Analysis of landslide susceptibility of Van province using frequency ratio method

Bu çalışmada, Van ili heyelan duyarlılığı Coğrafi Bilgi Sistemleri ortamında Frekans Oranı yöntemi kullanılarak belirlenmiştir. Heyelan duyarlılık analizinde; litoloji, fay hatlarına uzaklık, arazi kullanımı/örtüsü, yükseklik, eğim, bakı ve genel eğrilik faktörleri değerlendirmeye alınmıştır. Heyelan envanterinin %70’i eğitim verisi, %30’u doğrulama verisi olarak kullanılmıştır. Heyelan duyarlılık sonuçlarından kategorik heyelan duyarlılık haritasının oluşturulmasında Eşit Aralıklı, Doğal Aralıklı, Geometrik Aralıklı ve Kuantil Sınıflandırma teknikleri kullanılmış ve heyelan duyarlılığı Çok Yüksek, Yüksek, Orta, Düşük ve Çok Düşük olmak üzere beş sınıfa kategorilendirilmiştir. ROC (İşlem Karakteristik Eğrisi) analizi ve SCAI (Doğrulama Pikseli Alan İndeksi) indeksi ile heyelan duyarlılık haritalarının doğruluk değerlendirmesi gerçekleştirilmiş ve Doğal Aralıklı Sınıflandırma yönteminin daha iyi sonuç verdiği tespit edilmiştir. Doğal Aralıklı Sınıflandırma yöntemi sonucuna göre ilin %17,2’si Çok Yüksek, %27,5’i Yüksek, %27,7’si Orta, %20,0’ı Düşük ve %7,6’sı Çok Düşük heyelan duyarlılığı göstermektedir. Heyelan duyarlılık haritasının arazi kullanımı/örtüsü katmanı ile çakıştırılması sonucunda ilde yerleşim ve endüstriyel alanların 0,2 km2 ’sinin Çok Yüksek, 3,6 km2 ’sinin Yüksek heyelan duyarlılığında olduğu belirlenmiştir. Sonuç olarak, Frekans Oranı yöntemiyle elde edilen analiz sonuçlarından farklı sınıflandırma teknikleri ile optimum kategorik heyelan haritasının elde edilebileceği ve gelecekteki muhtemel heyelanlar için tehlike altında bulunan alanların öngörüsünde kullanılarak afet yönetimi ve planlama çalışmalarına entegre edilebileceği görülmüştür.In this study, the landslide susceptibility of Van province was determined using the Frequency Ratio method in the Geographical Information Systems environment. In the landslide susceptibility analysis; lithology, distance to fault lines, land use/cover, elevation, slope, aspect, and general curvature were taken into consideration. 70% of the landslide inventory was used as training data and 30% as test data. To obtain the categorical landslide susceptibility map from the landslide susceptibility analysis results, classification techniques of Equal Interval, Natural Breaks, Geometric Interval, and Quantile were used and landslide susceptibility was categorized into five classes as Very High, High, Medium, Low, and Very Low. The accuracy of the landslide susceptibility maps was evaluated by ROC (Receiver Operating Characteristic) analysis and SCAI (Seed Cell Area Index) index, and it was determined that the Natural Breaks Classification method gave better results. According to the result of the Natural Breaks Classification method, 17.2% of the province had Very High, 27.5% High, 27.7% Medium, 20.0% Low, and 7.6% Very Low landslide susceptibility. As a result of overlapping the landslide susceptibility map with the land use/cover layer, it was determined that 0.2 km2 of the residential and industrial areas in the province had Very High and 3.6 km2 had High landslide susceptibility. As a result, it has been seen that the optimum categorical landslide map can be selected by different classification techniques from the analysis results obtained by the Frequency Ratio method, and it can be integrated into disaster management and planning studies by using it in the prediction of endangered areas for possible future landslides

Validation of Spatial Prediction Models for Landslide Susceptibility Mapping by Considering Structural Similarity

In this paper, we propose a methodology for validating landslide susceptibility results in the Pinggu district (Beijing, China). A landslide inventory including 169 landslides was prepared, and eight factors correlated to landslides (lithology, tectonic faults, topographic elevation, slope gradient, aspect, slope curvature, land use, and road network) were processed, integrating two techniques, namely the frequency ratio (FR) and the certainty factor (CF), in a geographic information system (GIS) environment. The area under the curve (success rate curve and prediction curve) analysis was used to evaluate model compatibility and predictability. Validation results indicated that the values of the area under the curve for the FR model and the CF model were 0.769 and 0.768, respectively. Considering spatial correlation, an alternative complementary method for validating landslide susceptibility maps was introduced. The spatially approximate maps could be discriminated from their matrices which carry structural information, and the structural similarity index (SSI) was then proposed to quantify the similarity. As a specific example, the SSI value of the FR (74.15%) scored higher than that of the CF model (69.36%), demonstrating its promise in validating different landslide susceptibility maps. These results show that the FR model outperforms the CF model in producing a landslide susceptibility map in the study area

Landslide Geoanalytics Using LiDAR-derived Digital Elevation Models

Landslides are natural hazards that contribute to tremendous economic loss and result in fatalities if there is no well-prepared mitigation and planning. Assessing landslide hazard and optimizing quality to improve susceptibility maps with various contributing factors remain a challenge when working with various geospatial datasets. Also, the system of updating landslide inventories which identify geometry, deformation, and type of landslide with semi-automated computing processes in the Geographic Information System (GIS) can be flawed. This study explores landslide geoanalytics approaches combined with empirical approach and powerful analytics in the Zagros and Alborz Mountains of Iran. Light Detection And Ranging (LiDAR)-derived Digital Elevation Models (DEMs), Unmanned Aerial Vehicle (UAV) images, and Google Earth images are combined with the existing inventory dataset. GIS thematic data in conjunction with field observations are utilized along with geoanalytics approaches to accomplish the results. The purpose of this study is to explore the challenges and techniques of landslide investigations. The study is carried out by studying stream length-gradient (SL) index analysis in order to identify tectonic signatures. A correlation between the stream length-gradient index and the graded Dez River profile with slopes and landslides is investigated. By building on the previous study a quantitative approach for evaluating both spatial and temporal factors contributing to landslides for susceptibility mapping utilizing LiDAR-derived DEMs and the Probability Frequency Ratio (PFR) model is expanded. Furthermore, the purpose of this study is to create an algorithm and a software package in MATLAB for semi-automated geometric analysis to measure and determine the length, width, area, and volume of material displacement and flow direction, as well as the type of landslide. A classification method and taxonomy of landslides are explored in this study. LiDAR-derived DEMs and UAV images help to characterize landslide hazards, revise and update the inventory dataset, and validate the susceptibility model, geometric analysis, and landslide deformation. This study makes the following accomplishments and contributions: 1) Operational use of LiDAR-derived DEMs for landslide hazard assessment is estimated, which is a realistic ambition if we can continue to build on recent achievements; 2) While a steeper gradient could potentially be a signature for landslide identification, this study identifies the geospatial locations of high-gradient indices with potential to landslides; 3) An updated inventory dataset is achieved, this study indicates an improved landslide susceptibility map by implementing the PFR model compared to the existing data and previous studies in the same region. This study shows that the most effective factor is the lithology with 13.7% positive influence; and 4) This study builds a software package in MATLAB that can a) determine the type of landslide, b) calculate the area of a landslide polygon, c) determine and measure the length and width of a landslide, d) calculate the volume of material displacement and determine mass movement (i.e. deformation), and e) identify the flow direction of a landslide material movement. In addition to the contributions listed above, a class taxonomy of landslides is introduced in this study. The relative operating characteristic (ROC) curve method in conjunction with field observations and the inventory dataset are used to validate the accuracy of the PFR model. The validation of the result for susceptibility mapping accuracy is 92.59%. Further, the relative error method is applied to validate the performance of relative percentage of error of the selected landslides computing in the proposed software package. The relative percentage of error of the area, length, width, and volume is 0.16%, 1.67%, 0.30%, and 5.50% respectively, compared to ArcGIS. Marzan Abad and Chalus from Mazandaran Province of Iran and Madaling from Guizhou Province of China are used for validating the proposed algorithm