# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
## 4 Discussion
The exceptional efficacy of LSTM and XGBoost models in all timeframes highlights their resilience in capturing the intricate interaction of climatic factors that affect drought conditions. The improved predictive capacity is of great importance for the Mekong Delta region. Accurate forecasts of drought can provide valuable information for appropriate actions, leading to a reduction in agricultural losses and the mitigation of negative social effects. Moreover, these models can be incorporated into current climate monitoring systems to offer uninterrupted, instantaneous predictions of drought. This study has produced encouraging outcomes; however, it is imperative to acknowledge the existence of several constraints. The use of historical climate data suggests that the models might not fully encompass unprecedented climatic variations stemming from global warming. Future investigations ought to explore the inclusion of real-time data and the integration of supplementary environmental factors such as soil moisture and land-use \begin{table}
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**Related Work** & **Methods** & \
\begin{tabular}{c} **Data** \\ **Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Prediction** \\ **Steps** \\ \end{tabular} & **Metrics** & **Results** \\ \hline Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms [69] & RF, XGBoost, CNN and LSTM & monthly & 3 and 6-month & NSE, MSE, MAE, MBE and R\({}^{2}\) and R\({}^{2}\) & The spatial extent of drought increased significantly. Mild drought showed a non-significant increase. The XGB and RF versions are considered to be highly effective. \\ \hline Quantitative Assessment of Drought Impacts Using XGBoost based on the Drought Impact Reporter [70] & XGBoost, SHAP & monthly & 1, 3, 6, 9 and 12-month & F2, Recall and Accuracy & F2, Recall and Accuracy & The Shapley additive explanation technique revealed the rules guiding the prediction. \\ \hline Drought Forecasting in Alibori Department in Benin using the Standardized Precipitation Prediction Index and Machine Learning Approaches [71] & RF and XGBoost monthly & monthly & 1, 3, 6, 9 and 12-month & RMSE, MSE, MAE and R\({}^{2}\) & XGBoost showed better performance in drought prediction models. XGBoOST had coefficients of determination 0.89, 0.83, 0.99. \\ \hline Big data based architecture for drought forecasting using LSTM, ARIMA, and Prophet: Case study of the Jiangsu Province, China [72] & ARIMA, PROPHET, LSTM & monthly & 1, 3, 6, 9 and 12-month & RMSE, MAE and R\({}^{2}\) & LSTM outperformed other models in drought forecasting \\ \hline Drought prediction based on SPI and SPEI with varying timescales using LSTM recurrent neural network [73] & ARIMA, Holt-Winters and LSTM & monthly & 1, 6 and 12-month & RMSE, MAE and R\({}^{2}\) & LSTM outperforms ARIMA in long-term drought prediction accuracy. \\ \hline \hline \end{tabular}
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Table 5: _Cont._changes to enhance the accuracy of the model. Moreover, broadening the geographical scope beyond the Mekong Delta region could validate the applicability of the models to other areas facing similar climate-related challenges. Subsequent research should scrutinize the integration of more advanced feature selection techniques to further enhance the model's performance. Additionally, it would be valuable to investigate hybrid models that exploit the benefits of both machine learning and deep learning methodologies to boost the precision of SPEI forecasts. The implementation of these models in various geographic regions and the assessment of their adaptability to diverse environmental conditions could yield more profound insights into their robustness and importance.