OPTIMIZATION OF A PROBABILISTIC MODEL FOR PREDICTING GROUNDWATER FLUCTUATIONS IN VARIOUS FACTOR

This study focuses on the optimization of a probabilistic model for predicting groundwater fluctuations in response to various factors. Groundwater table fluctuations play a crucial role in hydrological and environmental systems, impacting water availability, ecosystem health, and human activities. Accurate prediction of groundwater table fluctuations is essential for effective water resource management and sustainable development. The probabilistic model integrates multiple factors that influence groundwater table fluctuations, including precipitation, evapotranspiration, land use, soil characteristics, and groundwater pumping. The model incorporates statistical techniques, such as regression analysis and probabilistic modelling, to capture the complex relationships between these factors and groundwater table dynamics. By considering the uncertainties associated with each factor, the model provides a probabilistic estimation of groundwater table fluctuations, enabling decision-makers to better understand and manage water resources. To optimize the model's performance, advanced machine learning algorithms, such as artificial neural networks and genetic algorithms, are employed. These algorithms enable the model to learn from historical data, identify patterns and trends, and improve the accuracy of predictions. Through an iterative optimization process, the model is fine-tuned to achieve the best possible fit between observed and predicted groundwater table fluctuations. The study utilizes historical groundwater data from multiple monitoring wells in various geographic locations. The dataset is carefully selected and processed to ensure its quality and representativeness. The model is trained and validated using this dataset, and its performance is assessed using appropriate evaluation metrics, such as root mean square error (RMSE) and coefficient of determination (R-squared). The results demonstrate the effectiveness of the optimized probabilistic model in predicting groundwater table fluctuations. The model accurately captures the temporal and spatial variability of groundwater levels, considering the interplay between different factors. The probabilistic estimation provides valuable insights into the uncertainties associated with the predictions, enabling stakeholders to make informed decisions and adapt their water management strategies accordingly. The findings of this study have important implications for water resource management and environmental planning. The optimized probabilistic model offers a robust tool for predicting groundwater table fluctuations under various scenarios, including climate change, land use change, and groundwater extraction. The model's probabilistic nature enhances decision-making by quantifying the uncertainties and risks associated with groundwater dynamic