Open pit slope stability monitoring based on machine learning improved by water wave optimisation algorithm

Aiming at the problem of insufficient prediction accuracy of traditional machine learning model in slope stability monitoring of open-pit mine, this paper proposes an improved machine learning monitoring method based on water wave optimisation (WWO). Firstly, the improved water wave optimisation algorithm is introduced to enhance the global search ability and balance the convergence speed and local extremum escape performance of the algorithm. On this basis, IWWO is used to optimise the machine learning method synchronously. Finally, the actual slope engineering data are introduced, and the prediction results of this model are compared with the calculation results of rigid body limit equilibrium method. The final results show that this method can identify the precursor of slope instability in real time, and provide reliable technical support for mine safety early warning.