Artificial neural networks in drought prediction in the 21st century-A scientometric analysis

Droughts are the most spatially complex geohazard, which often lasts for years, thereby severely impacting socio-economic sectors. One of the critical aspects of drought studies is developing a reliable and robust forecasting model, which could immensely help drought management planners in adopting adequate measures. Further, the prediction of drought events are extremely challenging due to the involvement of several hydro-meteorological factors, which are further aggravated by the effect of climate change. Among the several techniques such as statistical, physical and data-driven that are used to forecast droughts, artificial neural networks provide one of the most robust approach. As droughts are inherently non-linear and multivariate in nature, the capability of neural networks to capture the dynamic relationship easily and efficiently has seen a rise in its use. Here we evaluate the most used architectures in the last two decades, using scientometric analysis. A general framework used in drought prediction studies is explained and examples from various continents are provided, thus exploring the topic in a global context. The findings show that using sophisticated input representation, the artificial intelligence-based solutions applied to drought prediction of hydro-meteorological variables have promising success, particularly in complex geographical scenarios. The future works need to focus on interpretable models, use of deep learning architectures for long lead time forecasting and use of neural networks to predict different drought characteristics like drought propagation and flash droughts. We also summarize the most widely used neural network approaches in spatial drought prediction, which would serve as a foundation for future research in drought prediction studies. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study focuses on developing reliable drought forecasting models, with artificial neural networks being highlighted as one of the most powerful techniques due to their ability to capture dynamic relationships among nonlinear and multivariate factors.