# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 4 Discussion
This underscores the significance of judiciously selecting input parameters that effectively encapsulate the fundamental climatic factors influencing drought occurrences. The occurrence of droughs in the Mekong Delta is affected by a range of climatic factors, including fluctuations in precipitation, shifts in temperature, and the influence of large-scale atmospheric patterns like the Southern Oscillation Index (SOI). Prior research has emphasized the intricate characteristics of drought occurrences in the area, demonstrating substantial differences in space and time [63]. Our findings are consistent with these observations since the machine learning models accurately captured the complex dynamics of these climate variables. Moreover, the findings of this study align with prior research findings about severe droughts in 2010 and 2019 (where \(-2\leq\) SPEI \(<-1.5\)), as well as extreme droughts in 1987-1988, 1998, and 2015-2016 (where SPEI \(<-2\)) in the Mekong Delta in Vietnam [46; 64; 65]. The conventional approaches to drought prediction in the area have mainly depended on statistical models, which frequently fail to effectively forecast droughts due to their limited capacity to handle nonlinear associations among climatic variables. On the other hand, machine learning models, like XGBoost, have shown a greater ability to represent these complex interactions, resulting in more precise predictions. These results are consistent with findings from comparable studies conducted in other areas of the world, where the use of advanced modeling approaches has demonstrated enhanced accuracy in predicting droughts [19; 66; 67; 68; 69; 70; 71]. The table below provides a summary of the comparison between the results of this investigation and previous studies (Table 5). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Related Work** & **Methods** & \begin{tabular}{c} **Data** \\ **Frequency** \\ \end{tabular} & \begin{tabular}{c} **Prediction** \\ **Steps** \\ \end{tabular} & **Metrics** & **Results** \\ \hline Application of & & & & & & \\ Informer Model Based & ARIMA, LSTM, and Informer & monthly & \begin{tabular}{c} 1, 3, 6, 9, 12 \\ and NSE \\ \end{tabular} & \begin{tabular}{c} MAE, RMSE \\ and NSE \\ \end{tabular} & \begin{tabular}{c} NSE = 0.968–0.986 \\ Informer model enhanced \\ precision of SPEI prediction on \\ different timescales \\ \end{tabular} \\ \hline Application of a hybrid ARIMA-LSTM model based on the SPEI for drought forecasting [28] & ARIMA, SVR, LSTM, LSTM, and ARIMA-SVR, LS-SVR, and ARIMA-LSTM & monthly & \
\begin{tabular}{c} 1, 2, 6, 12 and \\ 24-month \\ \end{tabular} & NSE &
\begin{tabular}{c} The ARIMA-LSTM model has \\ the highest prediction accuracy \\ at the 6-, 12-, and \\ 24-month scales \\ \end{tabular} \\ \hline Prediction of the standardized & & & & & \\ precipitation index based on the Long & & & & \\ Short-Term Memory and empirical mode decomposition- extreme learning machine models: The Case of Sakarya, & & & & \\ Türkiye [29] & & & & \\ \hline A transparency fusion-based & XGBoost, RF, & & & & \\ methodology for & LightGBM & monthly & 1 and 12-month & R\({}^{2}\) & Extreme precipitation, soil moisture, runoff, and precedent SPEI explain over 80\% of the prediction variance. \\ \hline Drought Monitoring and Performance Evaluation Based on Machine Learning System of Multi-Source Remote Sensing & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Table comparing the results of the study to the existing literature. This table provides a comprehensive comparison between the current study and the existing literature on drought prediction models. The results indicate that the LSTM and XGBoost models are consistently effective across various studies and regions, showcasing their robustness in predicting drought indices such as SPEI. Our study aligns with this trend, demonstrating high accuracy in forecasting drought conditions in the Mekong Delta with R\({}^{2}\) values ranging from 0.90 to 0.96.