Prediction of mining induced subsidence by sparrow search algorithm with extreme gradient boosting and TOPSIS method

Xu, Chun; Zhou, Keping; Xiong, Xin; Gao, Feng; Lu, Yan

doi:10.1007/s11440-023-01830-7

Cited by 7 publications

(6 citation statements)

References 56 publications

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“…Different numbers of particle swarms lead to different optimization results. It is generally recommended to select a swarm size between 20 and 120 [ 49 ]. In this study, swarm sizes of 20, 40, 60, 80, 100, and 120 were chosen to optimize the prediction of PPV using the RUN algorithm in combination with XGBoost.…”

Section: Resultsmentioning

confidence: 99%

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Research on prediction of PPV in open-pit mine used RUN-XGBoost model

Sun,

Yang,

Yang

et al. 2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

“…The important parameters in XGBoost regression analysis are shown in Table 1 . The three main parameters that influence the XGBoost prediction results are num_trees , max_depth , and eta [ 49 ]. Usually, these parameters are manually adjusted during computation to obtain higher goodness of fit results.…”

Section: Methodsmentioning

confidence: 99%

Research on prediction of PPV in open-pit mine used RUN-XGBoost model

Sun,

Yang,

Yang

et al. 2024

Heliyon

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“…In addition to, the foundation concept of a XGBoost algorithm is completing outputs of the weak learners sequentially for obtain better performance. Due to these advantages, its application in mining studies and earth sciences has become widespread in recent years (Sharma, 2018;Ding et al, 2020;Zhong et al, 2020;Qiu et al, 2021;Xu et al, 2023, Wang et al, 2022Hosseini et al, 2023). The XGBoost is to minimize the arranged expense Eq (1) given as below (Kavzoğlu and Teke, 2022).…”

Section: Multicollinearity Analysismentioning

confidence: 99%

Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms

Kahraman,

Taiwo,

Hosseini

et al. 2024

Preprint

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This study compares base, hybrid, and voting modeling techniques to predict blast toe volume size. The investigation integrates independent models, explores synergies in hybrid approaches, and optimizes accuracy through ensemble voting to offer comprehensive knowledge and more reliable forecasts for blast toe volume estimation in various design. 457 blasting was investigated and data was collected at Anguran lead and zinc mine in Iran. Nine model accuracy indices were used to compare the algorithm's prediction accuracy. The study indicates a significant relationship between toe volume size and explosive charge per delay, as demonstrated by multicollinearity, Spearman, and Kendall correlation analyses. The analysis of the model showed that Light Gradient Boosting Machine (LightGBM) achieved the highest accuracy compared to the other 8 conventional models, with correlation coefficients (R2) of 0.9004 and 0.8625 for the training and testing datasets, respectively. The Hybrid 6 model, which combines LightGBM and CART algorithms, achieved the highest R2 scores of 0.9473 in the training phase and 0.9467 in the testing phase. The Voting 8 model, consisting of LightGBM, GBM, DT, ET, RF, CatBoost, CART, AdaBoost, and XGBoost, had the greatest R2 scores of 0.9876 and 0.97265 in both the training and testing stages. The voting models can reliably forecast toe volume resulting from a blast design pattern, thereby providing a novel tool for simulation.

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“…Then in 2023, Xu et al devised a novel intelligent approach to predict mining-induced land subsidence using a new sparrow search algorithm (Xu et al, 2023). They compared five machine learning models -extreme gradient boosting, random forest, adaptive boosting (or AdaBoost), bootstrap aggregation, and gradient boosting -with their proposed model.…”

Section: Introductionmentioning

confidence: 99%

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Jahanmiri,

Noorian-Bidgoli

2024

Environ Sci Pollut Res

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Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as troughs subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. We consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

show abstract

Prediction of mining induced subsidence by sparrow search algorithm with extreme gradient boosting and TOPSIS method

Cited by 7 publications

References 56 publications

Research on prediction of PPV in open-pit mine used RUN-XGBoost model

Research on prediction of PPV in open-pit mine used RUN-XGBoost model

Blast Toes Volume Estimation for Post-Blast Efficiency: A Comparative Analysis of hybrid ensemble learning, voting, and base AI-algorithms

Land subsidence prediction in coal mining using machine learning models and optimization techniques

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