Groundwater Quality Prediction using Deep Learning

Groundwater is a vital natural resource that sustains ecosystems and provides drinking water to a significant portion of the global population. An effective understanding of groundwater quality changes and its prediction is necessary for water resource management. This work integrates important chemical characteristics like pH, TDS, and others into a revolutionary deep-learning algorithm to forecast groundwater quality. This guarantees that under no circumstances will the results of the water quality examination be affected. We suggest a hybrid neural model that examines the dataset's parameter correlations for comprehensive and long-term feature extraction. This considerably aids in comprehending the parameter values, which are then integrated into a spatial-temporal neural network. The spatial-temporal factors also have a significant impact on the analysis. This study makes use of spatial-temporal neural networks, a subset of deep learning, to examine the intricate temporal and geographical correlations present in the dataset. To build an efficient dataset for model training, historical past groundwater quality data and the chosen environmental factors are gathered for pre-processing. This study shows the possibility of precise groundwater quality prediction, providing vital information for the management of water resources. By predicting the regions, where the groundwater quality is likely to decline, this study may be used practically to prevent pollution and guarantee a steady supply of safe water. Additionally, the combination of environmental data with deep learning establishes a standard for data-driven decision-making in the hydrogeology and water resource management fields, promoting the sustainable management of this critical natural resource.