Bibliometric Analysis of Methods and Tools for Drought Monitoring and Prediction in Africa

被引:25
作者
Adisa, Omolola M. [1 ,2 ]
Masinde, Muthoni [1 ]
Botai, Joel O. [1 ,2 ]
Botai, Christina M. [2 ]
机构
[1] Cent Univ Technol, Dept Informat Technol, Private Bag X200539, ZA-9300 Bloemfontein, South Africa
[2] South African Weather Serv, Private Bag X097, ZA-0001 Pretoria, South Africa
关键词
drought; monitoring; prediction; remote sensing; GIS; machine learning; LIMPOPO RIVER-BASIN; AGRICULTURAL DROUGHT; SOUTHERN AFRICA; METEOROLOGICAL DROUGHT; VEGETATION CONDITION; SEVERITY INDEX; REGIONS; HORN; RISK; REANALYSIS;
D O I
10.3390/su12166516
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The African continent has a long history of rainfall fluctuations of varying duration and intensities. This has led to varying degrees of drought conditions, triggering research interest across the continent. The research presented here is a bibliometric analysis of scientific articles on drought monitoring and prediction published in Africa. Scientific data analysis was carried out based on bibliometric mapping techniques applied to 332 scientific publications (1980 to 2020) retrieved from the Web of Science (WoS) and Scopus databases. In addition, time series of Standardized Precipitation Evapotranspiration Index for the previous 6 months (SPEI-6) over six regions in the continent was analysed giving the relative comparison of drought occurrences to the annual distribution of the scientific publications. The results revealed that agricultural and hydrological drought studies contributed about 75% of the total publications, while the remaining 25% was shared among socioeconomic and meteorological studies. Countries in the southern, western, and eastern regions of Africa led in terms of scientific publications during the period under review. The results further indicated that the continent experienced drought conditions in the years 1984, 1989, 1992, and 1997, thereby inducing an increase in the number of scientific publications on drought studies. The results show that the tools of analysis have also changed from simple statistics to the use of geospatial tools such as Remote Sensing (RS) and Geographical Information System (GIS) models, and recently Machine Learning (ML). The ML, particularly, contributed about 11% of the total scientific publications, while RS and GIS models, and basic statistical analysis account for about 44%, 20%, and 25% respectively. The integration of spatial technologies and ML are pivotal to the development of robust drought monitoring and drought prediction systems, especially in Africa, which is considered as a drought-prone continent. The research gaps presented in this study can help prospective researchers to respond to the continental and regional drought research needs.
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页数:22
相关论文
共 93 条
[1]  
Adeaga O, 2011, IAHS-AISH P, V344, P1
[2]   A Mixed Model Approach to Vegetation Condition Prediction Using Artificial Neural Networks (ANN): Case of Kenya's Operational Drought Monitoring [J].
Adede, Chrisgone ;
Oboko, Robert ;
Wagacha, Peter Waiganjo ;
Atzberger, Clement .
REMOTE SENSING, 2019, 11 (09)
[3]   A multivariate approach for persistence-based drought prediction: Application to the 2010-2011 East Africa drought [J].
AghaKouchak, Amir .
JOURNAL OF HYDROLOGY, 2015, 526 :127-135
[4]   Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research [J].
Aria, Massimo ;
Misuraca, Michelangelo ;
Spano, Maria .
SOCIAL INDICATORS RESEARCH, 2020, 149 (03) :803-831
[5]   bibliometrix: An R-tool for comprehensive science mapping analysis [J].
Aria, Massimo ;
Cuccurullo, Corrado .
JOURNAL OF INFORMETRICS, 2017, 11 (04) :959-975
[6]  
Awad M., 2015, Efficient learning machines: theories, concepts, and applications for engineers and system designers, DOI DOI 10.1007/978-1-4302-5990-9_4
[7]   Exploring hydro-meteorological drought patterns over the Greater Horn of Africa (1979-2014) using remote sensing and reanalysis products [J].
Awange, J. L. ;
Khandu ;
Schumacher, M. ;
Forootan, E. ;
Heck, B. .
ADVANCES IN WATER RESOURCES, 2016, 94 :45-59
[8]   Developing a hybrid model of prediction and classification algorithms for building energy consumption [J].
Banihashemi, Saeed ;
Ding, Grace ;
Wang, Jack .
1ST INTERNATIONAL CONFERENCE ON ENERGY AND POWER, ICEP2016, 2017, 110 :371-376
[9]   Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia [J].
Bayissa, Yared ;
Tadesse, Tsegaye ;
Demisse, Getachew .
REMOTE SENSING, 2019, 11 (04)
[10]   Drought risk assessment using remote sensing and GIS techniques [J].
Belal, Abdel-Aziz ;
El-Ramady, Hassan R. ;
Mohamed, Elsayed S. ;
Saleh, Ahmed M. .
ARABIAN JOURNAL OF GEOSCIENCES, 2014, 7 (01) :35-53