Detection of Non-functional Bore wells Using Machine Learning Algorithms

Abstract India is the largest user of ground water with approximately 27.5 million bore wells drawing over 230 cubic kilolitre per year. Bore wells are vertical drilled wells, bored into an underground water-bearing layer in the earth’s surface, to extract water for various purposes. Less rainfall, water scarcity, depletion of underground water has spearheaded the number of bore wells dug in a year. Excessive number of bore wells has led to squandering of groundwater at higher rates than the rate of water replenished and caused depletion of the groundwater levels. When the water gets dried, the motor is removed and the outer surface is not properly covered or sealed. The diameter is large enough for the child to fall inside. The inside of the bore well now non-functioning or left unused might have collapsed. According to a report, since 2009 more than 40 children fell into bore well, sadly seventy percentage of the rescue operations fail. The current paper incorporates the analysis of datasets of bore well acquired from Kaggle. A predictive model has been built by employing machine learning algorithms like Random forest, Extra-trees classifier, logistic regression ‘to predict the non-functional bore wells present which is to be reported and to be taken action in the immediate future’. The performance of the models have been evaluated by performance metrics, among which Random forest shows excellent performance in predicting non-functional killer bore wells.