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Outline
In order to improve the accuracy of slope stability prediction, a slope stability prediction model based on multi strategy improved cuckoo search algorithm (ICS) optimized Elman neural network was proposed. Firstly, the population was initialized using Sinusoidal chaos, and strategies such as dynamic step size control and 1% step discretization of discovery probability were introduced to improve the slow convergence speed and poor accuracy of the cuckoo search algorithm. Simulation results on 7 benchmark functions show that compared with the CS algorithm, the optimization ability and convergence speed of the ICS algorithm are significantly improved. Then, the ICS algorithm was used to optimize the weights and thresholds of the Elman neural network, and the optimal slope stability prediction model was constructed. The results show that the relative error range of the ICS-Elman model for predicting the safety factor of slope stability is –2.81%−6.98%, with root mean square error and mean absolute error of 0.275 0 and 0.342 2, respectively. Compared with the Elman and CS-Elman models, the ICS-Elman model has better stability and accuracy. Compared with the CPSO-BP neural network, the ICS-Elman neural network has a relative error range of –1.57%−1.25% in predicting values, indicating better prediction accuracy.
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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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