The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization

Abbaszadeh, Maliheh; Ehteram, Mohammad; Ahmed, Ali Najah; Singh, Vijay P.; El‐Shafie, Ahmed

doi:10.1007/s12145-021-00667-6

Cited by 9 publications

(4 citation statements)

References 47 publications

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“…Abbaszadeh et al [156] proposed a novel method for estimating copper grade in porphyry copper deposits (PCDs) using adaptive neuro-fuzzy inference system (ANFIS) and multi-layer perceptron (MLP) models. The study incorporated optimization techniques such as HGSO, WA, and MFO to fine-tune the parameters of the models.…”

Section: B Prediction and Classificationmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

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Section: B Prediction and Classificationmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

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“…Standalone and hybrid ANN models are considered as individual models. The outputs of multiple individual models can be embedded into the structure of an ensemble model, and then this ensemble model can be used to prepare the final outputs (Abbaszadeh et al 2021). Indeed, the IMM model receives outputs from multiple individual models as inputs.…”

Section: Inclusive Multiple Modelsmentioning

confidence: 99%

“…Indeed, the IMM model receives outputs from multiple individual models as inputs. Then, the IMM model predicts target variables based on received inputs (Abbaszadeh et al 2021). In this study, the IMM model is used to assemble climate models and ANN models.…”

Section: Inclusive Multiple Modelsmentioning

confidence: 99%

Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks

Ehteram

Kalantari

Ferreira

et al. 2022

Journal of Water and Climate Change

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Groundwater (GW) plays a key role in water supply in basins. As global warming and climate change affect groundwater level (GWL), it is important to predict it for planning and managing water resources. This study investigates the GWL of the Yazd-Ardakan Plain basin in Iran for the base period of 1979–2005 and predicts for periods of 2020–2059 and 2060–2099. Lagged temperature and rainfall are used as inputs to hybrid and standalone artificial neural network (ANN) models. In this study, the rat swarm algorithm (RSA), particle swarm optimisation (PSO), salp swarm algorithm (SSA), and genetic algorithm (GA) are used to adjust ANN models. The outcomes of these models are then entered into an inclusive multiple model (IMM) as an ensemble model. In this study, the output of climate models is also inserted into the IMM model to improve the estimation accuracy of temperature, rainfall, and GWL. The monthly average temperature for the base period is 12.9 °C, while average temperatures for 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 14.5 and 15.1 °C, and for 2060–2099 they are 16.41 and 18.5 °C under the same scenarios, respectively. In future periods, rainfall is low in comparison with the base period. Lagged rainfall and temperature of the base period are inserted into ANN-RSA, ANN-SSA, ANN-PSO, ANN-GA, and ANN models to predict GWL for the base period. Outputs of IMM, ANN, and the five hybrid models (ANN-RSA, ANN-SSA, ANN-PSO, and ANN-GA) indicate that root mean square errors (RMSE) are 2.12, 3.2, 4.58, 6.12, 6.98, and 7.89 m, respectively, in the testing level. It is found that GWL depletion in 2020–2059 under RCP 4.5 and RCP 8.5 scenarios are 0.60–0.88 m and 0.80–1.16 m, and in 2060–2099 under the same scenarios they are 1.49–1.97 m and 1.75–1.98 m, respectively. The results highlight the need to prevent overexploitation of GW in the Ardakan-Yazd Plain to avoid water shortages in the future.

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“…Nowadays, uncertainty analysis in various studies in the field of earth science has made progresses, such as in hydrology, three-dimensional geological structure model, meteorological problem, climate change problem, soil research, hydrology and structural analysis, mineral deposit grade prediction and seismology. (Yifru et al 2023;Liang et al 2021;Boller et al 2010;Anderson et al 2011;Barrena-González et al 2023;Abbaszadeh et al 2021;Afsari et al 2022. ) At present, most mineralization prospectivity work is based on providing the location, abundance and reserves of predicted minerals.…”

Section: Introductionmentioning

confidence: 99%

Application of the improved Probabilistic Fuzzy Logic Inference Engine Model to evaluate Mineralization Prospectivity - Taking the Gejiu region of Yunnan, China as an example

Jie,

Yongqing,

Pengda

et al. 2023

Preprint

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A probabilistic fuzzy logic inference engine simulated by the Monte Carlo method is used to determine the mineralization prospectivity of Sn deposits in the Gejiu region, and the uncertainty of the mineralization prospectivity is evaluated. The elemental analysis of the fuzzy logic inference engine is improved, and the Hilbert-Huang transformation (HHT) multi-scale model is integrated. The copula function is proposed to solve the problem of correlation between elements. The probabilistic fuzzy logic inference engine simulated by the Monte Carlo method can provide more information than the traditional method. Its most significant advantage is that it can describe the potential and uncertainty of data and models, which are caused by random error and fuzziness. This information can be used in the subsequent risk assessment of the exploration targets. It can also determine the largest source of uncertainty in the final mineralization prospectivity map, thereby reducing the uncertainty.

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The copper grade estimation of porphyry deposits using machine learning algorithms and Henry gas solubility optimization

Cited by 9 publications

References 47 publications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Prediction of future groundwater levels under representative concentration pathway scenarios using an inclusive multiple model coupled with artificial neural networks

Application of the improved Probabilistic Fuzzy Logic Inference Engine Model to evaluate Mineralization Prospectivity - Taking the Gejiu region of Yunnan, China as an example

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