干旱区生态环境知识资源中心(Arid): Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions

Arid

DOI	10.1007/s12517-021-06910-0
	Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions
	Aghelpour, Pouya; Singh, Vijay P.; Varshavian, Vahid
通讯作者	Aghelpour, P (corresponding author), Bu Ali Sina Univ, Fac Agr, Dept Water Sci & Engn, Hamadan, Hamadan, Iran.
来源期刊	ARABIAN JOURNAL OF GEOSCIENCES
ISSN	1866-7511
EISSN	1866-7538
出版年	2021
卷号	14 期号:7
英文摘要	Seasonal total precipitation is one of the important meteorological variables and its prediction is useful for the supply of water to different sectors. This study aims to compare Seasonal Autoregressive Integrated Moving Average (SARIMA), Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System-Subtractive Clustering (ANFIS-SC), and ANFIS-Fuzzy Cluster Means (ANFIS-FCM) for the prediction of seasonal precipitation. The precipitation data were obtained for the 1951-2018 period from 8 stations located in different climatic zones of Iran. The stations and their climates are Anzali (per-humid moderate climate), Babolsar (humid moderate climate), Kermanshah (semi-arid cold climate), Shiraz (semi-arid moderate climate), Bushehr (arid warm climate), Shahroud (arid cold climate), Isfahan (extra-arid cold climate), and Zahedan (extra-arid moderate climate). The time-lagged precipitation as input for all models was chosen using the autocorrelation function (ACF), and the data were divided into two periods: 1951-2001 for training (75%) and 2002-2018 for testing (25%). Based on the evaluation criteria (root mean squared error [RMSE], normalized root mean squared error [NRMSE], Wilmott Index [WI], Akaike Information Criterion [AIC], and Bayesian Information Criterion [BIC]), results showed that the SARIMA stochastic model was more accurate than the artificial intelligence methods and had the least over- and under-estimations. MLs exhibited good prediction accuracy, but ANFIS-FCM had a little higher accuracy. Consequently, due to the high accuracy and simplicity, the stochastic model is reported as the best predictor for seasonal precipitation in all climates. In terms of the R-2 values, the models showed better fitting in wet and normal years than in drought years. Further, the model predictions were more accurate in per-humid and humid areas than in arid and extra-arid climates. Also, the NRMSE values were in the range of 0.1 and 0.2, which indicated that SARIMA's performance was medium and well. A significant result of this study was that results for different climates based on RMSE were completely opposite to those based on NRMSE, WI, and R-2. This contrast was caused by the neglect of data range in the RMSE equation, so it is not a good choice to compare the results under different climates and it is better to use its normalized form NRMSE.
英文关键词	Seasonal precipitation ANFIS-FCM Normalized RMSE Time series prediction SARIMA Stochastic models
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000631492600001
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/2XILL650/368936
作者单位	[Aghelpour, Pouya; Varshavian, Vahid] Bu Ali Sina Univ, Fac Agr, Dept Water Sci & Engn, Hamadan, Hamadan, Iran; [Singh, Vijay P.] Texas A&M Univ, Dept Biol & Agr Engn, 321 Scoates Hall,2117 TAMU, College Stn, TX 77843 USA; [Singh, Vijay P.] Texas A&M Univ, Zachiy Dept Civil & Environm Engn, 321 Scoates Hall,2117 TAMU, College Stn, TX 77843 USA
推荐引用方式 GB/T 7714	Aghelpour, Pouya,Singh, Vijay P.,Varshavian, Vahid. Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions[J],2021,14(7).
APA	Aghelpour, Pouya,Singh, Vijay P.,&Varshavian, Vahid.(2021).Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions.ARABIAN JOURNAL OF GEOSCIENCES,14(7).
MLA	Aghelpour, Pouya,et al."Time series prediction of seasonal precipitation in Iran, using data-driven models: a comparison under different climatic conditions".ARABIAN JOURNAL OF GEOSCIENCES 14.7(2021).