干旱区生态环境知识资源中心(Arid): Evaluating the efficiency of the neural network to other methods in predicting drought in arid and semi-arid regions of western Iran

Arid

DOI	10.1007/s12517-019-4654-z
	Evaluating the efficiency of the neural network to other methods in predicting drought in arid and semi-arid regions of western Iran
	Azizi, E.; Tavakoli, Mohsen; Karimi, H.; Faramarzi, M.
通讯作者	Tavakoli, Mohsen
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2019
卷号	12 期号:15
英文摘要	Drought as one of the natural phenomena and one of the greatest climate problems has been of central importance and investigated as a matter of necessity. In recent decades, Iran and especially Ilam province faced drought problem and this research was conducted with the aim of predicting drought in Ilam (semi-arid region) and Dehloran (arid area) stations, as well as evaluating the efficiency of the neural network. In this study, the data of temperature, precipitation, relative humidity, wind speed, and monthly sunny hours in the period of 1983-2013 were used to calculate the standardized precipitation index (SPI) and moving average of 3, 5, and 7 years. Also, artificial neural network was employed for monitoring the drought in Ilam (semi-arid region) and Dehloran (arid region) stations. Accordingly, the artificial neural network (ANN) was modeled after determining the best input composition and the best number of data using the backpropagation algorithm and C-Sharp programming language. Then, the possible future drought was predicted in both stations. The architecture of network was optimized in form of 5-30-1, 5 neurons in input layer (precipitation, temperature, relative humidity, wind speed, and sunny hours); 30 neurons in the hidden layer and a neuron in the output layer (SPI) after training and repeated errors. In order to evaluate ANN performance in climate elements simulation, four designed models were investigated using different learning functions and the number of variously hidden neurons and the error was calculated using MAE, RMSE, and R-2 indices. The results showed that in the stations, bipolar sigmoid function pattern and updating weights by the method of the moment were introduced as the optimal model, due to the lowest error rate and highest correlation between input and output data. Then, outputs of the program were compared with observed SPI. The results of statistic method and output of network showed that the stations entered a prolonged drought phase from 1996-1997 to 2012. Although the stations showed short periods of wet year, the trend of these droughts has continued so far, with the difference that the severity of droughts in Dehloran station (Arid region) was higher and in this long period of drought, short, 7-, 5-, and 3-year droughts occurred and it was completely consistent with the definition of drought.
英文关键词	ANN SPI Moving average Ilam Iran
类型	Article
语种	英语
国家	Iran
收录类别	SCI-E
WOS记录号	WOS:000478567600004
WOS关键词	RIVER-BASIN ; INDEXES ; MODEL ; AUSTRALIA ; SYSTEM ; MARKOV ; FUZZY
WOS类目	Geosciences, Multidisciplinary
WOS研究方向	Geology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/214344
作者单位	Ilam Univ, Fac Agr, Dept Nat Resources, Ilam, Iran
推荐引用方式 GB/T 7714	Azizi, E.,Tavakoli, Mohsen,Karimi, H.,et al. Evaluating the efficiency of the neural network to other methods in predicting drought in arid and semi-arid regions of western Iran[J],2019,12(15).
APA	Azizi, E.,Tavakoli, Mohsen,Karimi, H.,&Faramarzi, M..(2019).Evaluating the efficiency of the neural network to other methods in predicting drought in arid and semi-arid regions of western Iran.ARABIAN JOURNAL OF GEOSCIENCES,12(15).
MLA	Azizi, E.,et al."Evaluating the efficiency of the neural network to other methods in predicting drought in arid and semi-arid regions of western Iran".ARABIAN JOURNAL OF GEOSCIENCES 12.15(2019).