Planned intervention: On Wednesday April 3rd 05:30 UTC Zenodo will be unavailable for up to 2-10 minutes to perform a storage cluster upgrade.
Published December 20, 2008 | Version 13861
Journal article Open

Optimum Neural Network Architecture for Precipitation Prediction of Myanmar

Description

Nowadays, precipitation prediction is required for proper planning and management of water resources. Prediction with neural network models has received increasing interest in various research and application domains. However, it is difficult to determine the best neural network architecture for prediction since it is not immediately obvious how many input or hidden nodes are used in the model. In this paper, neural network model is used as a forecasting tool. The major aim is to evaluate a suitable neural network model for monthly precipitation mapping of Myanmar. Using 3-layerd neural network models, 100 cases are tested by changing the number of input and hidden nodes from 1 to 10 nodes, respectively, and only one outputnode used. The optimum model with the suitable number of nodes is selected in accordance with the minimum forecast error. In measuring network performance using Root Mean Square Error (RMSE), experimental results significantly show that 3 inputs-10 hiddens-1 output architecture model gives the best prediction result for monthly precipitation in Myanmar.

Files

13861.pdf

Files (558.8 kB)

Name Size Download all
md5:030705165602c1227c043ffca3c90182
558.8 kB Preview Download

Additional details

References

  • A free Neural Network engine; www.tek271.com/free/nuExpert.html
  • BackPropagation Neural Network model; http://ieee.uow.edu.au/~daniel/software/libneural/BPN_tutorial/BPN_En glish/BPN_English/node8.html
  • B.Cannas, A. Fanni, , "River flow forecasting using neural networks and wavelet analysis", Electric and Electronic Engineering Department, University of Cagliari, Italy, Geophysical Research Abstracts, Vol. 7, 08651, 2005
  • C. Marzban, G.J.Stumpf, "A Neural Network for Tornado Prediction Based on Doppler Radar-derived Attributes", National Severe Storms Laboratory, Norman, OK 73069, 1998.
  • F.Yerong, et.al, " A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach", ADVANCES In ATMOSPHERIC SCIENCES, VOL. 23, NO. 3, Department of Atmospheric Science, Zhongshan University, Guangzhou 510275, 2006, pp. 405-414.
  • J. McCullagh, et.al, "A Neural Network Model for Rainfall Estimation", IEEE Transactions on Neural Networks, Department of Information Technology, Lafrobe University Bendigo, 2005.
  • K.Figueiredo, et.al, "Neural Networks for Inflow Forecasting Using Precipitation Information", Springerm , Electrical and Telecommunications Engineering Department, Brazil, 2007, pp.552- 561.
  • M.Adya, F.Collopy, "How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation", Journal of Forecasting, University of Maryland at Baltimore County, USA, 1998. pp.481-482.
  • Myanmar; "Myanmar Country Report: Flood Forecasting And Warning In Myanmar", 4th Annual Mekong Flood Forum, Siem Reap, Cambodia, 18-19 May 2006, pp. 142-144. [10] N.Chantasut, C.Charoenjit, l, "Predictive Mining of Rainfall Predictions Using Artificial Neural Networks for Chao Phraya River", In Proceedings of Joint Conference The 4th International Conference of The Asian Federation of Information Technology in Agriculture and The 2nd World Congress on Computers in Agriculture and Natural Resources, August 9-12, 2004, Bangkok, Thailand, pp. 117-122. [11] Neural Network Model; http://leenissen.dk/fann/report/node4.html [12] N. J. de Vos and T. H. M. Rientjes, "Constraints of artificial neural networks for rainfall-runoff modelling: trade-offs in hydrological state representation and model evaluation", Hydrology and Earth System Sciences, 9, 111-126, Water Resources Section, Delft University of Technology, Delft, The Netherlands, 2005 [13] S.Chattopadhyay, , "A Soft Computing Technique in rainfall forecasting", In Proceedings of International Conference on IT, HIT, Pailan College of Management and Technology, Kolkata - 700 104, 2007, pp. 523-525. [14] S.Kalogirou, C.Neocleous, , "Wind Speed Prediction Using Artificial Neural Networks", Higher Technical Institute Department of Mechanical Engineering, Cyprus.