Landslide Susceptibility Assessment Based on Slope Unit and BP Neural Network

Wang, Miao; Wu, Caiyan; Liu, Xiao; Wei, Qihao

doi:10.4236/oalib.1109390

Cited by 1 publication

(1 citation statement)

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“…Based on this proposition, time series model and support vector machine regressi on model were widely used in landslide displacement prediction [2][3][4][5][6] . Due to landslide being an extremely complex structure, BP neural network was proved to be more suitable for its computational advantage resulting from its large number of neurons [7][8][9] . Meanwhile, with the development of deep learning, Tian Naiman etc.…”

Section: Introductionmentioning

confidence: 99%

Research on feature engineering strategy in landslide displacement prediction

Tang

Wang

Meng

et al. 2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Due to that redundant feature will degrade the accuracy and efficiency of the displacement prediction model, a feature engineering strategy is proposed in this paper to prompt the displacement prediction. Firstly, the displacement-related factors are sorted out, and these factors are enriched by feature interaction. Then, the decision tree algorithm is combined with Spearman correlation coefficient in feature screening phase to eliminate the redundant features. Finally, based on the feature screening results, an integrated AdaBoost-BP neural network prediction model is constructed. Taking Xinpu landslide in Chongqing as an example, the prediction accuracy of MAE and MSE is 0.234mm and 0.099mm respectively, which performs better than that without feature engineering. It is demonstrated that the proposed feature engineering has superior applicability for landslides prediction.

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Section: Introductionmentioning

confidence: 99%