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Modern Applied Statistics With S

On robust estimation of the location parameter

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Introduction To Robust Estimation And Hypothesis Testing

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An R And S Plus Companion To Applied Regression

Agricultural landscapes cultivated in hilly and mountainous areas, often with terracing practice, could represent for some regions historical heritages and cultural ecosystem services. For this reason, they deserve to be protected. The complex morphology that characterises them, however, makes these areas intrinsically susceptible to hydrogeological instability, such as soil loss due to surface erosion or more severe mass movements. We can identify three major critical factors for such landscapes. The first is related to the socio-economic evolution of contemporary civilization, that increased the land abandonment of several rural regions, leading therefore to a lack of maintenance. A second element is the unsustainable agricultural practices, such as excessive heavy-mechanization that cause soil compaction thus accelerating degradation. Finally, the climate change forcing, with the increasing of the extreme rainfall. In this complex framework, it is necessary to find innovative solutions for the mitigation of hydrogeological risk and to respond in a well-prepared way to the possible future critical scenarios. Therefore, the use of sustainable agricultural practices, which allow the production of quality agricultural products in perfect harmony with the surrounding environment, becomes crucial. Suitable solutions must respond to the criterion of multidisciplinary, where the various stakeholders collaborate by offering their specific knowledge in a shared intention of problem-solving. The discipline of geography may become a valuable asset in this framework. In particular, thanks to the recent technological advances in the topographic survey (e.g. innovative remote sensing techniques such as drones and airborne laser scanning), it is possible to exploit digital terrain analysis to synthesize key information for decision-makers, in order to plan sustainable interventions. Moreover, thanks to the high-resolution and accuracy offered by digital topography and the advanced morphometric algorithms, it is possible to tackle the problem of hydrogeological risk from a unique and privileged perspective: that of prevention.

Agriculture in Hilly and Mountainous Landscapes: Threats, Monitoring and Sustainable Management

Point cloud filtering on UAV based point cloud

An approach for the automated extraction of road surface distress from a UAV-derived point cloud

Landslide susceptibility maps provide crucial information that helps local authorities, public institutions, and land-use planners make the correct decisions when they are managing landslide-prone areas. In recent years, machine-learning techniques have become very popular for producing landslide susceptibility maps. This study aims to compare the performance of these machine learning models with the traditional statistical methods used to produce landslide susceptibility maps. The landslide susceptibility for Ardanuc, Turkey was evaluated using three models: logistic regression (LR), support vector machine (SVM), and random forest (RF). Ten parameters that are effective in landslide occurrence are used in this study. The accuracy and prediction capabilities of the models were assessed using both the receiver operating characteristic (ROC) curve and area under the curve (AUC) methods. According to the AUC method, the success rate of the LR, SVM, and RF models was 83.1%, 93.2%, and 98.3%, respectively. Further, the prediction rates were calculated as 82.9% (LR), 92.8% (SVM), and 97.7% (RF). According to the verification results, RF and SVM models outperformed the traditional LR model in terms of success and prediction rate. The RF model, however, performed better than the SVM model in terms of success and prediction rates. The landslide susceptibility maps produced as a result of this study can guide city planners, local administrators, and public institutions related to disaster management to prevent and reduce landslide hazards.

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

Inexpensive and small unmanned aerial vehicles (UAVs) provide high-accuracy positional data and enable users to collect high-resolution aerial images. The analysis of images captured using UAVs in a specific reference system is traditionally accomplished using the georeferencing method with high-accuracy ground control points (GCPs). This study aims to test and compare the benchmarks and point clouds’ positional accuracy produced on three consecutive days with different flight combinations at 75 and 100 m flight altitude by means of network-based continuously operating reference stations and differential-based real-time kinematic georeferencing systems without using GCPs. A root mean squared error values were obtained 1–3 cm for the horizontal accuracy and 4–6 cm for the vertical accuracy values. Thus, the proposed system proved an acceptable positional accuracy level. According to the results of the proposed approach, it can be said that the evaluation and use of UAV data without using GCPs is within an adequate range for various mapping purposes.

Accuracy assessment of direct georeferencing UAV images with onboard global navigation satellite system and comparison of CORS/RTK surveying methods

The objective of this study was to evaluate and predict land movement by integrating geodetic, geophysical and meteorological data in a landslide area. Specifically, electrical resistivity tomography surveying, Global Navigation Satellite System and terrestrial laser scanning techniques were integrated to monitor a landslide. The study area lies to the southeast of the town of Taskent in southern Turkey, close to Balcilar in the Central Taurus mountain chain. Landslides result in considerable damage to structures, farmland and the environment in this area; therefore, it is important to characterise the size, extent and timing of past land movements in order to mitigate damage from future landslides. Analysis presented in this paper shows that the greatest land movements in the region occur in spring, when average motions can be up to 1.5 m per month. It is demonstrated that integrated techniques provide a better means for monitoring landslide processes and gathering data for predictions of future movements. Mapping landslide movements by integrating geophysical and geodetic observations can provide a meaningful evaluation of a landslide and its dynamics.

Mustafa Zeybek

Papers

Point cloud filtering on UAV based point cloud

An approach for the automated extraction of road surface distress from a UAV-derived point cloud

Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin), Turkey

Accuracy assessment of direct georeferencing UAV images with onboard global navigation satellite system and comparison of CORS/RTK surveying methods

Monitoring landslides with geophysical and geodetic observations