首页|Studies from Guizhou Normal University Provide New Data on Machine Learning (A New Interpretable Prediction Framework for Steplike Landslide Displacement)
Studies from Guizhou Normal University Provide New Data on Machine Learning (A New Interpretable Prediction Framework for Steplike Landslide Displacement)
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NETL
NSTL
Springer Nature
Research findings on Machine Learning are discussed in a new report. According to news reporting originating from Guizhou, People’s Republic of China, by NewsRx correspondents, research stated, “Machine learning models perform satisfactorily in landslide displacement prediction, but they are generally black-box models that are difficult to gain the trust of decision-makers. Therefore, a three-stage prediction framework based on the Hodrick-Prescott (HP) filter, double exponential smoothing (DES), natural gradient boosting (NGBoost), and Shapley additive explanations (SHAP) was proposed.” Funders for this research include Guizhou Provincial Science and Technology Projects, Guizhou University Research Initiation Fund. Our news editors obtained a quote from the research from Guizhou Normal University, “The framework quantifies the uncertainty in the predictions and provides fully transparent outputs. In the first stage, the HP filter decomposes cumulative displacements into trend and period displacements. The second stage uses DES and NGBoost to predict them separately. In the third stage, we compute the SHAP values of the features to analyze the impact of the features on the model output. It is applied to the Bazimen and Baishuihe landslides in the Three Gorges Reservoir area and compared with other literature. The results show that the framework can achieve high accuracy in both point and interval prediction, and its performance is similar to or even better than other models. And it is easier to operate and applicable to a wider range of people. Most importantly, the framework can interpret the model, allows users to verify the consistency of the model prediction basis with the landslide evolution mechanism, and reduces random errors by optimizing the data.”
GuizhouPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningGuizhou Normal University
NETL
NSTL
Springer Nature
