A Multivariable Predictive Model Based on LSTM Networks for Estimating Greenhouse Gas Emissions in the Mining Sector

This study presents an empirically validated multivariable predictive framework for estimating greenhouse gas emissions (GHG) in the mining sector using Long Short-Term Memory (LSTM) neural networks. Unlike previous conceptual approaches, the revised version explicitly describes the dataset used: the original base covers the period 1988-2023 and, after temporal harmonization to monthly frequency, was structured into 432 observations and six analytical variables (five predictors and one target variable), collected from official sources such as MINAM, MINEM, IEA, OEFA, and ANA [1]. An integrated and reproducible experimental design was implemented that incorporates data preprocessing, temporal harmonization, feature engineering, and hyperparameter optimization. The model performance was evaluated on the same harmonized dataset and under the same temporal split as the statistical and machine learning reference models, including ARIMA, linear regression, XGBoost, and LightGBM. Results demonstrate, based on empirical evidence rather than speculative thresholds, that the LSTM model outperforms all reference models, achieving an RMSE of 0.462 and an R2 of 0.92 on the test set, evidencing its superior ability to capture nonlinear temporal dependencies inherent to mining systems. The proposed framework favors reproducibility and scalability, as it specifies critical hyperparameters such as number of layers, units per layer, time window, learning rate, batch size, and dropout, and is designed for integration into systems of industrial monitoring (SCADA) for real-time environmental decision-making.