Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies

Precipitation deficit conditions and temperature anomalies are responsible for the occurrence of various types of natural disasters that cause tremendous loss of human life and economy of the country. Out of all natural disasters, drought is one of the most recurring and complex phenomenons. Prediction of the onset of drought poses significant challenges to societies worldwide. Drought occurrences occur across the world due to a variety of hydro-meteorological causes and anomalies in sea surface temperature. This article aims to provide a comprehensive overview of the fundamental concepts and characteristics of drought, its complex nature, and the various factors that influence drought, drought indicators, and advanced drought prediction models. An extensive survey is presented in the different drought prediction models employed in the literature, ranging from statistical approaches to machine learning and deep learning models. It has been found that advanced techniques like machine learning and deep learning models outperform traditional models by improving drought prediction accuracy. This review article critically examines the advancements in technology that have facilitated improved drought prediction, identifies the key challenges and opportunities in the field of drought prediction, and identifies the key trends and topics that are likely to give new directions to the future of drought prediction research. It explores the integration of remote sensing data, meteorological observations, hydrological modeling, and climate indices for enhanced accuracy. Under the frequently changing climate conditions, this comprehensive review provides a valuable resource for researchers, practitioners, and policymakers engaged in drought prediction and management and fosters a deeper understanding of their capabilities and limitations. This article paves the way for more accurate and effective drought prediction strategies, contributing to improved resilience and sustainable development in drought-prone regions.