The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction

Jiang, Hongwei; Wu, Jiayi; Zhou, Hao; Liu, Mengjie; Li, Shihao; Wu, Yuexu; Guo, Yongfan

doi:10.3390/eng6080169

Open AccessArticle

The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction

by

Hongwei Jiang

^1,2,3

,

Jiayi Wu

¹,

Hao Zhou

¹,

Mengjie Liu

¹,

Shihao Li

¹,

Yuexu Wu

⁴ and

Yongfan Guo

^5,*

¹

School of Urban Construction, Changzhou University, Changzhou 213164, China

²

School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454003, China

³

Badong National Observation and Research Station of Geohazards, China University of Geosciences, Wuhan 430074, China

⁴

Wushan Geological Environment Monitoring Station, Chongqing 404700, China

⁵

Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada

^*

Author to whom correspondence should be addressed.

Eng 2025, 6(8), 169; https://doi.org/10.3390/eng6080169

Submission received: 30 May 2025 / Revised: 11 July 2025 / Accepted: 21 July 2025 / Published: 23 July 2025

(This article belongs to the Section Chemical, Civil and Environmental Engineering)

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Abstract

Early warning systems depend heavily on the accuracy of landslide displacement forecasts. This study focuses on the Bazimen landslide located in the Three Gorges Reservoir region and proposes a hybrid prediction approach combining support vector regression (SVR) and long short-term memory (LSTM) networks. These models are optimized via the K-Nearest Neighbor (KNN) algorithm. Initially, cumulative displacement data were separated into trend and cyclic elements using a smoothing approach. SVR and LSTM were then used to predict the components, and KNN was introduced to optimize input factors and classify the results, improving accuracy. The final KNN-optimized SVR-LSTM model effectively integrates static and dynamic features, addressing limitations of traditional models. The results show that LSTM performs better than SVR, with an RMSE and MAPE of 24.73 mm and 1.87% at monitoring point ZG111, compared to 30.71 mm and 2.15% for SVR. The sequential hybrid model based on KNN-optimized SVR and LSTM achieved the best performance, with an RMSE and MAPE of 23.11 mm and 1.68%, respectively. This integrated model, which combines multiple algorithms, offers improved prediction of landslide displacement and practical value for disaster forecasting in the Three Gorges area.

Keywords: Bazimen landslide; landslide displacement prediction; integrated models; disaster forecasting

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MDPI and ACS Style

Jiang, H.; Wu, J.; Zhou, H.; Liu, M.; Li, S.; Wu, Y.; Guo, Y. The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction. Eng 2025, 6, 169. https://doi.org/10.3390/eng6080169

AMA Style

Jiang H, Wu J, Zhou H, Liu M, Li S, Wu Y, Guo Y. The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction. Eng. 2025; 6(8):169. https://doi.org/10.3390/eng6080169

Chicago/Turabian Style

Jiang, Hongwei, Jiayi Wu, Hao Zhou, Mengjie Liu, Shihao Li, Yuexu Wu, and Yongfan Guo. 2025. "The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction" Eng 6, no. 8: 169. https://doi.org/10.3390/eng6080169

APA Style

Jiang, H., Wu, J., Zhou, H., Liu, M., Li, S., Wu, Y., & Guo, Y. (2025). The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction. Eng, 6(8), 169. https://doi.org/10.3390/eng6080169

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The Application of KNN-Optimized Hybrid Models in Landslide Displacement Prediction

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