Predicting the Meteorological, Hydrological and Agricultural Droughts in Tehran Using Wavelet Method

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MASHAYEKHI H.; ZAKERI NIRI M.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2020 | Volume:16 | Issue:3 Page(s): 120-132

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Information Journal Paper

Title

Predicting the Meteorological, Hydrological and Agricultural Droughts in Tehran Using Wavelet Method

Author(s)

MASHAYEKHI H. | ZAKERI NIRI M. | Issue Writer Certificate

Keywords

Fourier TransformQ1

Wavelet Transform (WT)Q2

Possible Neural Network (PNN)Q1

Standardized Precipitation Index (SPI)Q1

StandarDized Runoff Index (SRI)Q1

Normalized Difference Vegetation Index (NDVI)Q1

Abstract

Prediction of drought is a main challenge even for countries which apply dynamic monthly prediction modeling systems. Due to the severe damage caused to humans by drought, it is important to predict drought as accurately as possible. Wavelet-neural network integration method is one of the most accurate methods to predict drought. An important and influential factor in the results of wavelet transforms is the use of the appropriate wavelet. The aim of this study is to determine the optimal wavelet for more accurate prediction of drought types. To this end, daily precipitation data, daily discharge, and satellite imagery related to Tehran were used for the period of 1969 to 2016 as the raw data to calculate the indicators. The wave transformations (WT) conversion method and PNN neural network have been used to predict droughts. From each time series of drought, wavelet transformations were performed using haar and bior1. 1 waves, and the prediction was made by neural network. It was found that the regression coefficient and error concentration for meteorological drought using haar wave are 0. 68039 and 0. 03368. The parameters for hydrological drought are respectively 0. 76271 and 0. 0666 and for agricultural drought are respectively 0. 92697 and 0. 1515. Then, the prediction of the three kinds of drought was also done with bior1. 1 wave and the regression coefficient and error concentration for meteorological drought (0. 71169 and 0. 9923), hydrological drought (0. 14147 and 0. 0329), and agricultural drought (0. 82049 and 0. 0016) were respectively found. The results showed that for the meteorological drought, the bior1. 1 wave and for hydrological and agricultural droughts, the haar wave gave better results.

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APA: Copy

MASHAYEKHI, H., & ZAKERI NIRI, M.. (2020). Predicting the Meteorological, Hydrological and Agricultural Droughts in Tehran Using Wavelet Method. IRAN-WATER RESOURCES RESEARCH, 16(3 ), 120-132. SID. https://sid.ir/paper/387928/en

Vancouver: Copy

MASHAYEKHI H., ZAKERI NIRI M.. Predicting the Meteorological, Hydrological and Agricultural Droughts in Tehran Using Wavelet Method. IRAN-WATER RESOURCES RESEARCH[Internet]. 2020;16(3 ):120-132. Available from: https://sid.ir/paper/387928/en

IEEE: Copy

H. MASHAYEKHI, and M. ZAKERI NIRI, “Predicting the Meteorological, Hydrological and Agricultural Droughts in Tehran Using Wavelet Method,” IRAN-WATER RESOURCES RESEARCH, vol. 16, no. 3 , pp. 120–132, 2020, [Online]. Available: https://sid.ir/paper/387928/en

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