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This paper focuses on large-scale landslide susceptibility modelling in NW Croatia. The objective of this research was to provide new insight into stable and unstable area sampling strategies on a representative inventory of small and shallow landslides mainly occurring in soil and soft rock. Four strategies were tested for stable area sampling (random points, stable area polygon, stable polygon buffering and stable area centroid) in combination with four strategies for unstable area sampling (landslide polygon, smoothing digital terrain model derived landslide conditioning factors, polygon buffering and landslide centroid), resulting in eight sampling scenarios. Using Logistic Regression, Neural Network, Random Forest and Support Vector Machine algorithm, 32 models were derived and analysed. The main conclusions reveal that polygon sampling of unstable areas is an imperative in large-scale modelling, as well as that subjective and/or biased stable area sampling leads to misleading models. Moreover, Random Forest and Neural Network proved to be more favourable methods (0.804 and 0.805 AUC, respectively), but also showed extreme sensitivity to the tested sampling strategies. In the comprehensive comparison, the advantages and disadvantages of 32 derived models were analysed through quantitative and qualitative parameters to highlight their application to large-scale landslide zonation. The results yielded by this research are beneficial to the susceptibility modelling step in large-scale landslide susceptibility assessments as they enable the derivation of more reliable zonation maps applicable to spatial and urban planning systems. [ABSTRACT FROM AUTHOR]

The study presents a landslide susceptibility assessment on a large scale in the City of Zagreb (Croatia). The susceptibility analysis was performed using the Weight of Evidence model in the pilot area (21 km² ) and applying the obtained weight values for each class of conditioning factors in the study area (130 km² ). The input data were LiDAR-based landslide inventory and six conditioning factors derived from 5 m LiDAR DTM, 5 m SfM DEM, and geological and land-use maps. The validation of the susceptibility assessment for the study area was evaluated with a ROC curve, which showed a high prediction rate (AUC = 84.4%), similar to the prediction rate for the pilot area (AUC = 86.9%). Based on the results, it can be concluded that the proposed method for large-scale landslide susceptibility assessment, where susceptibility conditions are defined in smaller pilot areas, can be applied to larger research areas with similar geomorphological and geological conditions. [ABSTRACT FROM AUTHOR]

The objective of the study is to show that landslide conditioning factors derived from different source data give significantly different relative influences on the weight factors derived with statistical models for landslide susceptibility modelling and risk analysis. The analysis of the input data for large-scale landslide hazard assessment was performed on a study area (20.2 km2) in Hrvatsko Zagorje (Croatia, Europe), an area highly susceptible to sliding with limited geoinformation data, including landslide data. The main advantage of remote sensing technique (i.e., LiDAR, Light Detection and Ranging) data and orthophoto images is that they enable 3D surface models with high precision and spatial resolution that can be used for deriving all input data needed for landslide hazard assessment. The visual interpretation of LiDAR DTM (Digital Terrain Model) morphometric derivatives resulted in a detailed and complete landslide inventory map, which consists of 912 identified and mapped landslides, ranging in size from 3.3 to 13,779 m2. This inventory was used for quantitative analysis of 16 input data layers from 11 different sources to analyse landslide presence in factor classes and thus comparing landslide conditioning factors from available small-scale data with high-resolution LiDAR data and orthophoto images, pointing out the negative influence of small-scale source data. Therefore, it can be concluded that small-scale landslide factor maps derived from publicly available sources should not be used for large-scale analyses because they will result in incorrect assumptions about conditioning factors compared with LiDAR DTM derivative factor maps. Furthermore, high-resolution LiDAR DTM and orthophoto images are optimal input data because they enable derivation of the most commonly used landslide conditioning factors for susceptibility modelling and detailed datasets about elements at risk (i.e., buildings and traffic infrastructure data layers). [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The monitoring of landslides has a practical application for the prevention of hazards, especially in the case of large deep-seated landslides. Monitoring data are necessary to understand the relationships between movement and triggers, to predict movement, and to establish an early warning system. This paper compares two phenomenological models for the prediction of the movement of the Kostanjek landslide, the largest landslide in the Republic of Croatia. The prediction models are based on a 4-year monitoring data series of landslide movement, groundwater level, and precipitation. The presented models for landslide movement prediction are divided into the model for the prediction of groundwater level from precipitation data and the model for the prediction of landslide velocity from groundwater level data. The statistical techniques used for prediction are multiple linear regression and random forests. For the prediction of groundwater level, 75 variables calculated from precipitation and evapotranspiration data were used, while for the prediction of landslide movement, 10 variables calculated from groundwater level data were used. The prediction results were mutually compared by k-fold cross-validation. The root mean square error analyses of k-fold cross-validation showed that the results obtained from random forests are just slightly better than those from multiple linear regression, in both, the groundwater level and the landslide velocity models, proofing that multiple linear regression has a potential for prediction of landslide movement. [ABSTRACT FROM AUTHOR]

Landslides in the Podsljeme area of the City of Zagreb cause significant economic losses, which have been increasing over the last several decades due to the urbanisation of hilly areas and the influence of climate changes. An airborne LiDAR digital terrain model (DTM) with a spatial resolution of 30 × 30 cm was used to prepare a landslide inventory map of the pilot area (21 km2) with more than 700 identified landslides. The area of the smallest identified landslide is 43 m2, while 90% of the landslides are between 100 and 2,000 m2. The frequency–size distribution of all mapped landslides in the pilot area shows a very high level of landslide inventory completeness. Therefore, it is concluded that the LiDAR-based terrain model is a valuable tool for the preparation of detailed landslide inventories in heavily vegetated regions such as the hilly area of Medvednica Mt. [ABSTRACT FROM AUTHOR]

Copyright of Rudarsko-Geolosko-Naftni Zbornik is the property of Faculty of Mining, Geology & Petroleum Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Two neighboring countries in the southeastern region of Europe, Croatia, and Bosnia and Herzegovina (BIH), belong to the same geotectonic units of the Dinarides and to the Pannonian Basin, which influence relief types, lithology, and types of slope movements, i.e., landslides. The Dinarides are a mountain chain with a northwest-southeast direction that span from Slovenia through Croatia, BIH and Montenegro to Albania. The Pannonian Basin is situated within the Alpine, Carpathian, and Dinaric mountain belts at the boundary between Central and Southeastern Europe. The paper describes the general geological and geomorphological conditions in the Dinarides and the European Pannonian Basin in Croatia and BIH that are preparatory causal factors for landslides in the following environments: the hills of the Istrian Peninsula and Rječina River Valley; hills, low- and mid-altitude mountains in the Dinarides; and lowlands and hills in the Pannonian Basin. Landslide types, dimensions, and activities in the described areas are related to natural conditions primarily influenced by tectonic evolution and by recent anthropogenic processes, e.g., urbanization. More detailed descriptions are provided for the following selected phenomena, which are interesting because of the associated damage and potential risk: translational block landslide Brus and erosion phenomena on the Istrian Penninsula; relict and historical large, deep-seated landslides in the Rječina River Valley, including the recently reactivated Grohovo and Valići Landslides; catastrophic landslides triggered by precipitation during Cyclone Tamara in BIH (2014) (the Mačkovac-Šerići Landslide, Mjestova Ravan Landslide, Kosova Landslide, and Lukavica Landslide); and a large, deep-seated landslide in urban area of Zagreb, the Kostanjek Landslide. Recent rainfall triggering conditions of landslides in Croatia (2013) and BIH (2014) are also specified to emphasize the landslide risk and necessity of landslide risk management. The conclusions of the paper also note historical and potential damage due to landslide reactivations together with the spatial distribution of landslide-prone areas, which requires landslide mapping in the form of landslide inventory, susceptibility, hazard, and risk maps. [ABSTRACT FROM AUTHOR]

This paper describes the application of advanced technologies for landslide detection, mapping and monitoring in the City of Zagreb since 2010. Airborne Light Detection and Ranging (LiDAR) data are used to visually identify landslides in the Podsljeme Zone over an area of 180 km² for the purpose of landslide inventory mapping. The total number of landslides in the analyzed area (approx. 1,600 landslides) is estimated based on the average landslide density (9 landslides per square km) from the most reliable geomorphological historical inventory produced in 1979. Examples of the visual interpretation of very-high-resolution bare-earth DEMs (Digital Elevation Model) are given to show the potential of these advanced techniques to identify the landslide contours of the very small, small and moderately small landslides that are characteristic of the study area, which is composed of engineering soils and/or soft rocks (marls). In the framework of the described landslide research, the biggest landslide in the Podsljeme Zone, the Kostanjek landslide (landslide area of 1 km²), was also equipped with an automated near-real time monitoring system that encompassed approximately 40 sensors to monitor landslide movement and landslide causal factors. Monitoring of this deep and large landslide during multiple extreme hydro- meteorological events from January 2013 to January 2015 enabled collection of data for the analysis of landslide movement both on and below the surface as well as analysis of the relationship between landslide reactivations and their triggers, i.e., changes in groundwater level and precipitation. The final aim of the described scientific research in terms of landslide mapping and monitoring is its practical application in land-use planning and civil protection systems in the City of Zagreb. Namely, the conclusions propose reliable and efficient methods for landslide identification and monitoring, which are necessary to provide data and solutions for hazard and risk reduction to local authorities that are managed by the City. [ABSTRACT FROM AUTHOR]

The first part of this paper is an overview of various methodologies for seismic-geotechnical hazard zonation that conform to the recommendations of International Society for Soil Mechanics and Geotechnical Engineering (ISSMGE, 1999). The purpose of this review is to illustrate the variety of methodologies currently in use for preparation of seismic hazard maps and to evaluate basic principles of zonation for different purposes and at different scales. In the second part of the paper, input data for seismic microzonation are discussed. Promulgated seismic regulations are a prerequisite for delineation of seismic hazard zones. Guidelines and recommendations for seismic microzonation should be incorporated into seismic regulations. There are two principal approaches to earthquake loss mitigation; one relates to land use management, and the other deals with the design and construction of individual buildings. Both approaches must be considered as components of urban planning and building design, and the application and use of these approaches should be required and enforced by municipal authorities. [ABSTRACT FROM AUTHOR]