There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model.