Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data

Abstract

Collecting mining influenced water (MIW) quality data can result in incomplete data sets with missing values and anomalies, making it challenging to use the data for optimizing mine water management. This work explores advanced statistical data analysis approaches for addressing missing data interpolation and anomaly detection in MIW data sets. The study compares the performance of five different interpolation techniques and four different anomaly detection techniques using supervised and unsupervised machine learning algorithms developed using Python 3.8.16. The results of the study demonstrate that the radial basis function, spline, and k-nearest-neighbors interpolation techniques, along with the predictive confidence interval level anomaly approach based on gradient boosting regression trees, perform best for missing data interpolation and anomaly detection, respectively. Thorough application of these advanced techniques can improve the accuracy and reliability of mine water quality data, which is crucial for making conclusions on the safety of the environment, public health, and effective MIW management. This paper highlights the importance of developing effective methods for addressing missing data and anomalies in MIW data sets, which can ultimately lead to improved treatment plant optimization.

Applications of Artificial Intelligence, Machine Learning, and Data Analytics in Water Environments

“…Other contributions introduce novel methodologies that combine ML with statistical approaches to address various environmental problems, such as sewer overflow pollution abatement, fault detection in water and wastewater treatment, , an assay for source apportionment of per- and polyfluorinated substances (PFAS), detection of freshwater algae, and influent water data . Beyond water quality, ML has been applied to model water quantity, exploring dominant factors influencing urban industrial wastewater discharges, model energy consumption of wastewater treatment, identify endocrine-active pollutants in the organic Unregulated Contaminant Monitoring Rule (UCMR 1–4) and their toxic potentials, and employ quantitative biodescriptors to predict in vivo toxicity …”