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Paper Information

Journal:   WATER ENGINEERING   SUMMER 2011 , Volume 4 , Number 9; Page(s) 17 To 27.
 
Paper: 

METEOROLOGICAL DROUGHT ANALYSIS IN THE PROVINCE OF TEHRAN BASED ON THE ARTIFICIAL NEURAL NETWORKS

 
 
Author(s):  ARAB SOLGHAR A.A.*, SEDGHI H., MALEKI M.
 
* SCIENCE AND RESEARCH BRANCH, ISLAMIC AZAD UNIVERSITY, TEHRAN
 
Abstract: 

Drought is the most serious natural hazard that affects human societies in the arid- and semi-arid lands; therefore, contemplating its occurrence and severity is of the utmost importance in planning and implementing the water shortage crisis management. The main objective of this research was to develop an approach to analyze the spatial patterns of meteorological droughts based on annual precipitation data in the Province of Tehran. The normalized and standardized precipitation data were classified into certain degrees of drought severity (extreme, severe, mild, and non drought) based on a number of truncation levels corresponding to specified quartiles of the standard normal distribution using a nonparametric spatial analysis neural network algorithm. The probability of drought severity at any given point in the region was determined, and a point degree was assigned to it as a Bayesian drought severity index. This index was used for constructing the drought severity maps in the Province of Tehran that display the spatial variability of drought severity on an annual basis. The results indicated that in the 1974-2008 period, 16.7% of the years experienced extreme droughts, 26.65% suffered from severe droughts, 30.00% faced mild droughts, and 23.00% escaped droughts altogether.

 
Keyword(s): DROUGHT, BAYESIAN DROUGHT SEVERITY INDEX, PROVINCE OF TEHRAN, NONPARAMETRIC SPATIAL ANALYSIS NEURAL NETWORK ALGORITHM
 
References: 
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