Article: Open pit slope stability monitoring based on machine learning improved by water wave optimisation algorithm Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.13 pp.61 - 80 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

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

Authors: Yunpeng Gong; Qing Yang

Addresses: School of Civil Engineering, Xi'an University of Architecture and Technology Huaqing College, Xi'an 710043, China ' Shaanxi Energy Institute, Xi'an 712000, China

Abstract: 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.

Keywords: water wave optimisation algorithm; support vector machine; SVM; random forest; long short-term memory; open pit slope.

DOI: 10.1504/IJICT.2025.146170

International Journal of Information and Communication Technology, 2025 Vol.26 No.13, pp.61 - 80

Received: 11 Mar 2025
Accepted: 22 Mar 2025
Published online: 08 May 2025 *

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

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