Drought Forecasting: A Bibliometric Analysis and Future Research Directions

Abstract

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.