Machine Learning Methods for Forecasting Intermittent Tin Ore Production

Machine Learning Methods for Forecasting Intermittent Tin Ore Production

orecasting;classification, machine learning;mining;CatBoost

Effective production forecasting is important for resource planning and management in the mining industry.Tin ore production from Cutter Section Dredges (CSD) may fluctuatedue 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 forecastingto predictfuturetin ore production values, which havenot 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

Nabila Dhia Alifa Rahmah1*, BudhiHandoko2,AnindyaApriliyanti Pravitasari

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5990/974

Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Bandung, Indonesia

15-10-2024

FAJAR BAGUS W

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