Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh

Landslides are a common hazard in the highly urbanized hilly areas in Chittagong Metropolitan Area (CMA), Bangladesh. The main cause of the landslides is torrential rain in short period of time. This area experiences several landslides each year, resulting in casualties, property damage, and economic loss. Therefore, the primary objective of this research is to produce the Landslide Susceptibility Maps for CMA so that appropriate landslide disaster risk reduction strategies can be developed. In this research, three different Geographic Information System-based Multi-Criteria Decision Analysis methods—the Artificial Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered Weighted Average (OWA)—were applied to scientifically assess the landslide susceptible areas in CMA. Nine different thematic layers or landslide causative factors were considered. Then, seven different landslide susceptible scenarios were generated based on the three weighted overlay techniques. Later, the performances of the methods were validated using the area under the relative operating characteristic curves. The accuracies of the landslide susceptibility maps produced by the AHP, WLC_1, WLC_2, WLC_3, OWA_1, OWA_2, and OWA_3 methods were found as 89.80, 83.90, 91.10, 88.50, 90.40, 95.10, and 87.10 %, respectively. The verification results showed satisfactory agreement between the susceptibility maps produced and the existing data on the 20 historical landslide locations

Multi-hazard risk assessment using GIS in urban areas: a case study for the city of Turrialba, Costa Rica

In the framework of the UNESCO sponsored project on “Capacity Building for Natural Disaster Reduction” a case study was carried out on multi-hazard risk assessment of the city of Turrialba, located in the central part of Costa Rica. The city with a population of 33,000 people is located in an area, which is regularly affected by flooding, landslides and earthquakes. In order to assist the local emergency commission and the municipality, a pilot study was carried out in the development of a GIS –based system for risk assessment and management. The work was made using an orthophoto as basis, on which all buildings, land parcels and roads, within the city and its direct surroundings were digitized, resulting in a digital parcel map, for which a number of hazard and vulnerability attributes were collected in the field. Based on historical information a GIS database was generated, which was used to generate flood depth maps for different return periods. For determining the seismic hazard a modified version of the Radius approach was used and the landslide hazard was determined based on the historical landslide inventory and a number of factor maps, using a statistical approach. The cadastral database of the city was used, in combination with the various hazard maps for different return periods to generate vulnerability maps for the city. In order to determine cost of the elements at risk, differentiation was made between the costs of the constructions and the costs of the contents of the buildings. The cost maps were combined with the vulnerability maps and the hazard maps per hazard type for the different return periods, in order to obtain graphs of probability versus potential damage. The resulting database can be a tool for local authorities to determine the effect of certain mitigation measures, for which a cost-benefit analysis can be carried out. The database also serves as an important tool in the disaster preparedness phase of disaster management at the municipal level

Statistical and spatial analysis of landslide susceptibility maps with different classification systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s12665-016-6124-1A landslide susceptibility map is an essential tool for land-use spatial planning and management in mountain areas. However, a classification system used for readability determines the final appearance of the map and may therefore influence the decision-making tasks adopted. The present paper addresses the spatial comparison and the accuracy assessment of some well-known classification methods applied to a susceptibility map that was based on a discriminant statistical model in an area in the Eastern Pyrenees. A number of statistical approaches (Spearman’s correlation, kappa index, factorial and cluster analyses and landslide density index) for map comparison were performed to quantify the information provided by the usual image analysis. The results showed the reliability and consistency of the kappa index against Spearman’s correlation as accuracy measures to assess the spatial agreement between maps. Inferential tests between unweighted and linear weighted kappa results showed that all the maps were more reliable in classifying areas of highest susceptibility and less reliable in classifying areas of low to moderate susceptibility. The spatial variability detected and quantified by factorial and cluster analyses showed that the maps classified by quantile and natural break methods were the closest whereas those classified by landslide percentage and equal interval methods displayed the greatest differences. The difference image analysis showed that the five classified maps only matched 9 % of the area. This area corresponded to the steeper slopes and the steeper watershed angle with forestless and sunny slopes at low altitudes. This means that the five maps coincide in identifying and classifying the most dangerous areas. The equal interval map overestimated the susceptibility of the study area, and the landslide percentage map was considered to be a very optimistic model. The spatial pattern of the quantile and natural break maps was very similar, but the latter was more consistent and predicted potential landslides more efficiently and reliably in the study area.Peer ReviewedPreprin

Towards the optimal Pixel size of dem for automatic mapping of landslide areas

Determining appropriate spatial resolution of digital elevation model (DEM) is a key step for effective landslide analysis based on remote sensing data. Several studies demonstrated that choosing the finest DEM resolution is not always the best solution. Various DEM resolutions can be applicable for diverse landslide applications. Thus, this study aims to assess the influence of special resolution on automatic landslide mapping. Pixel-based approach using parametric and non-parametric classification methods, namely feed forward neural network (FFNN) and maximum likelihood classification (ML), were applied in this study. Additionally, this allowed to determine the impact of used classification method for selection of DEM resolution. Landslide affected areas were mapped based on four DEMs generated at 1m, 2m, 5m and 10m spatial resolution from airborne laser scanning (ALS) data. The performance of the landslide mapping was then evaluated by applying landslide inventory map and computation of confusion matrix. The results of this study suggests that the finest scale of DEM is not always the best fit, however working at 1m DEM resolution on micro-topography scale, can show different results. The best performance was found at 5m DEM-resolution for FFNN and 1m DEM resolution for results. The best performance was found to be using 5m DEM-resolution for FFNN and 1m DEM resolution for ML classification

Predicting Landslides Using Locally Aligned Convolutional Neural Networks

Landslides, movement of soil and rock under the influence of gravity, are common phenomena that cause significant human and economic losses every year. Experts use heterogeneous features such as slope, elevation, land cover, lithology, rock age, and rock family to predict landslides. To work with such features, we adapted convolutional neural networks to consider relative spatial information for the prediction task. Traditional filters in these networks either have a fixed orientation or are rotationally invariant. Intuitively, the filters should orient uphill, but there is not enough data to learn the concept of uphill; instead, it can be provided as prior knowledge. We propose a model called Locally Aligned Convolutional Neural Network, LACNN, that follows the ground surface at multiple scales to predict possible landslide occurrence for a single point. To validate our method, we created a standardized dataset of georeferenced images consisting of the heterogeneous features as inputs, and compared our method to several baselines, including linear regression, a neural network, and a convolutional network, using log-likelihood error and Receiver Operating Characteristic curves on the test set. Our model achieves 2-7% improvement in terms of accuracy and 2-15% boost in terms of log likelihood compared to the other proposed baselines.Comment: Published in IJCAI 202

Visualizing Landslide Hazards: Methods for Empowering Communities in Guatemala Through Hazard Mapping

Landslides occur at a high frequency throughout the mountainous regions of Guatemala, posing an elevated risk to communities and their infrastructure. A crucial component of the analysis of landslide hazards incorporates the creation of landslide hazard or susceptibility maps. This paper\u27s research objective had two distinct components. The first was to identify practical and effective cartographic visualization methods to deliver map-based hazard information at the community level in Guatemala. Mapping methods were evaluated for their potential effectiveness in visually communicating landslide risks to the isolated rural communities of Lake Atitlan and the town of Santiago Atitlan. The research illustrated the importance of the depiction of relief, imagery, and landmarks in addition to local knowledge of the construction of hazard maps. The second component analyzed the suitability of SRTM 90-meter resolution DEMs for landslide susceptibility mapping. A SRTM 90-meter resolution DEM of the Sierra de las Minas, Guatemala and corresponding USGS landslide inventories were examined in the ArcMap 10 environment. Spatial analysis revealed that although lower resolution did limit the SRTM DEM\u27s suitability for comprehensive landslide hazard analysis in Guatemala, a potential existed for it to be a useful aid in identifying areas susceptible to large debris flow

Geomorphological mapping and geophysical profiling for the evaluation of natural hazards in an alpine catchment

International audienceLiechtenstein has faced an increasing number of natural hazards over recent decades: debris flows, slides, snow avalanches and floods repeatedly endanger the local infrastructure. Geomorphological field mapping and geo-electrical profiling was used to assess hazards near Malbun, a village potentially endangered by landslides, and especially debris flows. The area is located on the tectonic contacts of four different nappe slices. The bedrock consists of anhydrite and gypsum, dolomite, shale, marl, and limestone. The spatial distribution and occurrence of debris flows and slides is evaluated through a combination of geomorphological expert knowledge, and detailed visualization in a geographical information system. In a geo-database a symbol-based 1:3000 scale geomorphological map has been digitized and rectified into polygons. The polygons include information on the main geomorphological environment, the Quaternary material distribution and of geomorphological processes, which are stored in attribute tables. The spatial distribution of these attributes is then combined with geophysical information and displacement rates interpolated from benchmark measurements. On one of the landslides two geo-electrical profiles show that the distance to a potential failure plane varies between 10-20 m and that the topography of the failure plane is influenced by subterranean gypsum karst features. The displacement measurements show that this landslide actively disintegrates into minor slides and is not, therefore, a risk to the village of Malbun. The hazard zonation indicates that debris flows can pose a risk if no countermeasures are taken. Gypsum karst may locally accelerate the landslide activity. In contrast, the impact of debris flows is diminished because collapse dolines may act as sediment traps for the debris flow materials. This research illustrates how geomorphological expert knowledge can be integrated in a GIS for the evaluation of natural hazards on a detailed scale

Integrating expert knowledge with statistical analysis for landslide susceptibility assessment at regional scale

Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale