# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 3 Results
### Results of Evaluating Machine Learning Models
In the Gradient Boosting model, at Step 1, the MAE falls from 0.32 for SPEI1 to 0.085 for SPEI12. At the same time, the R2 Score increases from 0.84 to 0.94. The reason for this improvement is probably because longer-term drought indices, such as SPEI6 and SPEI12, are less influenced by short-term fluctuations and so exhibit greater predictability. The MAPE values exhibit a significant decline as the SPEI scale grows, suggesting that the percentage mistakes are considerably reduced for predictions made over longer periods. Model comparison: XGBoost and LSTM consistently demonstrate superior performance compared to the other models across all criteria. They attain the minimum MAE, MSE, RMSE, and MAPE values, as well as the maximum R-squared (R2) scores. Gradient Boosting and RNN models demonstrate moderate performance; however, ARIMA consistently exhibits inferior performance. As an illustration, in the case of SPEI12 at Step 1, XGBoost obtains a Mean Absolute Error (MAE) of 0.084 and an R2 Score of 0.97, whereas ARIMA has an MAE of 0.22 and an R2 Score of 0.91. These findings indicate that tree-based machine learning and deep learning models are better at capturing the intricate relationships in SPEI data, regardless of the SPEI scales and steps. The interaction between Step, SPEI, and Model: the effect of increasing steps is more noticeable for shorter SPEI scales and less sophisticated models. ARIMA's performance deteriorates more rapidly over time for SPEI1 compared to SPEI12. In contrast, XGBoost and LSTM exhibit consistent performance across the iterations, particularly for longer SPEI scales. This interaction implies that more advanced models are more effective at maintaining accurate predictions over extended forecast periods, especially for long-term drought indices (Figure 11). The extensive analysis of metrics across Steps, SPEI scales, and Models presents compelling evidence for the higher effectiveness of machine learning techniques, namely tree-based methods, in forecasting SPEI. Furthermore, it emphasizes the growing reliability of longer-term drought indices and the widespread difficulty of maintaining accurate predictions over long forecast periods. Through thorough comparisons, it becomes clear that LSTM routinely surpasses other models, particularly when it comes to making predictions for longer-term SPEI. The architecture of this model, specifically built for processing time series data, successfully captures temporal relationships, leading to reduced error metrics and improved R2 scores. Gradient Boosting exhibits strong performance, although it is marginally less precise than LSTM when making predictions over longer time intervals. XGBoost, although it performs well compared to other methods, often shows greater error metrics, suggesting that further optimization may be necessary. Recurrent Neural Network (RNN) models, while beneficial, typically exhibit elevated levels of errors, indicating that they may not be well-suited for this specific forecasting task. Overall, LSTM and Gradient Boosting models demonstrate notable efficacy, particularly for extended SPEI durations and prediction steps. These insights are essential for choosing the suitable model for drought forecasting applications, guaranteeing precise and dependable predictions over various time scales.