, 33(4), 421-448. Kumar, S., :Neural Network Computer Engineering Serie, The McGraw-Hill, New Delhi, 2007, 104-152
  286. Bryan, B.A., Adams, J.M., 2001: Quantitative and Qualitative Assessment of the Accuracy of Neurointerpolated Neural Networkual Mean Precipitation and Temperature Surfaces for China, Cartography, 30 (2), Perth, Australia.
  287. Bryan, B.A., Adams, J. M., 2002: Three-Dimensional Neurointerpolation of Neural Networkual Mean Precipitation and Temperature Surfaces for China, Geographical Analysis, 34 (2), 93-111
  288. Iseri, Y. G. C. Dandy., Maier, R., Kawamura, A., Jinno, K., 2002: Medium Term Forecasting of Rainfall Using Artificial Neural Networks, Part 1 background and methodology, Journal of Hydrology , 301 (1-4), 1834-1840
  289. Silva, A. P., :2003, Neural Networks Application to Spatial Interpolation of Climate Variables, Carried Out By, STSM on the Framework of COST 719 ZAMG
  290. Attorre, F., Alfo, M., Sanctis, M., Francesconi, F., Bruno, F., 2007: Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale, Int. J. Climatol., Royal Meteorological Society, Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/joc.1495
  291. Chattopadhyay, S., Chattopadhyay, G., 2008: Identification of the best hidden layer size for threelayered neural net in predicting monsoon rainfall in India, J. Hydroinformatics, 10 (2), 181-188
  292. Hung, N. Q., Babel, M. S., Weesakul, S., Tripathi N. K., 2009: An artificial neural network model for rainfall forecasting in Bangkok, Thailand, Hydrology and Earth System Sciences, 13, 1413–1425
  293. Mendes, D., Marengo, J., 2010: South America downscaling: using spatial artificial neural network, Geophysical Research Abstracts, 12, EGU2010-9475
  294. Sivapragasam, C., Arun, V.M., Giridhar, D., 2010, A simple approach for improving spatial interpolation of Rainfall using NEURAL NETWORK, Meteoro Atmos Phys, Springer, 109, 1-7
  295. Ghazanfari, S., Alizadeh, A., Farid, A., BNeural Networkayan, M., 2011: Comparison the PERSINEURAL NETWORK model with interpolation methods to estimate daily precipitation (A case study: North-Khorasan, Iran), Geophysical Research Abstracts, 13, EGU2011-12743
Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Chaos Prediction Forecasting Climate