Research on a BP Neural Network Slope Safety Coefficient Prediction Model Based on Improved Sparrow Algorithm Optimization

Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured. This work proposes an improved sparrow search algorithm to optimize the slope safety factor prediction model (ISSA–BP) of a BP neural network,...

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Published in:Applied sciences Vol. 13; no. 14; p. 8446
Main Authors: Wang, Yiwen, Liu, Dongna, Dong, Haiyu, Lin, Junwei, Zhang, Qi, Zhang, Xiaohui
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.07.2023
Subjects:
Accuracy
Algorithms
Analysis
Back propagation
BP neural network
Economic development
Food
Foraging behavior
Geology
Intelligence
Landslides
Landslides & mudslides
Learning strategies
Machine learning
Mathematical functions
neural network optimization
Neural networks
Optimization algorithms
Research methodology
Sensors
slope safety factor
sparrow search algorithm
Velocity
ISSN:2076-3417, 2076-3417
Online Access:Get full text
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Abstract Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured. This work proposes an improved sparrow search algorithm to optimize the slope safety factor prediction model (ISSA–BP) of a BP neural network, through an improvement in two aspects: introducing dynamic weight factors and reverse learning strategies to realize adaptive searches. The optimal value improves a defect in the traditional model, preventing it from easily falling into the local minimum. First, combined with 352 sets of actual slope data, three machine learning models were used to predict the safety factor of the slope. Then, the accuracy index was used for evaluation. Compared with other models, the MAPE, RMSE, and R2 of the ISSA-BP model were 1.64%, 0.0296, and 0.99, respectively, and the error was reduced by 78% compared with the BP neural network, showing better accuracy. Finally, the three models were applied to the slope stability analysis of Tianbao Port in Wenshan Prefecture. The research shows that the predicted value of the ISSA–BP model was the closest to the actual safety factor, which verified the experimental results. The improved ISSA–BP model can effectively predict the safety factor of slopes under different conditions, and it provides a new technology for slope disaster warning and control.
AbstractList Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured. This work proposes an improved sparrow search algorithm to optimize the slope safety factor prediction model (ISSA–BP) of a BP neural network, through an improvement in two aspects: introducing dynamic weight factors and reverse learning strategies to realize adaptive searches. The optimal value improves a defect in the traditional model, preventing it from easily falling into the local minimum. First, combined with 352 sets of actual slope data, three machine learning models were used to predict the safety factor of the slope. Then, the accuracy index was used for evaluation. Compared with other models, the MAPE, RMSE, and R2 of the ISSA-BP model were 1.64%, 0.0296, and 0.99, respectively, and the error was reduced by 78% compared with the BP neural network, showing better accuracy. Finally, the three models were applied to the slope stability analysis of Tianbao Port in Wenshan Prefecture. The research shows that the predicted value of the ISSA–BP model was the closest to the actual safety factor, which verified the experimental results. The improved ISSA–BP model can effectively predict the safety factor of slopes under different conditions, and it provides a new technology for slope disaster warning and control.
Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured. This work proposes an improved sparrow search algorithm to optimize the slope safety factor prediction model (ISSA–BP) of a BP neural network, through an improvement in two aspects: introducing dynamic weight factors and reverse learning strategies to realize adaptive searches. The optimal value improves a defect in the traditional model, preventing it from easily falling into the local minimum. First, combined with 352 sets of actual slope data, three machine learning models were used to predict the safety factor of the slope. Then, the accuracy index was used for evaluation. Compared with other models, the MAPE, RMSE, and R[sup.2] of the ISSA-BP model were 1.64%, 0.0296, and 0.99, respectively, and the error was reduced by 78% compared with the BP neural network, showing better accuracy. Finally, the three models were applied to the slope stability analysis of Tianbao Port in Wenshan Prefecture. The research shows that the predicted value of the ISSA–BP model was the closest to the actual safety factor, which verified the experimental results. The improved ISSA–BP model can effectively predict the safety factor of slopes under different conditions, and it provides a new technology for slope disaster warning and control.
Audience Academic
Author Zhang, Qi
Wang, Yiwen
Zhang, Xiaohui
Liu, Dongna
Dong, Haiyu
Lin, Junwei
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Snippet Through the stability evaluation of a slope, a landslide geological disaster can be identified, and the safety and risk control of a project can be ensured....
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SubjectTerms Accuracy
Algorithms
Analysis
Back propagation
BP neural network
Economic development
Food
Foraging behavior
Geology
Intelligence
Landslides
Landslides & mudslides
Learning strategies
Machine learning
Mathematical functions
neural network optimization
Neural networks
Optimization algorithms
Research methodology
Sensors
slope safety factor
sparrow search algorithm
Velocity
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Title Research on a BP Neural Network Slope Safety Coefficient Prediction Model Based on Improved Sparrow Algorithm Optimization
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