Forecasting copper price by application of robust artificial intelligence techniques - Khoshalan, Hasel Amini; Shakeri, Jamshid; Najmoddini, Iraj; Asadizadeh, Mostafa (Resources policy, 2021)
Metal price is one of the most important and effective parameters in assessing different projects such as industry and mining. In this regard, price variations can play a vital role in the correct decision-making of managers to develop or limit mining activities. Considering the increasing use of artificial intelligence (AI)-based networks in different fields such as price estimation, four methods were used in the present work for the first time to predict the price of important and extensively used copper-grade A cathode. These methods include Gene expression programming (GEP), Artificial neural network (ANN), Adaptive neuro-fuzzy inference system (ANFIS), and ANFIS-ACO (ant colony optimization algorithm). In this process, coal, aluminum, crude oil, gold, iron ore, natural gas, nickel, and lead were selected as the copper price parameters from 1990 to 2020. In this study, the ANN model with one hidden layer comprising 13 neurons, RMSE of 356.51, MAE of 239.105 ($/ton), MAPEof 5.70% ($/ton), and coefficient of determination (R2) of 98.1% for network test data was selected as the best model in predicting copper prices. In terms of their performance, ANFIS, ANFIS - ACO and GEP models were ranked next in the order of their appearance. Overall, an acceptable performance was found through all four AI methods in this study for predicting copper prices. •Artificial intelligence based networks are favorably used in different fields such as price estimation.•In this study four AI methods, Gene expression programming (GEP), artificial neural network (ANN), Adaptive neuro – fuzzy inference system (ANFIS), and ANFIS – ACO (Ant colony optimization algorithm), have been applied for forecasting the price of copper metal.•For this aim, the parameters of coal, aluminum, crude oil, gold, iron ore, natural gas, nickel, and lead are selected as the parameters affecting the price of copper while extracting various prices of these parameters along with the price of copper for 1990 to 2020.•Considering the determination of RMSE and coefficient of correlation for various models, the ANN model with one hidden layer comprising 13 neurons with RMSE of 356.51 ($/ton) and coefficient of determination (R2) of 98.1% for test data was selected as the best model in predicting copper prices.•Finally, the performance of the ANFIS-ACO, ANFIS and GEP models were in the next ranks, respectively.