# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
###### Abstract
A study by [6] analyzed the spatiotemporal variability of meteorological droughs in the Mekong Delta area of Vietnam using the Standardized Precipitation Index (SPI) and found that the frequency of drought scales decreased while their spatial distribution tended to increase, with the main scales including moderate and severe droughs. The most extreme drought during the study period occurred in 1990-1992, with 11 out of 13 provinces experiencing extreme drought with a peak SPI value of \(-2.63\) and a duration of 29 months. The study concluded that climate change was the major factor affecting drought in the study area rather than the El Nino phenomenon. The Mekong Delta has a long history of drought, with the 2015-2016 event being particularly severe, and The Mekong Delta suffered the worst historic drought and salinity intrusion occurrence on record [7]. This region has also experienced a shift in the spatial distribution of meteorological droughs, with a decrease in frequency and an increase in severity [8]. The impact of these droughs on agriculture, particularly on rice production, is significant [9]. [PERSON] et al. evaluated meteorological droughs using the Standardized Precipitation Index (SPI) based on data from the Tropical Rainfall Measuring Mission (TRMM) [10]. [PERSON] and [PERSON] analyzed the correlation between sea surface temperature anomalies (SSTA) and meteorological droughs in the Vietnam Mekong Delta [11]. These studies demonstrate the use of various methods and data sources for drought forecasting in the Mekong Delta. [12] investigated the spatiotemporal trends, intensity, duration, and frequency of meteorological droughs in the Vietnamese Mekong Delta (VMD) using the Standardized Precipitation Evaporation Index (SPEI) at multiple timescales (3, 6, and 12 months). The findings suggest that the intensity, duration, and frequency of drought events increased from 1985 to 2018, with extreme drought events from October 2013 to September 2016 being the most severe and prolonged during the study period. Various research has demonstrated good results when applying the ARIMA model to anticipate the drought index, notably the Standardized Precipitation Evapotranspiration Index (SPEI). The ARIMA model has proven to be effective in analyzing and predicting drought conditions when applied to SPEI time-series data at various temporal scales, including 1-month, 3-month, and 6-month intervals. As a result, it improved the understanding of climatic patterns and assisted in decision-making and resource management. ARIMA models, employing historical SPEI data, demonstrated higher accuracy in the Amman-Zaraa Basin compared to TBATS models. Performance measures indicated strong predicting capabilities [13]. In addition, the hybrid wavelet-ARIMA (W-ARIMA) model has been suggested as a means to enhance drought forecasting. This model combines wavelet transform with ARIMA and has demonstrated superior performance compared to the conventional ARIMA model, as evidenced by statistical metrics such as Root Mean Square Error, Mean Absolute Error, and the Mean Absolute Percentage Error [14]. The experiments demonstrate the strength and flexibility of ARIMA and its hybrid models in predicting drought indices such as SPEI. This offers useful insights for strategic planning and managing water resources. Current approaches to drought forecasting in the Mekong Delta have limitations. The lack of observation stations reduces the reliability of the monitoring results, making it difficult to accurately identify droughts [10]. Additionally, current weather and climate conditions have negatively affected the accuracy and reliability of traditional prediction indicators used by small-scale farmers in the region [15]. These indicators, which are based on traditional environmental cues, may not be as effective in predicting drought events under the current conditions of climate uncertainty and variability [16].