Machine Learning Methods for Forecasting Intermittent Tin Ore Production

Machine Learning Methods for Forecasting Intermittent Tin Ore Production

Effective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is zero. This study compares various combinations...

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Bibliographic Details
Main Authors: Nabila Dhia Alifa Rahmah, Budhi Handoko, Anindya Apriliyanti Pravitasari
Format: Article
Language:English
Published: Ikatan Ahli Informatika Indonesia 2024-10-01
Series:Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Subjects:
forecasting
classification
machine learning
mining
catboost
Online Access:https://jurnal.iaii.or.id/index.php/RESTI/article/view/5990
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Summary:Effective production forecasting is important for resource planning and management in the mining industry. Tin ore production from Cutter Section Dredges (CSD) may fluctuate due to a variety of factors, in which there are periods when the production is zero. This study compares various combinations of machine learning-based classification and forecasting to predict future tin ore production values, which have not been found in previous studies. The presence of zero values in the forecast in the next day's tin ore production forecast is addressed by combining classification and forecasting techniques. Random Forest and CatBoost classification techniques are used to determine the next day's CSD production operating status. Then, for each time point when the CSD is operational, a forecasting model is created using CatBoost and Bi-LSTM. This study's findings show that a serial combination of the Random Forest classification method and CatBoost forecasting can produce accurate tin ore production forecasts for the selected CSD (RMSE = 0.271, MAE = 0.179, MAE = 0.730, F1-score = 0,80). This study demonstrates how a serial combination of classification and forecasting models can improve the accuracy and efficiency of production forecasting for intermittent time series data.
ISSN:2580-0760

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