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:: Volume 32, Number 1 (6-2017) ::
geores 2017, 32(1): 149-162 Back to browse issues page
Modeling Monthly Rainfall in Southern Baluchestan Basin
Abstract:   (305 Views)

Flood and drought have caused several damages in natural and unnatural ecosystems in recent decade. Rainfall prediction can be useful in water resource management. The goal of this study is modeling the monthly precipitation of south east of Iran in South-Baluchistan basin, using artificial neural network (ANN) and stochastic models. This area has an unpredictable and complicated monthly rainfall pattern due to impact of several different precipitation systems of other surrounding regions. SARIMA time series models and Time Delay Neural Network (TDNN) are used in monthly precipitation forecasting. Monthly time series of rainfall during 1351-52 to 1387-88 in selected station were used in this study. Stations selection was based on Geographical distribution and data quality. Comparing the results of models of forecasting showed that TDNN model is superior to SARIMA time series model due to different rainfall systems and very sporadic precipitation in this area.

Keywords: Precipitation, ANN, Time Series, Forecasting, South-Baluchestan Basin
Full-Text [PDF 879 kb]   (158 Downloads)    
Type of Study: Research | Subject: Special
Received: 2017/06/13 | Accepted: 2017/06/13 | Published: 2017/06/13
References
1. Abdollah Nejad, K. (2015), Forecasting of Monthly Sum-raining by Stochastic Models in Time Series. geographical planning of space quarterly journal, 5 (17), pp. 15-25 (in Persian).
2. Addiscott, T., Whitmore, S. (1987), Computer simulation of changes in soil mineral nitrogen and crop nitrogen during autumn, winter and spring, The Journal of Agricultural Science, 109(01), pp. 141-157.
3. Ahhashimi, S. (2014), Prediction of monthly rainfall in Kirkuk using artificial neural network and time series models, Journal of engineering and development, 18 (1), pp. 129-142.
4. Ahmad, S., Simonovic, S. (2005), An artificial neural network model for generating hydrograph from hydro-meteorological parameters, Journal of Hydrology, 315(1), pp. 236-251.
5. Ashgar Tosi, S. (2003), Drought prediction in Khorasan province. Thesis of M.S. Ferdodi university, water engineering Dept (in Persian).
6. Babazadeh, H., Shamsnia, A., Bostani, F., Noroozi Aghdam, E., Khodadadi Dehkordi, D. (2012), Analysis of Drought, Wetness Year and Forecasting of Climate Parameters, Precipitation and Temperature Using Stochastic Methods in Shiraz City, Journal of geography and planning, 41, pp. 23-47 (in Persian).
7. Bayazidi, M., Siose Morde, M., Asr Agah, A. (2016), Predicting meteorological drought using time series methods (case study: Salmas Basin), Journal of environmental and water engineering, 4, pp. 346-359 (in Persian).
8. Bloomfield, P., Nychka, D. (1992), Climate spectra and detecting climate change. Climatic Change, 21(3), pp.275-287.
9. Box, G., Jenkins, G.y, Reinsel, G. (2013), Time series analysis: forecasting and control, John Wiley and Sons.
10. Burlando, P., Montanari, A., Ranzi, R. (1996), Forecasting of storm rainfall by combined use of radar, rain gages and linear models, Atmospheric research, 42(1), pp. 199-216
11. Canova, F., Hansen, B. (1995), Are seasonal patterns constant over time? A test for seasonal stability, Journal of Business and Economic Statistics, 13(3), pp. 237-252.
12. Dayhoff, J. (1990), Neural Network Principles, Prentice-Hall International, U.S.A.
13. Dickey, D., Fuller, W. (1979), Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74 (366a), pp. 427-431.
14. Enders, W. (2008), Applied econometric time series, John Wiley and Sons.
15. Enders, Walters (1995), Applied econometric time series: John Wiley and Sons.
16. Eric, A. (2010), Modeling and Forecasting Inflation Rates in Ghana, an Application of SARIMA Models, dissertation. Thesis of M.S. submitted to the School of Technology and Business Studies.
17. Feizi, V., Farajzadeh, M., Nozari, R. (2010), Using Mann-Kendall test to Recognize of Climate Change in Sistanand Baluchestan province, Paper presented at the 4th International Congress of the Islamic World Geographers. April 2010, Zahedan, Iran (in Persian).
18. Folland, Ch. (1990), Observed Climatic Variation and Change, Climate Change: The IPCC Scientific Assessment. Cambridge University Press. pp.195-238.
19. Fox, Douglas (1981), Judging air quality model performance, Bulletin of the American Meteorological Society, 62(5), pp. 599-609.
20. Franses, P., Hobijn, B. (1997), Critical values for unit root tests in seasonal time series, Journal of Applied Statistics, 24(1), pp. 25-48.
21. French, M., Krajewski, W., Cuykendall, R. (1992). Rainfall forecasting in space and time using a neural network. Journal of Hydrology, 137(1), pp. 1-31.
22. Gandomkar, A. (2008), Analysis the precipitation trend in Zabol, The First International Conference on Water Crisis (in Persian).
23. Halabian, A.H., Darand, M. (2012). Forecasting rainfall in Esfahan using neural network methods, Journal of geographical science, 26, pp. 47-63 (in Persian).
24. Hamilton, J. (1994), Time series analysis, Vol. (2). Princeton university press Princeton.
25. Hansen, J. (1988), Global surface air temperature, Geology Letter, (15), pp. 323-326.
26. Hosseinalizadeh, M., Hassanalizadeh, N., Babanejad, M., Rezanejad, M. (2014), Monthly rainfall prediction using time series pachage in R software (case study: Araz Kose station- Golestan), Journal of geography and planning, 2, (in Persian).
27. Hung, N., Babel, M., Weesakul S., Thripathi, N. (2009), An artifitual neural network model for rainfall forcasting in Bangkok, Tailand, Hydrology and Earth System Science, 13, pp. 1414-1425.
28. Hylleberg, S., Engle, R., Granger, C., Yoo, B. (1990), Seasonal integration and cointegration. Journal of econometrics, 44(1), pp. 215-238.
29. Ildarmi, A, Zare Abiane, H., Bayat Vorkshi, M. (2013), Rainfall Estimation with Artificial Neural Network Based on Non-Rainfall Weather Data in Shiraz, Mashhad and Kerman Regions, Journal of geography and planning, 43, pp.21-40 (in Persian).
30. Jahangeer, A., Raeini, M.d, Ahmadi, M. (2008), Comparison of artificial neural networks (ANN)
31. Jamshidi, V. (1989), Evaluation of Temperature and Rainfall in Tehran City by Time Series, Thesis of M.S. Tarbiat Modares University, Statistics, Dept. (in Persian).
32. Jones, P., Raper, S., Bradley, R., Diaz, H., Kellyo, P., Wigley, T. (1986), Northern Hemisphere surface air temperature variations: 1851-1984, Journal of Climate and Applied Meteorology, 25(2), pp. 161-179.
33. Karamoz, M., Araghineghad, S. (2005), Advanced Hydrology, Amir Kabir pub. (in Persian).
34. Kheradmand-Nia, M., Asakereh, H. (2001), Pattering of ARIMA for Annual Average Temperature in Jask (Iran), Paper presented at the 3rd Conference of Stochastic Process, Isfahan University.
35. Khorami, M. Bozorgnia, A. (2007), Time series analysis using MINITAB14, Sokhan Gostar publication, (in Persian).
36. Khosravi, M. Shakiba, H. (2010), Precipitation forecasting using Artificial Neural Network for Flood Management Case Study: Iranshahr region (South East of Iran), Paper presented at the 4th International Congress of the Islamic World Geographers, April 2010, Zahedan, Iran (in Persian).
37. Kleiber, Ch. (2008), Applied Econometrics with R.: Springer Science Business Media, LLC, NY, USA.
38. Kumar, A. (2000), Dispersion and risk modeling, Department of Civil Engineering, University of Toledo.
39. Kumar, S., Tripathy, D., Nayak, S. Mohaparta, S. (2013), Prediction of rainfall in India using artifitial neural network models, International Journal of intelligent system and applications, 12, pp. 1-22.
40. Maleki, M. (1989), Investigation and Modeling of Temperature and Rainfall in West Country
41. Mislan, H., Hardwinarto, S. Aipassa, M. (2015), Rainfall monthly prediction based on artifitual neural network: a case study in Tenggarong station, east Kalimantan, Indonesia, International conference in computer science and computational intelligent, pp. 142-151.
42. Nayak, D., Mahapatra, A., Mishra, P. (2013), A survey on rainfall prediction using artificial neural network, International journal of computer applications, 72 (16), pp. 32-40.
43. Noakes, D., McLeod, I., Hipel, K. (1985), Forecasting monthly river flow time series. International Journal of Forecasting, 1(2), pp. 179-190.
44. Omidvar, K., Nabavi, M., Meysam Samareh, Gh. (2015), Evaluating Narx backward neural network in daily precipitation forecasting in Karman. Journal of natural geography, 7(27), pp. 73-90 (in Persian).
45. Paras, D., Mathur, S. (2012), A simple weather forecasting model using mathematical regression, Indian research journal of extension education, 1, pp. 161-169.
46. Pfaff, B. (2008), Analysis of integrated and cointegrated time series with R: Springer.
47. Rasuli, A. (2002), Modeling of Climate Parameters in Country North-West. Forecasting Monthly Temperature of Tabriz City (Iran) by ARIMA model, Tabriz Uni., Journal of Sociology Science, 8.
48. Rezaie, M., Nahtani, M., Mogham Nia, A., Abkar, A., Rezaie M. (2015), Comparison of Artificial Neural Network and SDSM Methods in the Downscaling of Annual Rainfall in the HadCM3 Modelling (Case study: Kerman, Ravar and Rabor), Journal of water resource engineering, 24, pp. 25-40 (in Persian).
49. Rivero, C., Pucheta, J. (2014), Forecasting rainfall time series with stochastic output approximated by neural networks Bayesian approach, International journal of advanced computer science and applications, 5 (6), pp. 145-151.
50. Shabankari, M. Halbian, A. (2012), Synoptic relationship between daily rainfall variability in southern coastlines of Iran and changes in sea level pressure. Journal of geographic research. 27(1), pp. 165-184 (in Persian).
51. Shumway, R., Stoffer, D. (2010), Time series analysis and its applications: with R examples, Springer.
52. Taylor, R. (1997), On the practical problems of computing seasonal unit root tests, International Journal of Forecasting, 13(3), pp. 307-318.
53. Valverde Ramírez, M., Fraga de Campos Velho, H., Jesus Ferreira, N. (2005), Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region, Journal of Hydrology, 301(1), pp. 146-162.
54. Yevjevich, V. (1987), Stochastic models in hydrology, Stochastic Hydrology & Hydraulics, 1, pp.17-36.
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DOI: 10.18869/acadpub.geores.32.1.149


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Modeling Monthly Rainfall in Southern Baluchestan Basin. geores. 2017; 32 (1) :149-162
URL: http://georesearch.ir/article-1-109-en.html
Volume 32, Number 1 (6-2017) Back to browse issues page
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