Landslides account for approximately 5% of natural disasters resulting in significant socio-economic impacts. As a major infrastructure issue, slope stability has been traditionally analyzed with multiple deterministic and probabilistic methods to evaluate the stability of slopes or the probability of landslides. Geotechnical engineers tend to visit the sites of slopes, measure the geometry and soil properties, and use those traditional methods to analyze the slope stability and provide a factor of safety evaluation and recommendation. The fast-growing new technologies such as the internet of things and big data analytics provide new directions for natural hazard prevention. This study is the first to use deep learning as a new method for slope stability analysis for landslide prevention. A convolutional neural network was used to establish the model via transfer learning for processing simulated slope images. After training, our model can accurately predict the factor of safety of slopes for new slope images. Our proposed method was validated by comparing it with a classic limit equilibrium method, i.e., the simplified Bishop method, which is widely used in commercial programs for slope stability analysis. The comparison results showed that our proposed deep learning method outperformed the traditional method by decreasing the computation time by orders of magnitude without sacrificing accuracy. The results demonstrated the possibility and advantages of using deep learning as a new type of slope stability analysis method, including its ability to analyze raw image data directly, high level of automation, satisfactory accuracy, and short computing time, which will enable onsite evaluation for slope stability analysis. Thus, it facilitates fast in-situ decision-making for geotechnical applications and ensures the feasibility of using the internet of things and big data analytics for natural hazard prevention.

Landslides in urban areas have been relatively well-documented in landslide inventories despite issues in accuracy and completeness, e.g., the absence of small landslides. By contrast, less attention has been paid to landslides in sparsely populated areas in terms of their occurrences and locations. This study utilizes high-resolution and LiDAR-derived digital elevation models (DEMs) at two different times for landslide detection to (1) improve the localization and detection accuracies in landslide inventories, (2) minimize human intervention in the landslide detection process, and (3) identify landslides that cannot be easily documented in the current state of the practice. To achieve this goal, multiple preprocessing steps were used to ensure the spatial alignment of the multi-temporal DEMs. Map algebra was then used to calculate the vertical displacement for each cell and create a DEM of Difference (DoD) to obtain a quantitative estimation of ground deformations. Next, the elevation changes were filtered via an appropriate Level of Detection (LoD) threshold to mark potential landslide candidates. The landslide candidates were further assessed with the aid of customized topographic maps as auxiliary data and pattern recognition to distinguish landslides (true positive changes) from construction, erosion, and deposition (false positives). The results from the proposed method were compared with existing landslide inventories and reports to evaluate its performance. The new method was also validated with temporal high-resolution Google Earth images. The results showed the successful application of the method in landslide detection and mapping. Compared with traditional methods, the proposed method provides a semi-automatic way to obtain landslide inventories with publicly available yet lowly utilized DEM data, which can be valuable in preliminary analysis for landslide detection.

This paper presents a comparison study between methods of deep learning as a new category of slope stability analysis, built upon the recent advances in artificial intelligence and conventional limit equilibrium analysis methods. For this purpose, computer code was developed to calculate the factor of safety (FS) using four limit equilibrium methods: Bishop’s simplified method, the Fellenius method, Janbu’s simplified method, and Janbu’s corrected method. The code was verified against Slide2 in RocScience. Subsequently, the average FS values were used to approximate the “true” FS of the slopes for labeling the images for deep learning. Using this code, a comprehensive dataset of slope images with wide ranges of geometries and soil properties was created. The average FS values were used to label the images for implementing two deep learning models: a multiclass classification and a regression model. After training, the deep learning models were used to predict the FS of an independent set of slope images. Finally, the performance of the models was compared to that of the conventional methods. This study found that deep learning methods can reach accuracies as high as 99.71% while improving computational efficiency by more than 18 times compared with conventional methods.

The occurrence of landslides has been increasing in recent years due to intense and prolonged rainfall events. Lowering the groundwater in natural and man-made slopes can help to mitigate the hazards. Subsurface drainage systems equipped with pumps have traditionally been regarded as a temporary remedy for lowering the groundwater in geosystems, whereas long-term usage of pumping-based techniques is uncommon due to the associated high operational costs in labor and energy. This study investigates the intelligent control of groundwater in slopes enabled by deep reinforcement learning (DRL), a subfield of machine learning for automated decision-making. The purpose is to develop an autonomous geosystem that can minimize the operating cost and enhance the system’s safety without introducing human errors and interventions. To prove the concept, a seepage analysis model was implemented using a partial differential equation solver, FEniCS, to simulate the geosystem (i.e., a slope equipped with a pump and subjected to rainfall events). A Deep Q-Network (i.e., a DRL learning agent) was trained to learn the optimal control policy for regulating the pump’s flow rate. The objective is to enable intermittent control of the pump’s flow rate (i.e., 0%, 25%, 50%, 75%, and 100% of the pumping capacity) to keep the groundwater close to the target level during rainfall events and consequently help to prevent slope failure. A comparison of the results with traditional proportional-integral-derivative-controlled and uncontrolled water tables showed that the geosystem integrated with DRL can dynamically adapt its response to diverse weather events by adjusting the pump’s flow rate and improve the adopted control policy by gaining more experience over time. In addition, it was observed that the DRL control helped to mitigate slope failure during rainfall events.

Subsurface drainage has been widely accepted to mitigate the hazard of landslides in areas prone to flooding. Specifically, the use of drainage wells with pumping systems has been recognized as an effective short-term solution to lower the groundwater table. However, this method has not been well considered for long-term purposes due to potentially high labor costs. This study aims to investigate the idea of an autonomous pumping system for subsurface drainage by leveraging conventional geotechnical engineering solutions and a deep learning technique—Long-Short Term Memory (LSTM)—to establish a geotechnical cyber-physical system for rainfall-induced landslide prevention. For this purpose, a typical soil slope equipped with three pumps was considered in a computer simulation. Forty-eight cases of rainfall events with a wide range of varieties in duration, total rainfall depths, and different rainfall patterns were generated. For each rainfall event, transient seepage analysis was performed using newly proposed Python code to obtain the corresponding pump’s flow rate data. A policy of water pumping for maintaining groundwater at a desired level was assigned to the pumps to generate the data. The LSTM takes rainfall event data as the input and predicts the required pump’s flow rate. The results from the trained model were validated using evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and R². The R²-scores of 0.958, 0.962, and 0.954 for the predicted flow rates of the three pumps exhibited high accuracy of the predictions using the trained LSTM model. This study is intended to make a pioneering step toward reaching an autonomous pumping system and lowering the operational costs in controlling geosystems.