Landslides, a prevalent natural disaster, wreak havoc on both human lives and vital infrastructure, making them a significant global concern. Their devastating impact is immeasurable, necessitating proactive measures to minimize their occurrence. The ability to accurately forecast the severity of a landslide, including its potential fatality rate and the scale of destruction it may cause, holds tremendous potential for prevention and mitigation to reduce the risk and the damage caused by a landslide to infrastructure and life. In this study, the spatial variability in severity of landslides (in terms of mortality rates) and its dependence on various meteorological, geographical and soil composition has been attempted to be established. To do this, Ordinary Least Squares (global) and various Geographically Weighted (local) models have been employed to observe the varying relation between mortality rates and its various causative factors. Existence of geographical heterogeneity in the relationships is also investigated. The spatial pattern of landslide mortality and its associations with various causative variables in the South Asian Region are investigated and analysed. Through this, insights into targeting of prevention and mitigation measures for landslides based on a given location can be obtained by studying the various forms of heterogeneous spatial associations observed. The outcomes highlight that the local models in the form of Gaussian GWR and Poisson GWR outperform their global counterparts by a huge margin with better R2 and Adj R2 values. In comparison with Poisson GWR and Gaussian GWR, it is seen that Poisson GWR outperforms Gaussian GWR in terms of Mean Absolute Error, Mean Squared Error and Corrected Akaike Information Criterion. Furthermore, several intriguing local relationships patterns are also noted.

Land subsidence is an environmental geological phenomenon mainly caused by groundwater overexploitation. Long-term overexploitation of groundwater not only causes compaction of aquifer thickness and surface deformation but also leads to the loss of aquifer water storage capacity. The skeleton water storage coefficient (S-k) is an important parameter for evaluating the water storage capacity of aquifer groups. This article proposes a new research framework for obtaining the S-k of different aquifer groups: combining permanent scatter for SAR interferometry technology and a multiscale geographic weighted regression model to obtain subsidence information for different aquifer groups, inverting the S-k of different aquifer groups from the spatial scale, and discussing the deformation characteristics of soil layers under different water head change modes to evaluate the deformation and water storage characteristics of different aquifer groups. This framework is applied to the land subsidence region of the Beijing Plain. We calculated that the settlement proportions of different compression layer groups were 14.75%, 23.65%, 33.44%, and 28.16%. Due to the different lithological compositions and groundwater exploitation of different aquifers, the S-k values exhibit different spatial distribution characteristics. With the continuous development of subsidence, the water storage performance of the aquifer group is continuously declining. These findings contribute to managing the sustainable use of groundwater resources and controlling subsidence. It is demonstrated that the research framework proposed in this article can serve as an effective tool for obtaining settlement information and the S-k of different aquifer groups.