Toward Drought Modeling in South Asia: Machine Learning Approaches, Challenges, and Opportunities

Abstract

Drought is an environmental and economic problem. Sustainable ecosystems, water resources,food security, and ecosystem sustainability. Machine all are severely affected by drought. Due to theincreasing frequency and severity of droughts caused by climate change. Effective drought modeling iscrucial for early warning systems and risk mitigation. Recent advances in machine learning (ML) and deeplearning (DL) techniques have been developed as potential drought modeling tools, which offer accurateand reliable drought detection. This review paper summarizes the drought modeling(Drought Prediction,Drought Detection and Drought Forecasting) approaches. This paper focuses on three main aspect. 1)The selection of the region for this study, for this study South Asia(SA) is selected as region of interest(ROI) that offer accurate drought modeling, providing policymakers and decision-makers with insightfulinformation. The geographical scope of this study is the region of South Asia. This region is selected becauseof its heavy reliance on agriculture. 2) This paper focuses on the current and future trends, challenges, andadvances of and vulnerability to droughts. The review offers a thorough grasp of how drought conditions areevaluated by gathering and analyzing the most important drought indicators and metrics specific to SouthAsia. The paper explores the current state-of-the-art in ML and DL for drought modeling. 3) This reviewencapsulates the indicator and metrics (Complex Machine learning and deep learning models) for droughtmodeling which are most relevant to the SA region. This study sum up as most common challenges indrought modeling are, highlighting current challenges such as incomplete and inconsistent datasets, lack ofexplainable and interpretable models, and unavailability of data for model uncertainty analysis. This studyproposes that these problems can be solved with modern machine learning techniques such as explainablemachine learning and federated or Lack of explainability and interpretability in complex ML/DL models,unavailability of benchmarks. Based on these challenges, this review suggests the following techniques toaddress these challenges: Data integration (Data fusion), distributed machine learning. (Federated Learning)and explainable AI (XAI, SHAP, LIME, etc.).