Research on machine learning forecasting and early warning model for rainfall-induced landslides in Yunnan province.

Landslides are highly destructive geological disasters that pose a serious threat to the safety of people's lives and property. In this study, historical records of landslides in Yunnan Province, along with eight underlying factors of landslide (elevation, slope, aspect, lithology, land cover type, normalized difference vegetation index (NDVI), soil type, and average annual precipitation (AAP)), as well as historical rainfall and current rainfall data were utilized. Firstly, we analyzed the sensitivity of each underlying factor in the study area using the frequency ratio (FR) method and obtained a landslide susceptibility map (LSM). Then, we constructed a regional rainfall-induced landslides (RIL) probability forecasting model based on machine learning (ML) algorithms and divided warning levels. In order to construct a better RIL prediction model and explore the effects of different ML algorithms and input values of the underlying factor on the model, we compared five ML classification algorithms: extreme gradient boosting (XGBoost), k-nearest neighbor (KNN), support vector machine (SVM), logistic regression (LR), and random forest (RF) algorithms and three representatives of the input values of the underlying factors. The results show that among the obtained forecasting models, the LSM-based RF model performs the best, with an accuracy (ACC) of 0.906, an area under the curve (AUC) of 0.954, a probability of detection (POD) of 0.96 in the test set, and a prediction accuracy of 0.8 in the validation set. Therefore, we recommend using RF-LSM model as the RIL forecasting model for Yunnan Province and dividing warning levels.

The impact of urbanization on air stagnation: Shenzhen as case study.

One of the most concerning consequences arising from the dramatic urbanization in cities is air stagnation and the related high concentration of air pollutants. Many studies have investigated the impact of urbanization on air stagnation, but few have systematically evaluated such impact and its spatial-temporal variances at the municipal scale. This study proposed an approach based on high-resolution urban climate simulations for evaluating the impact of urbanization on air stagnation. We took the city of Shenzhen in south-eastern China, a city that grew from a small fishing and farming village to a highly urbanized city in the past thirty years, as a compelling case study. Using the WRF/Noah LSM/SLUCM model, we simulated and evaluated the probability of 6-hourly air stagnation cases (ASCs) in 1979 and 2010 at the spatial resolution of 1-km2 to demonstrate the change over a thirty-year period. Comparison results show that urbanization worsened the problem of air stagnation in Shenzhen. The number of 6-hourly ASCs has increased by 21,700 for the entire Shenzhen, and by 11.4 on average for each grid with a 1 km2 size. A maximum increase of 458 ASCs in a grid was also observed.