# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 4 Discussion  
This study evaluates the performance of various models (ARIMA, Gradient Boosting, XGBoost, RNN, and LSTM) across different prediction steps and SPEI durations using multiple error metrics: MAE, MSE, RMSE, R2 Score, and MAPE. Each metric provides unique insights into model accuracy and robustness, highlighting strengths and weaknesses in different contexts. Figure 11: Model performance comparison chart. The performance of the models varies for each step, with the MAE and RMSE often being larger in the beginning steps and improving as the prediction horizons increase. The observed pattern indicates that models may demonstrate improved adaptation to data patterns as the forecast timeframe increases. For example, LSTM regularly demonstrates low MAE and RMSE values at each step, suggesting its effectiveness in capturing temporal relationships. Gradient Boosting exhibits strong performance; however, it is slightly inferior to LSTM, especially during the first steps. Observations revealed a distinct pattern of enhanced model performance as the SPEI scale increased, encompassing all models and indicators. The enhancement was most noticeable while transitioning from SPEI1 and SPEI3 to SPEI6 and SPEI12. The observed trend indicates that it is easier to forecast long-term drought conditions compared to short-term variations. Multiple variables can potentially influence this phenomenon:  
* Long-term indices are less influenced by short-term weather fluctuations, rendering them more stable and predictable. * The process of collecting data over extended periods can reduce the impact of random variations and oscillations, enabling models to accurately identify and analyze underlying patterns. * Prolonged drought conditions may have a stronger correlation with large-scale climatic trends, which often change at a slower and more predictable rate. * These findings have significant ramifications for the prediction and control of droughs. They indicate that precisely anticipating short-term drought conditions is still difficult, but there is a higher level of confidence in predicting longer-term drought outlooks. Out of all the models, LSTM regularly performs better than the others, particularly when it comes to making predictions for longer time periods and SPEI durations. It demonstrates superior performance by achieving lower MAE, MSE, and RMSE values. Additionally, it exhibits higher R-squared (R2) scores, indicating its ability to accurately capture intricate patterns in time series data. Gradient Boosting exhibits impressive performance, especially in regard to its R2 score and MAPE, which demonstrates its resilience in generating precise predictions. XGBoost, although it is competitive, typically shows greater error metrics, indicating the necessity for more tuning. RNN models, while beneficial, generally exhibit elevated levels of errors, suggesting that they may not be as efficient for this particular task in comparison to LSTM and Gradient Boosting. The subpar performance of ARIMA underscores the constraints of linear statistical models in comprehending the intricacies of drought index dynamics. This study emphasizes the need to choose suitable models and indicators that align with the unique forecasting needs. LSTM and Gradient Boosting models exhibit exceptional efficacy in long-term SPEI forecasts, rendering them well-suited for use in drought forecasting and climate studies. These findings emphasize the importance of employing comprehensive assessment metrics to measure many facets of model performance, thus guaranteeing strong and dependable predictions. This holds paramount importance in the realm of establishing timely cautionary measures and alleviating the repercussions of drought situations. The discoveries further indicate that the integration of supplementary meteorological and oceanographic indicators, such as the SOI Anomaly and SST NINO4, has the potential to bolster the prognostic capacities of the models, particularly for extended-term forecasts (SPEI-12).