A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data
https://orcid.org/0000-0003-2803-7983
https://orcid.org/0000-0003-2035-1863
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.
This study explores advanced statistical data analysis approaches for missing data interpolation and anomaly detection in mining influenced water data, demonstrating how they can improve the accuracy and reliability of mine water quality data analysis.
Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data
https://orcid.org/0000-0003-2803-7983
https://orcid.org/0000-0003-2035-1863
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.
This study explores advanced statistical data analysis approaches for missing data interpolation and anomaly detection in mining influenced water data, demonstrating how they can improve the accuracy and reliability of mine water quality data analysis.
Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data
https://orcid.org/0000-0003-2803-7983
https://orcid.org/0000-0003-2035-1863
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.
This study explores advanced statistical data analysis approaches for missing data interpolation and anomaly detection in mining influenced water data, demonstrating how they can improve the accuracy and reliability of mine water quality data analysis.
Exploring Advanced Statistical Data Analysis Techniques for Interpolating Missing Observations and Detecting Anomalies in Mining Influenced Water Data
More, Kagiso S (author) / Wolkersdorfer, Christian (author)
ACS ES&T Water ; 4 ; 1036-1045
2024-03-08
Article (Journal)
Electronic Resource
English
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