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This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

The consolidation behavior of prefabricated vertical drain (PVD)-installed soft deposits mainly depends on the PVD performance. The purpose of this study is to propose a numerical solution for the consolidation of PVD-installed soft soil using the large-strain theory, in which the reduction of discharge capacity of PVD according to depth and time is simultaneously considered.

The proposed solution also takes into account the general constitute relationship of soft soil. Subsequently, the proposed solution is applied to analyze and compare with the monitoring data of two cases, one is the experimental test and another is the test embankment in Saga airport.

The results show that the reduction of PVD discharge capacity according to depth and time increased the duration required to achieve a certain degree of consolidation. The consolidation rate is more sensitive to the reduction of PVD discharge capacity according to time than that according to the depth. The effects of the reduction of PVD discharge capacity according to depth are more evident when PVD discharge capacity decreases. The predicted results using the proposed numerical solution were validated well with the monitoring data for both cases in verification.

In this study, the variation of PVD discharge capacity is only considered in one-dimensional consolidation. However, it is challenging to implement a general expression for discharge capacity variation according to time in the two-dimensional numerical solution (two-dimensional plane strain model). This is the motivation for further study.

A geotechnical engineer could use the proposed numerical solution to predict the consolidation behavior of the drainage-improved soft deposit considering the PVD discharge capacity variation.

The large-strain consolidation of PVD-installed soft deposits could be predicted well by using the proposed numerical solution considering the PVD discharge capacity variations according to depth and time.