Rain Forecasting for Production Capacity Planning at Open Pit Mining
Authors: 
Komarudin, DavidAuthors Info & Claims
Komarudin, DavidAuthors Info & ClaimsPages 349 - 353
Abstract
Extreme weather events have been one of the biggest challenges in the mining sector. One alternative to mitigate these risks is to improve the prediction of extreme weather events occur. One of the common extreme weather events faced by the mining sector is extreme rainfall. Continuous extreme rainfall can give rise to flooding events, which might disrupt the supply chain and operation of the mining sector. Previously, extreme rainfall prediction is conducted by employing the traditional statistical methods such as linear regression or autoregressive integrated moving average (ARIMA). Those methods result in good accuracy; however, they do not cover some of the assumptions of the data. Along with the development of information technology, advanced method, namely machine learning, is conducted. Thus, this study employed machine learning to predict the rainfall duration in open-pit mining. The predictive models constructed are a feed-forward neural network and an ARIMA model. This study also compared the performance of the neural network model and the ARIMA model by measuring its root mean square (RMSE). Based on the result, the neural network model outperforms the ARIMA model.
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- Rain Forecasting for Production Capacity Planning at Open Pit Mining
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Published In
September 2019
398 pages
ISBN:9781450376532
DOI:10.1145/3364335
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In-Cooperation
- The Hong Kong Polytechnic: The Hong Kong Polytechnic University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 27 September 2019
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- Research-article
- Research
- Refereed limited
Funding Sources
- Ministry of Research, Technology and Higher Education of the Republic of Indonesia
Conference
ICIBE 2019
ICIBE 2019: 2019 The 5th International Conference on Industrial and Business Engineering
September 27 - 29, 2019
Hong Kong, Hong Kong
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