A review of machine learning methods for drought hazard monitoring and forecasting: Current research trends, challenges, and future research directions. (March 2022)

• Record Type: Journal Article
• Authors: Prodhan, Foyez Ahmed; Zhang, Jiahua; Hasan, Shaikh Shamim; Pangali Sharma, Til Prasad; Mohana, Hasiba Pervin
• Is Part Of: Environmental modelling & software. Volume 149(2022)
• Publication Date: 2022-03
• Subjects: Machine learning -- Deep learning -- Forecasting -- Drought -- Big data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105327
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Machine learning is a dynamic field with wide-ranging applications, including drought modeling and forecasting. Drought is a complex, devastating natural disaster for which it is challenging to develop effective prediction models. Therefore, our review focuses on basic information about machine learning methods (MLMs) and their potential applications in developing efficient and effective drought forecasting models. We observed that MLMs have achieved significant advances in the robustness, effectiveness, and accuracy of the algorithms for drought modelling in recent years. The performance comparison of MLMs with other models provides a comprehensive conception of different model evaluation metrics. Further challenges of MLMs, such as inadequate training data sets, noise, outliers, and observation bias for spatial data sets, are explored. Finally, our review conveys in-depth understanding to researchers on machine learning applications in forecasting and modeling and provides drought mitigation strategy guidance for policymakers. Highlights: A data-based review of machine learning methods (MLMs) used in drought forecasting is summarized in this study. Machine learning (ML) and deep learning (DL) have achieved significant breakthroughs for drought modelling in recent years. MLMs and hybrid models have shown superior performance compared to statistical, time-series, and probabilistic model. A knowledge-based system with big data platforms, including ML and DL, for future research direction is proposed.