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Outline
With the expansion of open-pit mining scale, slope stability problems have become increasingly prominent. Great significance is attached to realizing safe and efficient open-pit mining by accurately predicting slope stability and taking preventive measures in advance. A whale optimization algorithm-convolutional neural network-long short-term memory (WOA-CNN-LSTM) prediction model for slope stability was constructed based on factor analysis. Six factors, including cohesion, internal friction angle, slope angle, slope height, pore pressure ratio and unit weight, were selected as the discrimination indicators of the model. Factor analysis was adopted to eliminate the correlation among the six indicators, reduce data dimensions and extract common factors. The extracted common factors were taken as the inputs of the WOA-CNN-LSTM model, and the model was applied to slope engineering cases to verify its reliability. The research results show that the CNN-LSTM network can be optimized by iteratively updating and searching for optimal network parameters using the WOA algorithm. The problems of the CNN-LSTM model, including falling into local optimal solutions and thegradient descent affectingthe prediction accuracy of slope stability, can be effectively overcome. The prediction accuracy and generalization ability of the model are thus improved, and a new idea for slope stability prediction is provided.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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