An accurate slope prediction model is important for slope reinforcement before the disaster. The k‐nearest neighbor (KNN) algorithm, as a simple and effective nonparametric machine learning method, is widely applied in classification recognition. In our study, the k‐nearest neighbor (KNN) algorithm is improved to reduce its sample dependence and improve the robustness of the algorithm, and then the prediction model of the slope stability is proposed based on the improved k‐nearest neighbor (KNN) algorithm. Extensive experimental results show that our proposed prediction model achieves high prediction performance in this regard. Moreover, a comparison between our proposed prediction model and the finite element method, which is the classical theoretical method of slope stability, was made, which will provide an important approach to predicting the slope stability for slope engineering. Finally, shaking table test of a slope model is conducted to evaluate whether the slope is stable or not, and the experimental results are in good agreement with the prediction results of our proposed prediction model, which further demonstrates its effectiveness.

The retrieval method plays a crucial role in deriving high‐quality temperature and humidity profiles for the ground‐based microwave radiometer (MWR). In this paper, we propose a new approach for temperature and humidity profile retrieval from the ground‐based MWR. First, a radiosonde dataset is established and the simulated brightness temperatures incorporated into the training dataset are calculated using a monochromatic radiative transfer model based on the constructed radiosonde dataset. Subsequently, the backpropagation neural network (BPNN) algorithm is adopted as the inversion tool for atmospheric temperature and humidity profiles. Meanwhile, the support vector regression (SVR) algorithm is proposed to develop bias correction models for the observed brightness temperatures, aiming further to improve retrieval performance in practical inversion scenarios. Extensive experimental results demonstrate the effectiveness of the proposed approach for retrieving temperature and humidity profiles.