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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Vydáno v:	Natural hazards (Dordrecht) Ročník 120; číslo 1; s. 297 - 319
Hlavní autoři:	Zheng, Binbin, Wang, Jiahe, Feng, Shuhu, Yang, Han, Wang, Wensong, Feng, Tingting, Hu, Tianyu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.01.2024 Springer Nature B.V
Témata:	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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Abstract	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.
AbstractList	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. 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.
Author	Feng, Shuhu Wang, Wensong Wang, Jiahe Zheng, Binbin Yang, Han Hu, Tianyu Feng, Tingting
Author_xml	– sequence: 1 givenname: Binbin surname: Zheng fullname: Zheng, Binbin email: zhengbin_vip@sina.com organization: School of Management Science and Engineering, Shandong Technology and Business University, Shandong Emergency Management Institute, Shandong Technology and Business University – sequence: 2 givenname: Jiahe surname: Wang fullname: Wang, Jiahe organization: School of Management Science and Engineering, Shandong Technology and Business University – sequence: 3 givenname: Shuhu surname: Feng fullname: Feng, Shuhu organization: School of Management Science and Engineering, Shandong Technology and Business University, Shandong Emergency Management Institute, Shandong Technology and Business University – sequence: 4 givenname: Han surname: Yang fullname: Yang, Han organization: School of Resources and Safety Engineering, Chongqing University – sequence: 5 givenname: Wensong surname: Wang fullname: Wang, Wensong organization: State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology – sequence: 6 givenname: Tingting surname: Feng fullname: Feng, Tingting organization: School of Management Science and Engineering, Shandong Technology and Business University – sequence: 7 givenname: Tianyu surname: Hu fullname: Hu, Tianyu organization: School of Civil Engineering, Chongqing Jiaotong University
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SubjectTerms	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
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Title	A new, fast, and accurate algorithm for predicting soil slope stability based on sparrow search algorithm-back propagation
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