Applied Intelligence and Computing
Innovating Water Purity Analysis with Gradient Boosting Classification Techniques
Authors: Jatin Sharma, Kanwarpartap Singh Gill, Mukesh Kumar and Ruchira Rawat
Publishing Date: 10-11-2024
ISBN: 978-81-955020-9-7
Abstract
Ensuring water quality is crucial for protecting both human health and aquatic ecosystems. In this research, we delve into the effectiveness of gradient boosting classifiers for predicting water quality parameters. Our study evaluates the potential of this approach in assessing groundwater quality, comparing it against three other gradient boosting-based algorithms, and contrasts its performance with traditional machine learning methods. We also analyze feature importance to identify the key factors influencing water quality. The findings reveal an accuracy rate of approximately 78%. Moreover, the identified significant features provide valuable insights into the factors driving water quality changes. This research advances predictive modeling techniques in water quality assessment, aiding in proactive management strategies for sustainable water resource use and ecosystem conservation. These models hold significant implications for informed decision-making in agricultural water management and resource allocation within the region. As the population grows and the demand for resources increases, managing our lives becomes more challenging. In this struggle, we sometimes resort to using poor or contaminated water sources, endangering our health. According to a recent World Health Organization (WHO) survey, over 2.2 billion people in India suffer from issues related to unsafe drinking water, and 21% of diseases are linked to impure water.
Keywords
Machine Learning, Water Quality, Gradient Boosting Classifier, Classifier Evaluation, Artificial Intelligence, Predictive Modelling Techniques, Diseases, Population, Resources, Resources.
Cite as
Jatin Sharma, Kanwarpartap Singh Gill, Mukesh Kumar and Ruchira Rawat, "Innovating Water Purity Analysis with Gradient Boosting Classification Techniques", In: Mukesh Saraswat and Rajani Kumari (eds), Applied Intelligence and Computing, SCRS, India, 2024, pp. 125-130. https://doi.org/10.56155/978-81-955020-9-7-14