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An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand : Volume 13, Issue 8 (07/08/2009)

By Hung, N. Q.

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Book Id: WPLBN0003975569
Format Type: PDF Article :
File Size: Pages 13
Reproduction Date: 2015

Title: An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand : Volume 13, Issue 8 (07/08/2009)  
Author: Hung, N. Q.
Volume: Vol. 13, Issue 8
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2009
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

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Tripathi, N. K., Babel, M. S., Weesakul, S., & Hung, N. Q. (2009). An Artificial Neural Network Model for Rainfall Forecasting in Bangkok, Thailand : Volume 13, Issue 8 (07/08/2009). Retrieved from http://cn.ebooklibrary.org/


Description
Description: School of Engineering and Technology, Asian Institute of Technology, Thailand. This paper presents a new approach using an Artificial Neural Network technique to improve rainfall forecast performance. A real world case study was set up in Bangkok; 4 years of hourly data from 75 rain gauge stations in the area were used to develop the ANN model. The developed ANN model is being applied for real time rainfall forecasting and flood management in Bangkok, Thailand. Aimed at providing forecasts in a near real time schedule, different network types were tested with different kinds of input information. Preliminary tests showed that a generalized feedforward ANN model using hyperbolic tangent transfer function achieved the best generalization of rainfall. Especially, the use of a combination of meteorological parameters (relative humidity, air pressure, wet bulb temperature and cloudiness), the rainfall at the point of forecasting and rainfall at the surrounding stations, as an input data, advanced ANN model to apply with continuous data containing rainy and non-rainy period, allowed model to issue forecast at any moment. Additionally, forecasts by ANN model were compared to the convenient approach namely simple persistent method. Results show that ANN forecasts have superiority over the ones obtained by the persistent model. Rainfall forecasts for Bangkok from 1 to 3 h ahead were highly satisfactory. Sensitivity analysis indicated that the most important input parameter besides rainfall itself is the wet bulb temperature in forecasting rainfall.

Summary
An artificial neural network model for rainfall forecasting in Bangkok, Thailand

Excerpt
Gwangseob, K. and Ana, P. B.: Quantitative flood forecasting using multisensor data and neural networks, Journal of Hydrology, 246, 45–62, 2001.; French, M. N., Krajewski, W. F., and Cuykendall, R. R.: Rainfall forecasting in space and time using neural network, J. Hydrol., 137, 1–31, 1992.; Abrahart, R. J. and See, L.: Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecast in two contrasting catchments, Hydrol. Proc., 14, 2157–2172, 2000.; Ahmad, S. and Simonovic, S. P.: An artificial neural network model for generating hydrograph from hydro-meteorological parameters, J. Hydrol., 315(1–4), 236–251, 2005.; ASCE: Task Committee on Application of Artificial Neural Networks in Hydrology. I: Preliminary Concepts, J. Hydrol. Eng., 5(2), 115–123, 2000.; ASCE: Task Committee on Application of Artificial Neural Networks in Hydrology. II: Hydrologic Applications, J. Hydrol. Eng., 5(2), 124–137, 2000; Campolo, M. and Soldati, A.: Forecasting river flow rate during low-flow periods using neural networks, Water Resour. Res., 35 (11), 3547–3552, 1999.; Fletcher, D. S. and Goss, E.: Forecasting with neural network: An application using bankruptcy data, Inf. Manage., 24, 159–167, 1993.; Coulibaly, P., Anctil, F., and Bobee, B.: Daily reservoir inflow forecasting using artificial neural networks with stopped training approach, J. Hydrol., 230, 244–257, 2000.; Hsu, K., Gupta, H. V., and Sorooshian, S.: Artificial neural network modeling of the rainfall-runoff process, Water Resour. Res., 31(10), 2517–2530, 1995.; Koizumi, K.: An objective method to modify numerical model forecasts with newly given weather data using an artificial neural network, Weather Forecast., 14, 109–118, 1999.; Lapedes, A. S. and Farber, R. M.: Nonlinear signal processing using neural networks: Prediction and system modeling, Los Alamos Report LA-UR 87-2662, 1987.; Lippmann, R. P.: An introduction to computing with neural nets, IEEE ASSP Magazine, 4, 4–22, 1987.; Luk, K. C., Ball, J. E., and Sharma, A.: A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, J. Hydrol., 227, 56–65, 2000.; Maier, R. H. and Dandy, G. C.: The use of artificial neural network for the prediction of water quality parameters, Water Resour. Res., 32(4), 1013–1022, 1996.; Maier, R. H. and Dandy, G. C.: Comparison of various methods for training feed-forward neural network for salinity forecasting, Water Resour. Res., 35(8), 2591–2596, 1999.; Rogers, L. L. and Dowla, F. U.: Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling, Water Resour. Res., 30(2), 457–481, 1994.; Rosenblatt, F.: The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Cornell Aeronautical Laboratory, Psychological Review, 65(6), 386–408, 1958.; Rumelhart, D. E. and McClelland, J. L.: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, London, UK, The MIT Press, 1986.; Shamseldin, A. Y.: Application of a neural network technique to rainfall-runoff modeling, J. Hydrol., 199, 272–294, 1997.; Toth, E., Montanari, A., and Brath, A.: Comparison of short-term rainfall prediction model for real-time flood forecasting, J. Hydrol., 239, 132–147, 2000.; Zealand, C. M., Burn, D. H., and Simonovic, S. P.: Short term streamflow forecasting using artificial neural networks, J. Hydrol., 214, 32–48, 1999.; Werbos, P. J.: Beyond Regression: New Tools for Prediction and Analysis in Behavioral Sciences, Ph.D. dissertation, Appl. Math., Harvard University, Cambridge, MA, USA, 1974.

 
 



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