气候变化领域集成服务门户(CCPortal): Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm

CCPortal

DOI	10.1007/s11069-020-04180-9
	Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
	Mohamadi S.; Sammen S.S.; Panahi F.; Ehteram M.; Kisi O.; Mosavi A.; Ahmed A.N.; El-Shafie A.; Al-Ansari N.
发表日期	2020
ISSN	0921030X
起始页码	537
结束页码	579
卷号	104 期号:1
英文摘要	The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP), radial basis function neural network (RBFNN), and support vector machine (SVM) models to forecast meteorological droughts in Iran. The spatial–temporal pattern of droughts in Iran was also found using recorded observation data from 1980 to 2014. A nomadic people algorithm (NPA) was utilized to train the ANFIS, MLP, RBFNN, and SVM models. Additionally, the NPA was benchmarked against the bat algorithm, salp swarm algorithm, and krill algorithm (KA). The hybrid ANFIS, MLP, RBFNN, and SVM models were used to forecast the 3-month standardized precipitation index. New evolutionary algorithms were utilized to improve the convergence speed of the soft computing models and their accuracy. First, random stations, namely, in Azarbayjan (northwest Iran), Khouzestan (southwest Iran), Khorasan (northeast Iran), and Sistan and Balouchestan (southeast Iran) were selected for the testing of the models. According to the results obtained from the Azarbayjan station, the Nash–Sutcliffe efficiency (NSE) was 0.93, 0.86, 0.85, and 0.83 for the ANFIS–NPA, MLP–NPA, RBFNN–NPA, and SVM–NPA models, respectively. For Sistan and Baloucehstan, the results indicated the superiority of the ANFIS–NPA model, followed by the MLP–NPA model, compared to the RBFNN–NPA and SVM–NPA models, and suggested that the hybrid models performed better than the standalone MLP, RBFNN, ANFIS, and SVM models. The second aim of the study was to capture the relationship between large-scale climate signals and drought indices by using a wavelet coherence analysis. The general results indicated that the NPA and wavelet coherence analysis are useful tools for modelling drought indices. © 2020, Springer Nature B.V.
关键词	ANFIS Drought MLP Nomadic people optimization algorithm SPI SVM
英文关键词	algorithm; drought; machine learning; modeling; optimization; prediction; support vector machine; water management; Iran; Euphausiacea
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205835
作者单位	Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran; Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah, Diyala Governorate, Iraq; Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran; Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran; Department of Civil Engineering, School of Technology, IIia State University, Tbilisi, 0162, Georgia; Institute of Research and Development, Duy Tan University, Da Nang, 550000, Viet Nam; Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Kajang, Selangor Darul Ehsan 43000, Malaysia; Departme...
推荐引用方式 GB/T 7714	Mohamadi S.,Sammen S.S.,Panahi F.,et al. Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm[J],2020,104(1).
APA	Mohamadi S..,Sammen S.S..,Panahi F..,Ehteram M..,Kisi O..,...&Al-Ansari N..(2020).Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm.Natural Hazards,104(1).
MLA	Mohamadi S.,et al."Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm".Natural Hazards 104.1(2020).