# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
###### Abstract
These limitations highlight the need to enhance traditional prediction methods and develop new approaches that can better account for the changing environmental and climatic conditions in the Mekong Delta [17]. Drought index forecasting faces challenges in accurately predicting drought events due to deficiencies in capturing the complex interactions between meteorological and hydrological variables [18; 19]. These deficiencies can lead to intensified drought characteristics in certain climatic regions, impacting the precision of drought forecasts. To address these issues, a novel approach utilizing the Informer model has been proposed, which outperforms traditional methods like machine learning and deep learning models by better capturing information over time and enhancing long-term prediction accuracy. The models adapt to different timescales effectively, significantly improving the precision of SPEI prediction and aligning forecasted trends with actual observations. By incorporating diverse climatological variables and advanced forecasting techniques, the proposal aims to overcome the limitations of existing SPEI forecasting methods and provide more reliable drought predictions. Artificial Intelligence (AI) techniques have been increasingly used for drought forecasting. These models have been applied to improve current weather forecasts and as alternatives to conventional predictions of extreme events [20]. In the Mekong Delta of Vietnam, where drought has become more severe owing to climate change, ML-based models have been used to assess future drought hazards [21]. [PERSON] and [PERSON] demonstrated the effectiveness of this technology for predicting drought indices and rainfall, respectively [22; 23]. The use of climate indices as input variables in these models further enhances their accuracy. [PERSON] et al. compared the performance of various Artificial Intelligence models in drought forecasting, and the ARIMAX model showed the highest precision [24]. [PERSON] and [PERSON] provided a comprehensive review of the role of Artificial Intelligence in drought assessment, monitoring, and forecasting, highlighting its significance in these areas [25]. Deep learning models have shown significant potential in forecasting drought indices, such as the Standardized Precipitation Evapotranspiration Index (SPEI), which is crucial for regions like the Mekong Delta in Vietnam [21; 26; 27; 28]. These models, including Deep Neural Networks (DNNs), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNNs), can effectively handle complex datasets and extract relevant features for accurate predictions, aiding in mitigating drought impacts like crop failure and water shortages. By integrating deep learning methods like Long Short-Term Memory (LSTM) into forecasting systems, the accuracy and lead time of drought predictions can be improved, enabling policymakers to develop proactive risk management strategies and drought mitigation plans tailored to the long-term impacts of climate change in coastal regions like the MekongDelta. These studies collectively underscore the potential of Artificial Intelligence in improving drought forecasting in the Mekong Delta. Deep learning models, particularly Long Short-Term Memory (LSTM), have been successfully applied in forecasting drought indices like the Standardized Precipitation Evaporation Index (SPEI) to predict drought occurrences with high accuracy [29; 30]. These models utilize meteorological data, including precipitation and temperature, to make timely and precise predictions of drought conditions, which are crucial for mitigating the negative impacts on agriculture, water resources, and ecosystems [30; 31]. LSTM models have shown superior performance in predicting drought at different time scales, such as 1 month and 3 months, outperforming other hybrid models like EMD-ELM.