Two significant uncertainties that are crucial for landslide susceptibility prediction modeling are attribute interval numbers (AIN) division of continuous landslide impact factors in frequency ratio analysis and various susceptibility prediction models. Five continuous landslide impact factor interval attribute classifications (4, 8, 12, 16, 20) and three data-driven models (deep belief networks (DBN), random forest (RF), and neural network (back propagation (BP)) were used for a total of fifteen different scenarios of landslide susceptibility prediction studies in order to investigate the effects of these two factors on modeling and perform a landslide susceptibility index uncertainty analysis (including precision evaluation and statistical law). The findings indicate that: (1) The results demonstrate that for the same model, as the interval attribute value rises from 4 to 8 and finally to 20, the forecast accuracy of landslide susceptibility initially increases gradually, then progressively grows until stable. (2) The DBN model, followed by the RF and BP models, provides the highest prediction accuracy for the same interval attribute value. (3) AIN = 20 and DBN models have the highest prediction accuracy under 15 combined conditions, while AIN = 4 and BP models have the lowest. The accuracy and efficiency of landslide susceptibility modeling are higher when the AIN = 8 and DBN models are combined. (4) The landslide susceptibility index uncertainty predicted by the deeper learning model and the bigger interval attribute value is comparatively low, which is more in line with the real landslide probability distribution features. The conditions that the environmental component attribute interval is divided into eight parts and DBN models are used allow for the efficient and accurate construction of the landslide susceptibility prediction model.

Ground-based synthetic aperture radar interferometry (GB-InSAR) enables the continuous monitoring of areal deformation and can thus provide near-real-time control of the overall deformation state of dam surfaces. In the continuous small-scale deformation monitoring of a reservoir dam structure by GB-InSAR, the ground-based synthetic aperture radar (GB-SAR) image acquisition may be interrupted by multiple interfering factors, such as severe changes in the meteorological conditions of the monitoring area and radar equipment failures. As a result, the observed phases before and after the interruption cannot be directly connected, and the original spatiotemporal datum for the deformation measurement is lost, making the follow-up monitoring results unreliable. In this study, a multi-threshold strategy was first adopted to select coherent point targets (CPTs) by using successive GB-SAR image sequences. Then, we developed differential GB-InSAR with image subsets based on the CPTs to solve the dam surface deformation before and after aberrant interruptions. Finally, a deformation monitoring experiment was performed on an actual large reservoir dam. The effectiveness and accuracy of the abovementioned method were verified by comparing the results with measurements by a reversed pendulum monitoring system.

Although constructing a dam can bring significant economic and social benefits to a region, it can be catastrophic for the population living downstream when it breaks. Given the dynamic and nonlinear characteristics of dam deformation, the traditional dam prediction model has been unable to meet the actual engineering demands. Consequently, this paper advocates for a novel method to solve this issue. The proposed method is based on the optimization of improved chicken swarm (ICSO) and support vector machine (SVM). To begin with, the mean square error is used as the objective function, and then, we apply the improved chicken swarm algorithm to iterate continuously, and finally, the optimal SVM parameters are obtained. Through the modeling and simulation experiments of a nonlinear system, the validity of the improved chicken swarm algorithm to optimize an SVM model has been verified. Based on the horizontal displacement monitoring data of FengMan Dam, this paper analyzed the influencing factors of horizontal displacement. According to the results, three prediction models have been established, respectively: the SVM prediction model optimized by the improved chicken swarm algorithm, the SVM prediction model optimized by the basic chicken swarm algorithm and the BP neural network prediction model optimized by the genetic algorithm. The obtained results from the experiment authenticate the validity and superiority of the proposed method.

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm³cm⁻³, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stability.