Amir Naghibi

As a subset of hydrological drought, groundwater drought arises from the specific features of aquifers and human-induced changes in the hydrological system. Because of the complexity of these underground systems and difficulties in obtaining field measurements, modern approaches such as machine learning and remote sensing have become popular tools for monitoring and forecasting. This chapter employs bibliometric analysis and data mining techniques rooted in knowledge discovery within research databases to comprehensively discover the present and future perspectives on the implementation of machine learning and remote sensing approaches for monitoring groundwater drought. To fulfill this objective, the initial step involves a comprehensive review of the developed indices for monitoring groundwater drought. In the subsequent stage, an analysis of articles extracted from the Scopus database is conducted to identify the main research domains through an assessment of the word cloud generated by VOSViewer software. Finally, the research gaps in groundwater drought are examined by word cloud to improve the forecasting accuracy using machine learning and remote sensing approaches. Although numerous studies have addressed different aspects of drought, the results highlight a noticeable gap in research specifically focused on groundwater drought. Additionally, “remote sensing” and “machine learning” have emerged as the most frequently used keywords in recent years.