Droughts represent one of the most dangerous natural disasters in the world, due to their ability to progressively spread over large areas up to continental scale, as well as their adverse environmental, human and socio-economic effects. Unfortunately, these effects are increasingly accentuated under the influence of climate change. One of the main challenges today is to mitigate the damage associated with droughts by developing tools capable of predicting the occurrence of such events in advance. Many solutions have been implemented for this purpose. But with the great progress in artificial intelligence, many scientists propose the use of Machine Learning to provide more optimal solutions to the problem related to droughts. In the present study, a bibliometric analysis was conducted to assess the current level of research on forecasting and monitoring of droughts in the world in general, particularly in West Africa through different methods based on the artificial intelligence. A search for articles on the topic was performed in the Web of Science (WoS) database, which is a global, publisher-independent citation database. The search identified a total of 3284 documents and the collected data was analyzed using a bibliometric tool called Bibliometrix. The main results are presented and discussed, followed by some potential avenues for research.
References
[1]
Dikshit, A., Pradhan, B. and Huete, A. (2021) An Improved SPEI Drought Forecasting Approach Using the Long Short-Term Memory Neural Network. Journal of Environmental Management, 283, Article ID: 111979. https://doi.org/10.1016/j.jenvman.2021.111979
[2]
COP-15, Rapport (2022) La Secheresse en Chiffres 2022. Convention des Nations Unies sur la Lutte contre la Désertification (CNULCD).
[3]
Abhirup, D., Pradhan, B. and Alamri, A.M. (2020) Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches. Atmosphere, 11, Article 585. https://doi.org/10.3390/atmos11060585
[4]
Yuan, X., Wood, E.F., Chaney, N.W., Sheffield, J., Kam, J., Liang, M. and Guan, K. (2013) Probabilistic Seasonal Forecasting of African Drought by Dynamical Models. Journal of Hydrometeorology, 14, 1706-1720. https://doi.org/10.1175/JHM-D-13-054.1
[5]
Météo-France. (2020) Droughts and Climate Change. https://meteofrance.com/changement-climatique/observer/changement-climatique-et-secheresses
[6]
Yildirim, G., Rahman, A. and Singh, V.P. (2022) A Bibliometric Analysis of Drought Indices, Risk, and Forecast as Components of Drought Early Warning Systems. Water, 14, Article 253. https://doi.org/10.3390/w14020253
[7]
Harvey, C. (2022) “Absolutely No Doubt” That Climate Intensified Current Drought. https://www.scientificamerican.com/article/absolutely-no-doubt-that-climate-intensified-current-drought/#:~:text=Climate%20change%20increased%20the%20odds,change%20and%20extreme%20weather%20events
[8]
Kologo, O. (2008) Changements Climatiques, Reponses Endogenes D’Attenuation Et D’Adaptation Au Burkina Faso: Cas Des Mouvements De Populations. Université de Ouagadougou, Département de Sociologie, Ouagadougou.
[9]
Masih, I., Maskey, S., Mussá, F.E.F. and Trambauer, Patricia. (2014) A Review of Droughts on the African Continent: A Geospatial and Long-Term Perspective. Hydrology and Earth System Sciences, 18, 3635-3649. https://doi.org/10.5194/hess-18-3635-2014
[10]
Das, P., Naganna, S.R., Deka, P.C. and Pushparaj, J. (2020) Hybrid Wavelet Packet Machine Learning Approaches for Drought Modeling. Environmental Earth Sciences, 79, Article No. 221. https://doi.org/10.1007/s12665-020-08971-y
[11]
Mouatadid, S., Deo, R.C. and Adamowski, J.F. (2015) Prediction of SPEI Using MLR and ANN: A Case Study for Wilsons Promontory Station in Victoria. 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, 9-11 December 2015, 318-324. https://doi.org/10.1109/ICMLA.2015.87
[12]
Khan, M.H., Muhammad, N.S. and El-Shafie, A. (2020) Wavelet Based Hybrid ANN-ARIMA Models for Meteorological Drought Forecasting. Journal of Hydrology, 590, Article ID: 125380. https://doi.org/10.1016/j.jhydrol.2020.125380
[13]
Wamba, S.F. (2020) Humanitarian Supply Chain: A Bibliometric Analysis and Future Research Directions. Annals of Operations Research, 319, 937-963. https://doi.org/10.1007/s10479-020-03594-9
[14]
Wang, B., Pan, S.-Y., Ke, R.-Y., Wang, K. and Wei, Y.-M. (2014) An Overview of Climate Change Vulnerability: A Bibliometric Analysis Based on Web of Science Database. Natural Hazards, 74, 1649-1666. https://doi.org/10.1007/s11069-014-1260-y
[15]
OMM (2019) état du climat en Afrique 2019. Organisation Météorologique Mondiale.
[16]
Elgendi, M. (2019) 5 Features of a Highly Cited Article. Nature Index. https://www.natureindex.com/news-blog/five-features-highly-cited-scientific-article
[17]
Fuchs, B.A. and Svoboda, M. (2016) Manuel des indicateurs et indices de sécheresse. Organisation météorologique mondiale (OMM) et Partenariat mondial pour l’eau (GWP), Genève.
[18]
Belayneh, A., Adamowski, J. and Khalil, B. (2016) Short-Term SPI Drought Forecasting in the Awash River Basin in Ethiopia Using Wavelet Transforms and Machine Learning Methods. Sustainable Water Resources Management, 2, 87-101. https://doi.org/10.1007/s40899-015-0040-5
[19]
Liu, Z.N., Li, Q.F., Nguyen, L.B. and Xu, G.H. (2018) Comparing Machine-Learning Models for Drought Forecasting in Vietnam’s Cai River Basin. Polish Journal of Environmental Studies, 27, 2633-2646. https://doi.org/10.15244/pjoes/80866
[20]
Klisch, A. and Atzberger, C. (2016) Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series. Remote Sensing, 8, Article 267. https://doi.org/10.3390/rs8040267
