A new, fast, and accurate algorithm for predicting soil slope stability based on sparrow search algorithm-back propagation

Slope stability prediction is one of the most essential and critical tasks in mining and geotechnical projects. A fast and precise slope stability prediction is crucial for safe operations and cost-effective slope maintenance. In this work, a back propagation (BP) neural network based on a sparrow s...

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Vydané v:Natural hazards (Dordrecht) Ročník 120; číslo 1; s. 297 - 319
Hlavní autori: Zheng, Binbin, Wang, Jiahe, Feng, Shuhu, Yang, Han, Wang, Wensong, Feng, Tingting, Hu, Tianyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Dordrecht Springer Netherlands 01.01.2024
Springer Nature B.V
Predmet:
Algorithms
Back propagation
Back propagation networks
Civil Engineering
Coefficient of variation
Coefficients
Decision trees
Earth and Environmental Science
Earth Sciences
Environmental Management
Geophysics/Geodesy
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Independent variables
Natural Hazards
Neural networks
Original Paper
Predictions
Regression coefficients
Safety
Search algorithms
Slope stability
Soil stability
Support vector machines
Slope stability
BP neural network
Safety factor
Sparrow search algorithm
ISSN:0921-030X, 1573-0840
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Popis
Shrnutí:Slope stability prediction is one of the most essential and critical tasks in mining and geotechnical projects. A fast and precise slope stability prediction is crucial for safe operations and cost-effective slope maintenance. In this work, a back propagation (BP) neural network based on a sparrow search algorithm (SSA) is developed to predict the slope safety coefficients by using five input features, including unit weight ( γ ), cohesion ( c ), friction angle ( φ ), slope angle ( α ), and slope height ( H ). The proposed model is trained and simulated using 55 data samples. The regression coefficient of the proposed SSA-BP neural network is 0.9405, with a mean relative error (MAE) of 0.1684. Compared with fusion algorithms, such as Ridge Regression (RR), Decision Tree (DT), Random forest (RF), Support Vector Regression (SVR), and Light Gradient Boosting Machine (lightGBM), the proposed method yields more accurate and robust prediction results. Furthermore, a multivariate function relationship between the slope safety coefficient and the five variables is constructed based on the relationship between five independent input variables and the variation of the safety coefficient. The proposed method introduces a novel approach for calculating the slope safety coefficient.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-023-06210-8