globalchange  > 气候变化与战略
DOI: 10.1007/s11069-020-04180-9
论文题名:
Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
作者: Mohamadi S.; Sammen S.S.; Panahi F.; Ehteram M.; Kisi O.; Mosavi A.; Ahmed A.N.; El-Shafie A.; Al-Ansari N.
刊名: Natural Hazards
ISSN: 0921030X
出版年: 2020
卷: 104, 期:1
起始页码: 537
结束页码: 579
语种: 英语
中文关键词: 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
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.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/168634
Appears in Collections:气候变化与战略

Files in This Item:

There are no files associated with this item.


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; Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, 50603, Malaysia; National Water Center (NWC), United Arab Emirates University, P.O. Box 15551, Al Ain, United Arab Emirates; Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Luleå, 97187, Sweden

Recommended Citation:
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]. Natural Hazards,2020-01-01,104(1)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Mohamadi S.]'s Articles
[Sammen S.S.]'s Articles
[Panahi F.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Mohamadi S.]'s Articles
[Sammen S.S.]'s Articles
[Panahi F.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Mohamadi S.]‘s Articles
[Sammen S.S.]‘s Articles
[Panahi F.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.