COMPARISON OF STOCHASTIC AND ARTIFICIAL NEURAL NETWORKS MODELS IN MODELING AND FORECASTING THE STANDARDIZED PRECIPITATION INDEX VALUES AND CLASSES

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HEJABI S.; BAZRAFSHAN J.; GHAHREMAN N.

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Journal: Physical Geography Research Year:2013 | Volume:45 | Issue:2 (84) Page(s): 91-108

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Information Journal Paper

Title

COMPARISON OF STOCHASTIC AND ARTIFICIAL NEURAL NETWORKS MODELS IN MODELING AND FORECASTING THE STANDARDIZED PRECIPITATION INDEX VALUES AND CLASSES

Author(s)

HEJABI S. | BAZRAFSHAN J. | GHAHREMAN N. | Issue Writer Certificate

Keywords

ARTIFICIAL NEURAL NETWORK MODELSQ2

DROUGHTQ2

FORECASTINGQ2

STANDARDIZED PRECIPITATION INDEXQ3

STOCHASTIC MODELSQ2

WET AND DRY CLIMATESQ2

Abstract

Drought is a temporary and recurring meteorological event which results from the lack of precipitation over an unusual extended period of time. Early indication of possible DROUGHTs can help set out DROUGHT mitigation strategies and measures, in advance. Therefore, the DROUGHT FORECASTING plays an important role in the planning and management of water resource systems.Stochastic models have been extensively used for FORECASTING hydrologic variables such as annual and monthly stream flow, precipitation, and etc. in the past. But they are basically linear models assuming that data are stationary, and have a limited ability to capture non-stationaries and nonlinearities in the hydrologic data. However, it is necessary to consider alternative models when nonlinearity and non-stationarity play a significant role in the FORECASTING. In the recent decades, artificial neural networks have shown great ability in modeling and FORECASTING nonlinear and non-stationary time series due to their innate nonlinear property and ﬂexibility for modeling.The aim of this study is to compare the stochastic and ARTIFICIAL NEURAL NETWORK MODELS in FORECASTING the STANDARDIZED PRECIPITATION INDEX (SPI) in some stations of Iran. This is because of the multiplicity of DROUGHT occurrences in Iran and the necessity to determine the best FORECASTING model.

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APA: Copy

HEJABI, S., BAZRAFSHAN, J., & GHAHREMAN, N.. (2013). COMPARISON OF STOCHASTIC AND ARTIFICIAL NEURAL NETWORKS MODELS IN MODELING AND FORECASTING THE STANDARDIZED PRECIPITATION INDEX VALUES AND CLASSES. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, 45(2 (84)), 91-108. SID. https://sid.ir/paper/138819/en

Vancouver: Copy

HEJABI S., BAZRAFSHAN J., GHAHREMAN N.. COMPARISON OF STOCHASTIC AND ARTIFICIAL NEURAL NETWORKS MODELS IN MODELING AND FORECASTING THE STANDARDIZED PRECIPITATION INDEX VALUES AND CLASSES. PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2013;45(2 (84)):91-108. Available from: https://sid.ir/paper/138819/en

IEEE: Copy

S. HEJABI, J. BAZRAFSHAN, and N. GHAHREMAN, “COMPARISON OF STOCHASTIC AND ARTIFICIAL NEURAL NETWORKS MODELS IN MODELING AND FORECASTING THE STANDARDIZED PRECIPITATION INDEX VALUES AND CLASSES,” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. 45, no. 2 (84), pp. 91–108, 2013, [Online]. Available: https://sid.ir/paper/138819/en

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