| Summary: | The process of landslides in mine dumps is a dynamic, large-delay, highly nonlinear characteristic problem. There are many factors affecting the landslide of mine dumps, and each characteristic index has mutual influence. But there is no strict division standard of landslide warning for dumping sites, this paper proposes a method of combining Adaptive Boosting(AdaBoost), improved Particle Swarm Optimization(PSO) and Extreme Learning Machine(ELM) for short-term prediction of mine dumps. Firstly, the particle swarm optimization algorithm is used to obtain the optimal input parameters of the ELM model. Then, the adaptive learning algorithm is used to form a plurality of extreme learning machine weak predictors into a new strong predictor and predict it. The data collected by the soil field is taken as an example. The results show that the improved particle swarm optimization algorithm, adaptive lifting algorithm and extreme learning machine model combination method have better prediction accuracy than the extreme learning machine model optimized by particle swarm optimization algorithm and separate one. The prediction accuracy of the extreme learning machine model is close to the true value, which provides a possibility to realize the landslide warning of mine dumps.
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