Landslides represent major threats to life and property in many areas of the world, such as the landslides in the Three Gorges Dam area in mainland China.

The paper summarizes key aspects in the assessment of geological hazard and risk and exemplifies these with applications in practice. Methods for estimating the risk, from simple qualitative approaches to more complex quantitative ones are presented. Risk frameworks are presented. Key components of risk management, such as the notion of acceptable and/or tolerable risk, risk mitigation, especially early warning systems, risk communication and multi-risk issues are addressed. Case studies on the stability of a rock slope in Western Norway, vulnerability assessment and risk management for landslides, the assessment of tsunami risk in a coastal town, risk mitigation to prevent quick clay landslides, the hazard and risk associated with a major dam breach, the implementation of early warning systems to prevent catastrophic consequences and risk communication with all categories stakeholders are provided. The paper also briefly describes three ongoing research projects in the 7^th Framework Programme of the European Commission on risk assessment and management of geohazards.

The notions of risk assessment, risk management, acceptable risk and risk mitigation are addressed, and the State-of-Practice of geo-risk assessment and risk mitigation is exemplified with case studies. The examples illustrate the assessment of hazard, vulnerability and risk, and the treatment (mitigation) of risk under different design situations, including underwater slopes subjected to earthquake hazard, the stability of quick clays, and the potential breach of embankment dams.

Leak-off pressure (LOP) is a key parameter to determine the allowable weight of drilling mud in a well and the in situ horizontal stress. The LOP test is run in situ and is frequently used by the petroleum industry. If the well pressure exceeds the LOP, wellbore instability may occur, with hydraulic fracturing and large mud losses in the formation. A reliable prediction of LOP is required to ensure safe and economical drilling operations. The prediction of LOP is challenging because it is affected by the usually complex earlier geological loading history, and the values of LOP and their measurements can vary significantly geospatially. This paper investigates the ability of machine learning algorithms to predict leak-off pressure on the basis of geospatial information of LOP measurements. About 3000 LOP test data were collected from 1800 exploration wells offshore Norway. Three machine learning algorithms (the deep neural network (DNN), random forest (RF), and support vector machine (SVM) algorithms) optimized by three hyperparameter search methods (the grid search, randomized search and Bayesian search) were compared with multivariate regression analysis. The Bayesian search algorithm needed fewer iterations than the grid search algorithms to find an optimal combination of hyperparameters. The three machine learning algorithms showed better performance than the multivariate linear regression when the features of the geospatial inputs were properly scaled. The RF algorithm gave the most promising results regardless of data scaling. If the data were not scaled, the DNN and SVM algorithms, even with optimized parameters, did not provide significantly improved test scores compared to the multivariate regression analysis. The analyses also showed that when the number of data points in a geographical setting is much smaller than that of other geographical areas, the prediction accuracy reduces significantly.