# Application of Artificial Intelligence to Forecast Drought Index for the Mekong Delta
###### Abstract
Additionally, LSTM models have been used to simulate vegetation dynamics and predict vegetation activities and stresses based on meteorological data, showcasing their versatility in various environmental forecasting applications [32]. The Standardized Precipitation Evapotranspiration Index (SPEI) is a popular index for evaluating drought conditions. It has been used in various studies to analyze drought patterns and severity [18; 33; 34; 35]. The SPEI combines meteorological and hydrological variables, such as precipitation, evapotranspiration, and groundwater levels, to provide a comprehensive assessment of drought [36]. It has been found to accurately characterize severe drought events in different climatic regions. Additionally, the SPEI has been used to monitor drought conditions during critical phenological phases of crops, such as maize cultivation, and to assess the temporal and spatial variability of droughts. The SPEI is a drought index used to assess water balance and drought conditions. It calculates a standardized value based on a continuous probability distribution fitted to a water balance time series. Different probability distributions, such as generalized logistic (GLO), generalized extreme value (GEV), Pearson Type III (PE3), and normal (NOR) distributions, have been considered for SPEI analysis in various regions. Studies have recommended using PE3 or GEV distributions for SPEI analysis in Canada [37], whereas a new multiscale SPEI dataset has been provided for reference and future time horizons in Italy [38]. Regional drought analysis using SPEI has been performed in the Gediz Basin, Turkey, with different distributions found to be the best fit for different reference periods [35]. In China, the SPEI has been used to accurately monitor drought events, with spatiotemporal distribution and trends analyzed in various climatic sub-regions [39]. In Malaysia, the SPEI has been used to determine drought indices for the Pahang River Basin with the aim of mitigating the impact on water supply and economic development [40]. SPEI has several advantages: It is useful for assessing both drought and wetter-than-normal conditions and provides a comprehensive understanding of moisture variability [38]. SPEI is a reliable tool for drought prediction because it is simpler, faster, and requires fewer data points than dynamic models [30]. It can accurately determine the spatial and temporal dimensions of drought events, making it valuable for drought monitoring and risk assessments. The SPEI is particularly effective in predicting droughts, with higher overall accuracy and fewer mistakes compared to other indices, such as the Standardized Precipitation Index (SPI). Additionally, the SPEI can be used to estimate the impact of drought events on water availability, agriculture, and ecosystems, aiding in the mitigation of economic losses and damage to the quality of life [41]. The versatility of the SPEI allows for the development of ensemble PDFs, making it suitable for assessing drought projections throughout the 21 st century. SPEI is a reliable drought index that can be used for accurate drought assessment and forecasting. Artificial Intelligence plays a transformational role in drought forecasting, providing novel solutions to the constraints of traditional methodologies. Drought forecasting becomes increasingly accurate, flexible, and responsive as AI is used, ultimately aiding effective water resource management and increasing resilience in drought-prone areas.