# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 5 Conclusions  
The study evaluated ARIMA, Gradient Boosting, XGBoost, RNN, and LSTM models, among other predictive models for Mekong Delta drought forecasting. Using error measures, including MAE, MSE, RMSE, R2 Score, and MAPE, the models were evaluated over several prediction steps and SPEIs. Research evidence demonstrates that LSTM models frequently outperform other models, particularly for longer prediction intervals and SPEI durations. LSTM showed better R2 scores and reduced MAE, MSE, and RMSE values; therefore, this suggests its ability to capture intricate temporal patterns in drought time series data. Particularly for longer-term SPEIs, XGBoost also displayed excellent performance.  
The models consistently demonstrated enhanced performance as the SPEI scale progressively raised from 1 month to 12 months, as observed across all models and indicators. This implies that long-term drought conditions are more predictable than short-term fluctuations. Nevertheless, the accuracy of the forecasts decreased for all models as the prediction steps grew from one to six, with ARIMA exhibiting the most significant deterioration. By incorporating climatic indices such as SOI Anomaly and SST NINO4, the model's performance was improved, especially for projections that extend over longer periods of time. This emphasizes the significance of including major climate factors in drought forecasting models for the Mekong Delta region.  
These findings are consistent with earlier research on severe drought episodes in the Mekong Delta and are in line with similar studies conducted in other places, which have shown the effectiveness of advanced modeling tools for predicting troughs. The exceptional efficacy of LSTM and XGBoost models in comprehending intricate correlations among meteorological variables establishes a strong basis for making well-informed decisions in the management of water resources, agriculture, and the preservation of ecosystems in regions susceptible to drought.  
Although this study presents encouraging findings, its limitations stem from the use of past climate data and the possible inability to accurately represent extraordinary climate fluctuations caused by global warming. Subsequent investigations should examine the integration of up-to-the-minute data, supplementary environmental variables, and more sophisticated methods for selecting features in order to further improve the performance of the model. Furthermore, conducting tests on these models in other geographical regions could yield more profound insights into their resilience and suitability in varied environmental settings.  
To summarize, this study showcases the considerable capacity of Artificial Intelligence, specifically LSTM and XGBoost models, to improve drought forecasting capacities for the Mekong Delta. These advanced modeling tools enhance the precision, dependability, and effectiveness of drought predictions, thereby facilitating preemptive measures to alleviate the negative effects of droughs on communities, economies, and ecosystems in the region.  
[PERSON] led the investigation, conceptualization, methodology, statistics, visualization, and writing of the original and final drafts. P.N.D., T.H.L., T.T.D., T.T.N., T.N.M. ([PERSON]) and T.N.M. ([PERSON]) helped with conceptualization, writing, reviewing, visualization, and editing. All authors have read and agreed to the published version of the manuscript.