Shahrood University of Technology Journal of Mining and Environment 2251-8592 15 2 2024 04 01 Ground Water Quality Analysis using Machine Learning Techniques: a Critical Appraisal 419 426 2970 10.22044/jme.2023.13452.2484 EN Naman Chandel Environmental Engineering, Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India Sushindra Kumar Gupta Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India 0000-0003-0396-5580 Anand Kumar Ravi Health, Safety and Environmental Engineering, Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India Journal Article 2023 08 22 Groundwater is an essential resource for human survival, but its quality is often degraded by the human activities such as improper disposal of waste. Leachate generated from landfill sites can contaminate groundwater, causing severe environmental and health problems. Machine learning techniques can be used to predict groundwater quality and leachate characteristics to manage this issue efficiently. This study proposes a machine learning-based model for the prediction of groundwater quality and leachate characteristics using the effective water quality index (EWQI). The leachate dataset used in this study was obtained from a landfill site, and the groundwater quality dataset was collected from literature review. The mean values of TDS, Ca, Mg, NO3^-, and PO₄^- exceeded the prescribed limit for drinking water purposes. The proposed model utilizes a machine learning architecture based on a convolutional neural network (CNN) to extract relevant features from the input data. The extracted features are then fed into a fully connected network to estimate the EWQI of the input samples. The model, trained and tested on leachate and groundwater quality datasets, achieves a high accuracy and computational efficiency, aiding in predicting groundwater quality and leachate characteristics for waste management. https://jme.shahroodut.ac.ir/article_2970_583f327e413e85eec7c6143b22ad3058.pdf