In this study, we selected 11 townships with severe ground subsidence located in Weishan County as the study area. Based on the interpretation data of Landsat images, the Binary logistic regression model was used to explore the relationship between land use and land cover (LULC) change and the related 7 driving factors at a resolution of 60 m. Using the CLUE-S model, combined with Markov model, the simulation of LULC under three scenarios—namely, natural development scenario, ecological protection scenario and farmland protection scenario—were explored. Firstly, using LULC map in 2005 as input data, we predicted the land use spatial distribution pattern in 2016. By comparing the actual LULC map in 2016 with the simulated map in 2016, the prediction accuracy was evaluated based on the Kappa index. Then, after validation, the spatial distribution pattern of LULC in 2025 under the three scenarios was simulated. The results showed the following: (1) The driving factors had satisfactory explanatory power for LULC changes. The Kappa index was 0.82, which indicated good simulation accuracy of the CLUE-S model. (2) Under the three scenarios, the area of other agricultural land and water body showed an increasing trend; while the area of farmland, urban and rural construction land, subsided land with water accumulation, and tidal wetland showed a decreasing trend, and the area of urban and rural construction land and tidal wetland decreased the fastest. (3) Under the ecological protection scenario, the farmland decreased faster than the other two scenarios, and most of the farmland was converted to ecological land such as garden land and water body. Under the farmland protection scenario, the area of tidal wetland decreased the fastest, followed by urban and rural construction land. We anticipate that our study results will provide useful information for decision-makers and planners to take appropriate land management measures in the mining area.

Critical rainfall thresholds can be the key to ensuring effective debris-flow forecasting. They are significant for the study of the trigger mechanisms of debris flows, for the forecast of the characteristics of future events, and for the development of engineering guidance for mitigation. Using a hydrological approach, we first calculated the flood peak discharge at different frequencies and then the corresponding rainfall thresholds for the initiation of different scales of debris flows in Zhouqu County, China. This was followed by the establishment of a functional relation between the intensity and duration of rainfall events that trigger debris flows of different warning levels in two initial soil conditions (dry and moist). This, in turn, yielded four warning levels and two preliminary warning levels. For the two early soil conditions (dry and moist) in the Sanyanyu Gully of Zhouqu County, the Level I (red) warning values for rainfall triggering debris flow are 56 and 51 mm h −1 , respectively; the Level II (orange) warning values are 41 and 38 mm h −1 , respectively; the Level III (yellow) warning values are 32 and 30 mm h −1 , respectively; and the Level IV (blue) warning values are 24 and 22 mm h −1 , respectively. The Level V preliminary warning values are 17 and 16 mm h −1 , respectively; and the Level VI preliminary warning values are 10 and 9.5 mm h −1 , respectively. The rainfall intensity and duration were found to exhibit a power function relation, I   =   αD β , where the values of α and β vary with the warning levels. Rainfall events capable of triggering debris flows in the new thresholds and intensity–duration relations presented here can be used for forecasting purposes and in operational geohazard warning systems. These research results also provide a scientific basis for regional hazard mitigation and reduction in Zhouqu County.

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)-LSTM network, the proposed model has higher forecast accuracy.

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