15 research outputs found

    Formation and Usage of Landslide Digital Databases: Examples from Various Countries and Croatia - Availability of Landslide Data in the Rijeka Area

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    U radu se daje pregled formiranja digitalnih baza podataka o klizištima u svijetu i stanja u Hrvatskoj. Kao primjer izabrane su države iznimno ugrožene padinskim procesima i/ili one koje imaju razrađene i dostupne baze klizišta (npr. Japan, Kina i Novi Zeland). Prikazuju se parametri od kojih su pojedine baze načinjene (Italija, Slovenija i Hrvatska) te daje prikaz preporučenih kategorija pri izradi inventara i baza klizišta na svjetskoj razini. Također se iznosi pregled osnovne terminologije, iznimno važne u istraživanju i evaluaciji podložnosti padina klizanju, hazarda i rizika. Dostupnost podataka u Hrvatskoj razmotrena je na primjeru riječkog područja.This paper reviews development of digital landslide databases in various countries and in Croatia. Countries that were chosen are those endangered by slope processes and/or those that have landslide databases established and available for review (e.g. Japan, China, New Zealand). The paper shows parameters used in the databases (in Italy, Slovenia and Croatia) and categories recommended for landslide inventory and database development at the global level. Terminology important in research and evaluation of landslide susceptibility, hazard, and risk is also reviewed. The availability of landslide data in Croatia is shown using the example of Rijeka area

    PEMANFAATAN CITRA ALOS AVNIR-2 UNTUK ANALISIS TINGKAT KERAWANAN LONGSOR LAHAN (STUDI KASUS DI KECAMATAN SAMIGALUH DAERAH PERBUKITAN MENOREH KABUPATEN KULON PROGO)

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    Penelitian ini bertujuan untuk mengkaji kemampuan citra ALOS AVNIR-2 untuk memperoleh parameter lahan yang digunakan untuk menentukan longsorlahan, menyusun dan menentukan zonasi tingkat kerawanan longsor di kecamatan Samigaluh, mengkaji tingkat kerawanan longsorlahan di kecamatan Samigaluh.Dari penelitian ini dihasilkan a) Citra ALOS AVNIR-2 yang diintegrasikan dengan Sistem Informasi Geografis mampu memperoleh parameter fisik lahan untuk kerawanan longsor masing-masing memiliki akurasi untuk kerapatan vegetasi sebesar 83,33 % dan penggunaan lahan sebesar 86,4 %, dan b) 52 Kelas tingkat kerawanan longsor yang ditentukan dalam penelitian ini yaitu sebanyak 3 kelas tingkat kerawanan longsor yaitu tingkat kerawanan longsor rendah, sedang, dan tinggi dengan luas kerawanan longsor tinggi memiliki luas 2413.29ha (35.80%), tingkat kerawanan longsor sedang memiliki luas 3562.45ha (52.85%), dan tingkat kerawanan rendah memiliki luas 764.79ha (11.35%) dan c). Zonasi tingkat kerawanan longsorlahan yang diperoleh kemudian digunakan untuk mengkaji dan menganlisis tingkat kerawanan longsorlahan

    Mantıksal Regresyon, Frekans Oranı ve ArcGIS Pro Suitability Model ile Yalova-Çınarcık Bölgesinin Heyelan Duyarlılık Analizi

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    Bu çalışmada Türkiye’nin batısında yer alan Yalova-Çınarcık bölgesinde, yaklaşık 11 km2 olan inceleme alanında heyelan duyarlılık analizi yapılmıştır. Bölgede geniş yayılımlı 20 heyelan bölgesi incelenmiştir. Mantıksal Regresyon, Frekans Oranı ve ArcGIS Pro Uygunluk Modeli ile heyelan duyarlılık analizi yapılmıştır. Verilerin %80’i analiz, %20’si test için kullanılmıştır. Analizin doğruluğunu test etmek için Alıcı İşletim Karakteristik (Receiver Operating Charasteristic-ROC) eğrisi ve Eğrinin Altında Kalan Alan (Area Under the Curve-AUC) kullanılmıştır. Yapılan analizler sonucunda Mantıksal Regresyon yöntemine göre inceleme alanının %5.41’i çok düşük, %10.32’si düşük, %31,22’si orta, %24,98’i yüksek ve %28,05’i çok yüksek heyelan duyarlılık sınıfındadır ve AUC sonucu ise %78,8’dir. Frekans Oranı için ise inceleme alanının %30,8’i çok düşük, %14,48’i düşük, %4,5’i orta, %24,02’si yüksek ve %26,2’si çok yüksek heyelan duyarlılık sınıfında olup AUC sonucu %64,4’tür. ArcGIS Pro Uygunluk Modeli için ise %4,42’si çok düşük, %20,57’si düşük, %27,9’u orta, %23,42’si yüksek ve %23,67’si çok yüksek ve AUC değeri %69,7’dir. Bu çalışmada elde edilen duyarlılık analizi verileri ile yüksek ve çok yüksek duyarlılık sınıfındaki bölgeler için gerekli önlemlerin ve bölgede yapılacak arazi planlaması için bu sonuçların dikkate alınması önerilmiştir

    LANDSLIDE SUSCEPTIBILITY ZONATION MODEL ON JENEBERANG WATERSHED BASED ON GEOGRAPHICAL INFORMATION SYSTEM AND ANALYTICAL HIERARCHY PROCESS

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    The area of Jeneberang Watershed is prone to landslides due to geologic, geomorphologic and rainfall characteristics of the region. In the year 2004 the huge caldera wall collapsed in the Eastern part of the watershed area resulting in infrastructure damage, human casualties and sequencely disaster as debries flow. Potential landslides still occur in the future. It is necessary to conduct research to prepare a landslide susceptibility map of the region. The objectives of this study are as follows: firstly, to investigate the contributing parameters induceed landsliding in the Jeneberang watershed and secondly, to construct landslide susceptibility zonation map. In this study, the analytical hierarchy process (AHP) based on Geogeographical Information System (GIS) methods were used to produce map of landslide susceptibility. Data layers such as elevation, slope aspect, slope steepnes, proximity to road, proximity to river, lithology, lineaments, soil texture, rainfall, landuse or land cover and landslide inventory were extracted from various sources and used to calculate index of landslide susceptibility. The study area was classified into five hazard classification namely: very low, low moderate, high and very high. The presentage distribution of landslide degrees was calculated. It was found that about 28.40% of the study area is classified as very and high susceptibility. The results of this study is useful for preparing landslide mitigation efforts through a comprehensive planning schem

    SLOPE-2015 - Abdul Hakam

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    Predicting Landslides in Costa Rica Using Self-Organizing Map Machine Learning

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    Landslides are natural hazards commonly understated in both number of occurrences and cost of economic impacts. The Costa Rican terrain is predominately geologically young and therefore, severely impacted by landslides. It has limited resources and infrastructure and with a large portion of the population being poor, this causes communities to build in hazardous locations and infrastructure that can be easily crippled by landslides. Being able to identify where and when landslides are going to occur is key to mitigating the effects, either by stabilizing the slope or by evacuating communities. Machine learning is one method that has been increasingly used to monitor and predict landslides in recent times. These methods do not have the shortcomings of traditional analytical methods and can be easily adapted for different locations, changing or missing data, and number of factors studied. This research proposes that Self Organizing Maps (SOM) can be used as a versatile and effective method for landslide prediction. The results of this study have shown how SOM can be used for multi scale susceptibility analysis and for prediction with use of precipitation data, by producing significant results identifying high risk areas with a varying number and combination of variables. It has also shown that when precipitation data is used, it can identify high risk locations based on precipitation amounts and static variables (slope, TWI, curvature, NDVI, etc.). At the five-time scales tested, four of the tests produced correlations between increased precipitation and higher landslides risk (6 hour r2 = 0.38, 12 hour r2 = 0.36, 1 day r2 = 0.24, 1 month r2 = 0.33). This study has shown the versatility and effectiveness of SOM by producing significant results, as well as being able to use current weather conditions to produce landslide prediction analysis

    Modelling for the automatic assessment of rainfall triggered landslide susceptibility due to changes in groundwater level and soil water content.

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    Risk assessment of rain-triggered landslides over large areas is quite challenging due to the complexity of the phenomenon. In fact, rainfall represents one of the most important triggering factors for landslides performing an erosive action at ground level, and, through deep infiltration, increasing the soil saturation degree and feeding the groundwater table leading to fluctuations that can affect the slope stability. These phenomena represent an open challenge for technicians and authorities involved in landslide risk management and mitigation. For this reason, it is necessary to develop appropriate models for the landslides susceptibility assessment that are operationally compatible with good resolution and computational speed. Standard methods of 3D slope stability analysis are generally applied over limited areas or at low resolution. In this dissertation, two automatic procedures are proposed for estimating landslide susceptibility induced by changes in (i) groundwater levels and (ii) soil saturation conditions. A physically based Integrated Hydrological and Geotechnical (IHG) model was implemented in GIS environment to effectively analyse areas of a few square kilometres, typically at a scale of 1:5.000. Referring to each volume element in which the whole mass under study is discretized, a simplified hydrological soil-water balance and geotechnical modelling are applied in order to assess the debris and earth slide susceptibility in occasion of measured or forecasted rainfalls. The IHG procedure allows 3D modelling of landslide areas, both morphologically and with regard to geotechnical/hydrological parameters thanks to the spatialisation of input data from in situ measurements, and renders easy-to-understand results. Critical issues inherent the discretization of quite large areas, referred to soil characterization, interpolation/extrapolation of in situ measurements, spatial resolution and computational effort, are here discussed. Considering rain-triggered shallow landslides, the stability can be markedly influenced by the propagation of the saturation front inside the unsaturated zone. Soil shear strength varies in the vadose zone depending on the type of soil and the variations of soil moisture. Monitoring of the unsaturated zone can be done by measuring volumetric water content using low-cost instrumentation (i.e. capacitive sensors) that are easy to manage and provide data in near-real time. For a proper soil moisture assessment a laboratory soil-specific calibration of the sensors is recommended. Knowing the soil water content, the suction parameter can be estimated by a Water Retention Curve (WRC), and consequently the soil shear strength in unsaturated conditions is evaluated. The automatic procedure developed in GIS environment, named assessment of Soil Apparent Cohesion (SAC), here described, allows the estimate of the soil shear strength starting from soil moisture monitoring data (from sensor networks or satellite-derived map). SAC results can be integrated into existing models for landslide susceptibility assessment and also for the emergency management. Some significant results concerning the automatic IHG and SAC procedures, implemented in Python, applied to landslides within the Alcotra AD-VITAM project are here presented

    Landslide classification, characterization and susceptibility modeling in KwaZulu-Natal

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    In eastern South Africa landslides are widespread owing to the dramatic topographic-, climatic-, geological- and geomorphological-gradients across the region. In the KwaZulu-Natal (KZN) province numerous landslides and associated deposits are geohazards that represent threats to development and strategic infrastructure. The regional landslide inventory and susceptibility mapping project, following international classification systems and modeling techniques, has revealed the widespread occurrence of landslides. Landslide types mapped include; falls, topples, flows, translational and rotational slides. The bivariate statistical landslide susceptibility modeling method and Analytical Hierarchy Process (AHP) was used to evaluate landslide susceptibility, using a Geographic Information System (GIS). The huge size of some palaeo-landslides mapped is a revelation in the context of KwaZulu-Natal where recent landslide events are mainly small features triggered by intense rainfall events affecting embankments and steep hillslopes. Radiocarbon dating of organic material derived from sag ponds yielded minimum ages for the large middle to late Holocene landslide events
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