ABSTRACT
Efficient water drilling is crucial for sustainable water resource management, especially in regions reliant on groundwater. The existing system considers only the following three parameters soil layer classification, water table depth and duration but the proposed work includes water quality and erosion rate to predict the best drilling process.This work has proposed a machine learning framework to enhance drilling efficiency by predicting soil classifications, water table depths, duration, water quality and erosion rate. The experimental studies compare three algorithms Random Forest, Gradient Boosting and Extreme Gradient Boosting to determine the most accurate model for drilling. The results obtained from above three algorithms demonstrate that Random Forest outperforms the other algorithms in terms of accuracy. The first aspect of our approach involves soil classification. By leveraging above machine learning techniques on soil data, it categorizes different soil types to facilitate targeted drilling. Random Forest exhibited superior performance in classifying soil types accurately, enabling precise identification of areas suitable for groundwater extraction. Furthermore, machine learning is used to predict water table depths, a critical factor in determining drilling success. Random Forest again emerged as the most effective algorithm, providing accurate predictions of water table depths, water quality and erosion rate across different geographical locations. This capability is invaluable for optimizing drilling operations and minimizing resource wastage.
