Application of machine learning algorithms for nonlinear system forecasting through analytics — A case study with mining influenced water data

More, Kagiso Samuel; Wolkersdorfer, Christian

doi:10.1016/j.wri.2023.100209

Cited by 8 publications

(8 citation statements)

References 34 publications

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“…Predicting and forecasting analysis using ANN or Machine learning (ML) in general, has been used in various domain, including nonlinear timeseries and have gained overwhelming attention over the past years [4]. Forecasting mining influenced water data using various ML technique including tree based method and ANN show positive result, with close to accurate prediction [5]. Beside ML method, traditional model such as auto regressive integrated moving average (ARIMA), Box-Jenkins, etc.…”

Section: Introductionmentioning

confidence: 99%

The application of artificial neural network model to predicting the acid mine drainage from long-term lab scale kinetic test

Abfertiawan,

Kautsar,

Hasan

et al. 2024

E3S Web of Conf.

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Acid mine drainage (AMD) is one of the common environmental problems in the coal mining industry that was formed by the oxidation of sulfide minerals in the overburden or waste rock. The prediction of acid generation through AMD is important to do in overburden management and planning the post-mining land use. One of the methods used to predict AMD is a lab-scale kinetic test to determine the rate of acid formation over time using representative samples in the field. However, this test requires a long-time procedure and large amount of chemical reagents lead to inefficient cost. On the other hand, there is potential for machine learning to learn the pattern behind the lab-scale kinetic test data. This study describes an approach to use artificial neural network (ANN) modeling to predict the result from lab-scale kinetic tests. Various ANN model is used based on 83 weeks experiments of lab-scale kinetic tests with 100% potential acid-forming rock. The model approaches the monitoring of pH, ORP, conductivity, TDS, sulfate, and heavy metals (Fe and Mn). The overall Nash-Sutcliffe Efficiency (NSE) obtained in this study was 0.99 on training and validation data, indicating a strong correlation and accurate prediction compared to the actual lab-scale kinetic tests data. This show the ANN ability to learn patterns, trends, and seasonality from past data for accurate forecasting, thereby highlighting its significant contribution to solving AMD problems. This research is also expected to establish the foundation for a new approach to predict AMD, with time efficient, accurate, and cost-effectiveness in future applications.

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Section: Introductionmentioning

confidence: 99%

The application of artificial neural network model to predicting the acid mine drainage from long-term lab scale kinetic test

Abfertiawan,

Kautsar,

Hasan

et al. 2024

E3S Web of Conf.

View full text Add to dashboard Cite

show abstract

“… a Definitions: n , number of measurements; x̅ , average; σ, standard deviation; min, minimum value; max, maximum value. pH average calculated as −log 10 [∑ C i / n ], where C is the proton activity (); measured values and units as reported by the plant (from More and Wolkersdorfer). …”

Section: Introductionmentioning

confidence: 99%

“…As a result, these insights can be used to improve mine water management, predict mine water chemistry, and provide effective, sustainable water management. 10 Consequently, this highlights the potential of advanced statistical data analysis methods in addressing the environmental challenges associated with mine water treatment and management.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to advanced statistical data analysis techniques, these methods are therefore less effective in the treatment and management of MIW due to their limited ability to capture and process complex patterns and relationships in the data. , This manuscript presents techniques that use all the data in a given data set to uncover insights and patterns in the specific parameter of interest. As a result, these insights can be used to improve mine water management, predict mine water chemistry, and provide effective, sustainable water management . Consequently, this highlights the potential of advanced statistical data analysis methods in addressing the environmental challenges associated with mine water treatment and management.…”

Section: Introductionmentioning

confidence: 99%

“…Exploratory data analysis is conducted on the data before they can be used on the selected algorithms to detect anomalies and interpolate the missing observations. This method is conducted to understand the behavior of the data and as a guidance to apply appropriate techniques such as the probabilistic methods (e.g., ref ) or numerical statistical modeling (e.g., ref ). This paper explores several advanced numerical statistical methods for interpolating missing data, such as k -nearest-neighbors (kNN) interpolation, radial basis function (RBF) interpolation, spline interpolation, multivariate adaptive regression splines (MARS), and artificial neural network (ANN) interpolation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Wolkersdorfer

2023

ACS EST Water

Self Cite

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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.

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Modern Real-World Applications Using Data Analytics and Machine Learning

Vijayakumar Ponnusamy,

Nallarasan V.,

Rajasegar R. S.

et al. 2024

Studies in Big Data

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Application of machine learning algorithms for nonlinear system forecasting through analytics — A case study with mining influenced water data

Cited by 8 publications

References 34 publications

The application of artificial neural network model to predicting the acid mine drainage from long-term lab scale kinetic test

The application of artificial neural network model to predicting the acid mine drainage from long-term lab scale kinetic test

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

Modern Real-World Applications Using Data Analytics and Machine Learning

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