The success rate in landslide susceptibility mapping efforts increased with the advancements in machine learning algorithms and the availability of geospatial data with high spatial and temporal resolutions. Existing data-driven susceptibility mapping models are not globally applicable due to the high variability of landslide conditioning parameters and the limitations in the availability of up-to-date and accurate data. Among numerous applications, landslide susceptibility maps are essential for site selection and health monitoring of engineering structures, such as dams, for increasing their lifetime and to prevent from disastrous events caused by the damages. In this study, landslide susceptibility mapping performance of XGBoost algorithm was evaluated in a landslide-prone area in the upper basin of Ataturk Dam, which is a prime investment located in the southeast of Turkey. The study area has a size of 2718.7 km2 with an elevation difference of ca. 2000 m and contains 27 lithological units. EU-DEM v1.1 from the Copernicus Programme was used to derive the geomorphological features. High classification accuracy with area under curve value of 0.96 could be obtained from the XGBoost algorithm. According to the results, the main factors controlling the landslides in the study area are the lithology, altitude and topographic wetness index.

Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers.

Amaç: Bu çalışma, hemşirelerde profesyonellik davranışları ve tükenmişlik düzeylerini belirlemek amacıyla yapılmıştır. Gereç ve Yöntem: Bu çalışma tanımlayıcı tiptedir. Araştırmanın evrenini bir ilçedeki kamu sağlık kuruluşlarında görev yapan hemşireler oluşturmuştur. Örnekleme yöntemi olarak amaçlı örneklem yapılmıştır. Araştırmada veri toplama aracı olarak üç bölümden oluşan anket formu kullanılmıştır. Anket formunun birinci bölümünde kişisel bilgi formu, ikinci bölümünde Profesyonel Davranış Değerlendirme Envanteri (HPDE), üçüncü bölümünde Maslach Tükenmişlik Ölçeği yer almaktadır. Bulgular: Hemşirelerin %36’sının klinik servislerde çalıştığı, %59,6’sının 36-45 yaş aralığında ve %78,7’sinin kadın olduğu belirlenmiştir. Hemşirelerin profesyonellik davranışlarından toplumsal hizmet sunma ve yeterlilik ve sürekli eğitim boyutları ile duygusal tükenmişlik arasında negatif yönlü, düşük düzeyde anlamlı ilişki olduğu tespit edilmiştir. Regresyon katsayılarının anlamlılığına göre profesyonelliğin alt boyutlarından olan yeterlilik ve sürekli eğitim boyutunun duygusal tükenmişlik üzerinde negatif yönlü anlamlı etkiye sahip olduğu belirlenmiştir. Sonuç: Hemşirelerin profesyonellik davranışları düşük ve tükenmişlik düzeyi ise yüksek olduğu saptanmıştır. Bu sonuçlar doğrultusunda eğitim ve uygulamaların yapılması önerilmektedir.  

Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.

Abstract. Geospatial data are fundamental to understand the relationship between the geographical events and the Earth dynamics. Although the geospatial technologies aid geodata collection, the increasing possibilities yield new application areas and cause even a greater demand. Considering the increment in data quantity and diversity, to be able to work with the data, they must be collected, stored, analysed and presented with the help of specifically designed platforms. Geographical Information Systems (GIS) with mobile and web support are the most suitable platforms for these purposes. On the other hand, the location-enabled mobile, web and geospatial technologies empowered the rise of the citizen science (CitSci) projects. With the CitSci, mobile GIS platforms enable the data to be collected from almost any location. As the size of the collected data increases, considering automatic control of the data quality has become a necessity. Integrating artificial intelligence (AI) with the CitSci based GIS designs allows automatic quality control of the data and helps eliminating data validation problem in CitSci. For this reason, the purpose of the present study is to develop a CitSci and AI supported GIS platform for landslide data collection because landslide hazard mitigation efforts require landslide susceptibility, hazard and risk assessments. Especially, landslide hazard assessments are necessary the time of occurrence of a landslide. Although this information is crucial, it is almost impossible to collect time of occurrence in regional hazard assessment efforts. Consequently, use of CitSci for this purpose may provide valuable information for landslide hazard assessments.