干旱区生态环境知识资源中心(Arid): Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting

Arid

DOI	10.3390/atmos13091436
	Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting
	Alawsi, Mustafa A.; Zubaidi, Salah L.; Al-Ansari, Nadhir; Al-Bugharbee, Hussein; Ridha, Hussein Mohammed
通讯作者	Al-Ansari, N
来源期刊	ATMOSPHERE
EISSN	2073-4433
出版年	2022
卷号	13 期号:9
英文摘要	Modelling drought is vital to water resources management, particularly in arid areas, to reduce its effects. Drought severity and frequency are significantly influenced by climate change. In this study, a novel hybrid methodology was built, data preprocessing and artificial neural network (ANN) combined with the constriction coefficient-based particle swarm optimisation and chaotic gravitational search algorithm (CPSOCGSA), to forecast standard precipitation index (SPI) based on climatic factors. Additionally, the marine predators algorithm (MPA) and the slime mould algorithm (SMA) were used to validate the performance of the CPSOCGSA algorithm. Climatic factors data from 1990 to 2020 were employed to create and evaluate the SPI 1, SPI 3, and SPI 6 models for Al-Kut City, Iraq. The results indicated that data preprocessing methods improve data quality and find the best predictors scenario. The performance of CPSOCGSA-ANN is better than MPA-ANN and SMA-ANN algorithms based on various statistical criteria (i.e., R-2, MAE, and RMSE). The proposed methodology yield R-2 = 0.93, 0.93, and 0.88 for SPI 1, SPI 3, and SPI 6, respectively.
英文关键词	drought forecast model metaheuristic algorithms artificial neural network standardised precipitation index Iraq
类型	Article
语种	英语
开放获取类型	Green Submitted, gold
收录类别	SCI-E
WOS记录号	WOS:000858114300001
WOS关键词	SINGULAR SPECTRUM ANALYSIS ; MARINE PREDATORS ALGORITHM ; ARTIFICIAL NEURAL-NETWORKS ; URBAN WATER DEMAND ; AWASH RIVER-BASIN ; TIME-SERIES ; PREDICTION ; PRECIPITATION ; RAINFALL ; MODELS
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/391923
推荐引用方式 GB/T 7714	Alawsi, Mustafa A.,Zubaidi, Salah L.,Al-Ansari, Nadhir,et al. Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting[J],2022,13(9).
APA	Alawsi, Mustafa A.,Zubaidi, Salah L.,Al-Ansari, Nadhir,Al-Bugharbee, Hussein,&Ridha, Hussein Mohammed.(2022).Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting.ATMOSPHERE,13(9).
MLA	Alawsi, Mustafa A.,et al."Tuning ANN Hyperparameters by CPSOCGSA, MPA, and SMA for Short-Term SPI Drought Forecasting".ATMOSPHERE 13.9(2022).