干旱区生态环境知识资源中心(Arid): ANFIS-based soft computing models for forecasting effective drought index over an arid region of India

Arid

DOI	10.2166/aqua.2023.204
	ANFIS-based soft computing models for forecasting effective drought index over an arid region of India
	Kikon, Ayilobeni; Dodamani, B. M.; Barma, Surajit Deb; Naganna, Sujay Raghavendra
通讯作者	Kikon, A
来源期刊	AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY
ISSN	2709-8028
EISSN	2709-8036
出版年	2023
卷号	72 期号:6 页码:930-946
英文摘要	Drought is a natural hazard that is characterized by a low amount of precipitation in a region. In order to evaluate the drought-related issues that cause chaos for human well-being, drought indices have become increasingly important. In this study, the monthly precipitation data from 1964 to 2013 (about 50 years) of the Jodhpur district in the drought-prone Rajasthan state of India was used to derive the effective drought index (EDI). The machine learning models hybridized with evolutionary optimizers such as the genetic algorithm adaptive neurofuzzy inference system (GA-ANFIS) and particle swarm optimization ANFIS (PSO-ANFIS) were used in addition to the generalized regression neural network (GRNN) to predict the EDI index. Using the partial autocorrelation function (PACF), models for forecasting the monthly EDI were constructed with 2-, 3- and 5-input combinations to evaluate their outcomes based on various performance indices. The results of the different combination models were compared. With reference to 2-input and 3-input combination models, both GA-ANFIS and PSOANFIS show better performance results with R-2 = 0.75, while among the models with 5-input combination, GA-ANFIS depicts better performance results compared to other models with R-2 = 0.78. The results are presented suitably with the aid of scatter plots, Taylor's diagram and violin plots. Overall, the GA-ANFIS and PSO-ANFIS models outperformed the GRNN model.
英文关键词	drought EDI forecasting GA-ANFIS GRNN PSO-ANFIS
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000993817500001
WOS关键词	RIVER-BASIN ; ALGORITHM
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395412
推荐引用方式 GB/T 7714	Kikon, Ayilobeni,Dodamani, B. M.,Barma, Surajit Deb,et al. ANFIS-based soft computing models for forecasting effective drought index over an arid region of India[J],2023,72(6):930-946.
APA	Kikon, Ayilobeni,Dodamani, B. M.,Barma, Surajit Deb,&Naganna, Sujay Raghavendra.(2023).ANFIS-based soft computing models for forecasting effective drought index over an arid region of India.AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY,72(6),930-946.
MLA	Kikon, Ayilobeni,et al."ANFIS-based soft computing models for forecasting effective drought index over an arid region of India".AQUA-WATER INFRASTRUCTURE ECOSYSTEMS AND SOCIETY 72.6(2023):930-946.