Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index

Saeed Azimi () and Mehdi Azhdary Moghaddam ()
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Saeed Azimi: University of Sistan and Baluchestan
Mehdi Azhdary Moghaddam: University of Sistan and Baluchestan

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2020, vol. 34, issue 4, No 6, 1369-1405

Abstract: Abstract Determining the quantitative occurrence of droughts, discovering the spatial correlation of droughts, and predicting the occurrence of undesirable classes of drinking water quality and aquifer farming is of high importance. In this research, the Standardized Precipitation Index (SPI) was calculated and analyzed with a monthly survey of 26,027 wells in 609 study areas over a period of 20 years. After analyzing the missing data, the annual rainfall was forecasted in 362 synoptic stations of the country based on an artificial intelligence model. In addition, statistical relationships were extracted in order to achieve a comprehensive and historical map of the state of shortages and surpluses of water resources, as well as verification of artificial intelligence relationships in predicting base data cultivars. The results indicated that the “mild drought” indicator was steeper than the “near-normal drought” indicator. Eventually, the southern and eastern regions and certain parts of the northeast of the country in the period from 2005 to 2015 were placed in the 7th and 8th classes, which indicates severe drought. The analysis of the period 1994–2014 showed that the plains of the Sistan and Baluchestan Province in the south-east region of the country have been significantly more affected by the droughts. With the exception of the central parts of Khorasan, the general eastern, southeast, and southern regions of the country can be considered as an absolute drought class for the long term.

Keywords: Meteorological drought; Short term rainfall forecast; Artificial intelligence; Artificial neural networks; Gaussian process classification (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11269-020-02507-6

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