Development of geosteering system based on GWO–SVM model

Mao, Min; Yang, Hai; Xu, Fang; Ni, Pengbo; Wu, Haosheng

doi:10.1007/s00521-021-06583-6

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…The GWO-SVM model has a significant improvement in the average crossover rate and prediction accuracy. Compared with the original model, it increased by 5.38% and 7.74%, respectively [20,21]. In addition, the GWO exhibits exceptional competitiveness when compared to other optimization algorithms, such as PSO, GSA, DE, EP, and ES [19].…”

Section: Introductionmentioning

confidence: 97%

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

Li,

Zhang,

Zhao

et al. 2023

Water

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The occurrence of debris flows are a significant threat to human lives and property. Estimating the debris flow scale is a crucial parameter for assessing disaster losses in such events. Currently, the commonly used method for estimating debris flow runoff relies on fitting techniques, which often yield low prediction accuracy and limited data representation capabilities. Addressing these challenges, this study proposes an improved grey wolf algorithm optimized support vector machine prediction model. The model’s effectiveness is validated using data from 72 debris flow events in Beichuan County. The results demonstrate a prediction accuracy of 95.9% using this approach, indicating its strong predictive capabilities for debris flow scale. Additionally, it is observed that the basin area, the basin relative, and the main channel length are the key factors influencing debris flow scale in Beichuan County.

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Section: Introductionmentioning

confidence: 97%

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

Li,

Zhang,

Zhao

et al. 2023

Water

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Topical collections on machine learning based semantic representation and analytics for multimedia application

Yan

Liu

2022

Neural Comput & Applic

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Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm

et al. 2022

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The flexible job shop scheduling problem (FJSP) is of great importance for realistic manufacturing, and the problem has been proven to be NP-hard (non-deterministic polynomial time) because of its high computational complexity. To optimize makespan and critical machine load of FJSP, a discrete improved grey wolf optimization (DIGWO) algorithm is proposed. Firstly, combined with the random Tent chaotic mapping strategy and heuristic rules, a hybrid initialization strategy is presented to improve the quality of the original population. Secondly, a discrete grey wolf update operator (DGUO) is designed by discretizing the hunting process of grey wolf optimization so that the algorithm can solve FJSP effectively. Finally, an adaptive convergence factor is introduced to improve the global search ability of the algorithm. Thirty-five international benchmark problems as well as twelve large-scale FJSPs are used to test the performance of the proposed DIGWO. Compared with the optimization algorithms proposed in recent literature, DIGWO shows better solution accuracy and convergence performance in FJSPs at different scales.

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Development of geosteering system based on GWO–SVM model

Cited by 3 publications

References 11 publications

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

Debris Flow Scale Prediction Based on Correlation Analysis and Improved Support Vector Machine

Topical collections on machine learning based semantic representation and analytics for multimedia application

Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm

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