# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
###### Abstract  
Droughs have a substantial impact on water supplies, agriculture, and ecosystems worldwide. Agricultural sustainability and production in the Mekong Delta of Vietnam are being jeopardized by droughs caused by climate change. Conventional forecasting methods frequently struggle to comprehend the intricate dynamics of meteorological occurrences connected to drought, necessitating the use of sophisticated prediction techniques. This study assesses the effectiveness of various statistical models (ARIMA), machine learning, and deep learning models (Gradient Boosting, XGBoost, RNN, and LSTM) in forecasting the SPEI over different time periods (1, 3, 6, and 12 months) across six prediction intervals. The models were developed and evaluated using data from 11 meteorological stations spanning from 1985 to 2022. These models incorporated various climatic variables, including precipitation, temperature, humidity, potential evapotranspiration (PET), Southern Oscillation Index (SOI) Anomaly, and sea surface temperature in the NINO4 region (SST_NINO4). The results demonstrate that XGBoost and LSTM models exhibit outstanding performance, showcasing lower error metrics and higher R\({}^{2}\) values compared to Gradient Boosting and RNN. The performance of the model fluctuated depending on the forecast step, with error metrics often increasing with longer prediction horizons. The use of climatic indices improved the accuracy of the model. These findings are consistent with earlier research on drought episodes in the Mekong Delta and support studies from other areas that show the effectiveness of advanced modeling tools for predicting droughs. The work emphasizes the capacity of machine learning and deep learning models to enhance the precision of drought forecasting, which is vital for efficient water resource management and agricultural planning in places prone to drought. d 2024 202  
Accurately predicting drought is essential for reducing the negative effects of drought in the Mekong Delta, Vietnam, which impact agriculture, water management, and community resilience. It facilitates strategic agricultural planning, optimizes water resource allocation, and improves early warning systems to prepare for difficulties associated with drought. Accurate predictions are essential for adjusting to climate change through guiding sustainable practices and policy development. This holistic method for predicting drought enhances agricultural output in the area, guarantees sustainable water resources, and strengthens resistance to climate-related challenges. Thus, accurate drought forecasting is the cornerstone of proactive and effective risk management. It empowers stakeholders to make informed decisions, implement timely interventions, and build resilience in the face of a changing climate, ultimately contributing to the sustainable development of regions vulnerable to drought in the Mekong Delta. This study focuses on using Artificial Intelligence (AI) to anticipate the drought index in the Mekong Delta, with the goal of addressing and achieving critical objectives. Its primary goal is to overcome the limitations of traditional drought forecasting systems, which frequently fail to adequately describe the Mekong Delta's complex and fluctuating meteorological circumstances. The aim of this study is to dramatically increase the precision and dependability of drought forecasts using Artificial Intelligence. Furthermore, AI's better forecasting power is meant to assist in the delivery of timely warnings and the implementation of preventative actions. This proactive strategy enables communities, governments, and other stakeholders to appropriately prepare for and minimize the effects of impending water scarcity and related difficulties. Current methods for forecasting drought involve a combination of statistical, probabilistic, and data-driven approaches.