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

Análise multi-critério aplicada ao mapeamento da suscetibilidade a escorregamentos

This paper presents the application of a multi-criteria analysis (MCA) tool for landslide susceptibility assessment in Porto Alegre municipality, southern Brazil. A knowledge driven approach was used, aiming to ensure an optimal use of the available information. The landslide conditioning factors considered were slope, lithology, fl ow accumulation and distance from lineaments. Standardization of these factors was done through fuzzy membership functions, and evaluation of their relative importance for landslide predisposition was supported by the analytic hierarchy process (AHP), based on local expert knowledge. Finally, factors were integrated in a GIS environment using the weighted linear combination (WLC) method. For validation, an inventory, including 107 landslide points recorded between 2007 and 2013 was used. Results indicated that 8.2% (39.40 km²) of the study area are highly and very highly susceptible to landslides. An overall accuracy of 95% was found, with an area under the receiver operating characteristic (ROC) curve of 0.960. Therefore, the resulting map can be regarded as useful for monitoring landslide-prone areas. Based on the fi ndings, it is concluded that the proposed method is eff ective for susceptibility assessment since it yielded meaningful results and does not require extensive input data.Este estudo apresenta a aplicação de uma ferramenta de análise multi-critério para mapear a suscetibilidade a escorregamentos no município de Porto Alegre, sul do Brasil. Uma abordagem guiada pelo conhecimento de especialistas foi utilizada, com o intuito de otimizar a utilização das informações disponíveis. Os fatores condicionantes dos escorregamentos considerados foram declividade, litologia, acúmulo defl uxo e distância de lineamentos. A padronização desses fatores foi realizada por meio da aplicação de funções fuzzy e a importância relativa de cada um na predisposição do terreno a escorregamentos foi estabelecida com o apoio da técnica AHP (Analytic Hierarchy Process), com base no conhecimento de especialistas locais. Por fi m, a integração dos fatores em ambiente SIG se deu por meio do método denominado Combinação Linear Ponderada (WLC). Para validar os resultados, utilizou-se um mapa inventário contendo 107 cicatrizes de escorregamentos, registradas entre 2007 e 2013. Os resultados indicam que 8,2% (39,38 km²) da área de estudo possui uma suscetibilidade alta e muito alta a escorregamentos. A validação dos resultados obteve uma exatidão geral de 95%, com uma área abaixo da curva ROC (Receiver Operating Characteristic) de 0,960. Portanto, o mapa obtido pode ser considerado útil para monitorar as áreas propensas a esses processos. Com base nos resultados, conclui-se que o método proposto é efi caz para a avaliação da suscetibilidade, uma vez que os resultados obtidos são robustos e que não foi necessária uma quantidade extensa de dados de entrada

Spatially explicit multi-criteria decision analysis for managing vector-borne diseases

The complex epidemiology of vector-borne diseases creates significant challenges in the design and delivery of prevention and control strategies, especially in light of rapid social and environmental changes. Spatial models for predicting disease risk based on environmental factors such as climate and landscape have been developed for a number of important vector-borne diseases. The resulting risk maps have proven value for highlighting areas for targeting public health programs. However, these methods generally only offer technical information on the spatial distribution of disease risk itself, which may be incomplete for making decisions in a complex situation. In prioritizing surveillance and intervention strategies, decision-makers often also need to consider spatially explicit information on other important dimensions, such as the regional specificity of public acceptance, population vulnerability, resource availability, intervention effectiveness, and land use. There is a need for a unified strategy for supporting public health decision making that integrates available data for assessing spatially explicit disease risk, with other criteria, to implement effective prevention and control strategies. Multi-criteria decision analysis (MCDA) is a decision support tool that allows for the consideration of diverse quantitative and qualitative criteria using both data-driven and qualitative indicators for evaluating alternative strategies with transparency and stakeholder participation. Here we propose a MCDA-based approach to the development of geospatial models and spatially explicit decision support tools for the management of vector-borne diseases. We describe the conceptual framework that MCDA offers as well as technical considerations, approaches to implementation and expected outcomes. We conclude that MCDA is a powerful tool that offers tremendous potential for use in public health decision-making in general and vector-borne disease management in particular

Earthquake risk assessment using an integrated Fuzzy Analytic Hierarchy Process with Artificial Neural Networks based on GIS: A case study of Sanandaj in Iran

Earthquakes are natural phenomena, which induce natural hazard that seriously threatens urban areas, despite significant advances in retrofitting urban buildings and enhancing the knowledge and ability of experts in natural disaster control. Iran is one of the most seismically active countries in the world. The purpose of this study was to evaluate and analyze the extent of earthquake vulnerability in relation to demographic, environmental, and physical criteria. An earthquake risk assessment (ERA) map was created by using a Fuzzy-Analytic Hierarchy Process coupled with an Artificial Neural Networks (FAHP-ANN) model generating five vulnerability classes. Combining the application of a FAHP-ANN with a geographic information system (GIS) enabled to assign weights to the layers of the earthquake vulnerability criteria. The model was applied to Sanandaj City in Iran, located in the seismically active Sanandaj-Sirjan zone which is frequently affected by devastating earthquakes. The Multilayer Perceptron (MLP) model was implemented in the IDRISI software and 250 points were validated for grades 0 and 1. The validation process revealed that the proposed model can produce an earthquake probability map with an accuracy of 95%. A comparison of the results attained by using a FAHP, AHP and MLP model shows that the hybrid FAHP-ANN model proved flexible and reliable when generating the ERA map. The FAHP-ANN model accurately identified the highest earthquake vulnerability in densely populated areas with dilapidated building infrastructure. The findings of this study are useful for decision makers with a scientific basis to develop earthquake risk management strategies

Contribution of GIS to the mapping of landslide risk areas in the city of Bafoussam

Landslide risk prevention remains a major global concern. This work allowed the characterization and mapping of landslide risk areas and the evaluation of their accessibility in the city of Bafoussam.The methodological approach was based on the integration in a geographic information system (GIS) of data interpreted from satellite images, pedology, climatic data and the digital elevation model (DEM). On the basis of multi-criteria analysis, the main factors of landslide risk were considered: relief, rainfall, occupation and nature of soils.The multi-criteria spatial analysis carried out in a GIS allowed the elaboration of hazard maps as well as the map of landslide risk areas. This map includes five classes: areas with very low landslide risk (18.36%), areas with low landslide risk (34.33%), areas with moderate landslide risk (26.36%), areas with high landslide risk (16.67%) and areas with very high landslide risk (4.28%). A buffer zone around road traffic routes allowed us to obtain the accessible areas for the last four classes. Thus, we have the following accessibility rates: 10.57% for low landslide risk areas, 11.36% for moderate landslide risk areas, 11.29% for high landslide risk areas and 18.19% for very high landslide risk areas. This rate represents for each area, the percentage of the accessible surface. The results of our work can be used not only for landslide risk prevention but also for potential crisis management

FLOOD SUSCEPTIBILITY MODELLING USING GEOSPATIAL-BASED MULTI-CRITERIA DECISION MAKING IN LARGE SCALE AREAS

Flood is one of the most hazardous natural disasters that cause damages and poses a major threat to human lives and infrastructures worldwide, and its prevention is almost unfeasible. Thus, the detection of flood susceptible areas can be a key to lessen the amount of destruction and mortality. This study aims to implement a framework to identify flood potential zones in an ungauged large-scale area with frequent flood events in recent years. We used two Multi-Criteria Decision Making (MCDM) approaches combined with geospatial analysis, and remote sensing observations for this susceptibility analysis. Nine geomorphological and environmental factors that have an impact on flood behaviour were selected and used for susceptibility modelling. At first, the criteria’s weights were estimated using two MCDM approaches and based on experts’ knowledge. The resultant weights revealed that Flow Accumulation, Topographic wetness index, and Distance to River were the most influential flood susceptibility criteria. After calculating these weights, the criteria’s layers were aggregated through geospatial analysis, which resulted in generating flood susceptibility map. The area under the curve (AUC) and statistical measures such as the Kappa index were used to evaluate the proposed method's efficiency. The validation results illustrate that hybrid FAHP, with AUC= 96.68 and Kappa = 81.36 performed more efficiently than standard AHP, with AUC= 94.53 and Kappa=76.35. Overlaying these maps with the historical flood inventory dataset revealed that 86.43% of flooded areas were categorized as “high” and “very high”. Therefore, the flood susceptibility maps generated through the proposed approach can help the decision-makers and managers allocate the mitigation equipment and facility in data-scarce and ungauged large-scale areas

A novel rule-based approach in mapping landslide susceptibility

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes

Automatic landslide detection using Dempster–Shafer theory from LiDAR-derived data and orthophotos

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. A good landslide inventory map is a prerequisite for landslide hazard and risk analysis. In tropical countries, such as Malaysia, preparation of the landslide inventory is a challenging task because of the rapid growth of vegetation. Thus, it is crucial to use rapid and accurate technique and effective parameters. For this purpose, Dempster Shafer theory (DST) was applied in fusing high resolution LiDAR derived data products and Greenness index derived from orthophoto imagery. Two sites were selected, for the implementation and evaluation of the DST model; site “A” for DST implementation and site “B” for the comparison. For model implementation, vegetation index, slope and height were used as effective parameters for identifying automatic landslide detection. Two type of DST based fusions were evaluated; (greenness and height) and (greenness and slope). Furthermore, validation techniques were used to validate the accuracy are confusion matrix and area under the curve. The overall accuracy of the first and second evaluated fusions were (73.4% and 84.33%), and area under the curve were (0.76 and 0.81) respectively. Additionally, the result was compared with Random Forest (RF) based detection approach. The results showed that DST does not require a priori knowledge

Characterization of Susceptible Landslide Zones by an Accumulated Index

In order to characterize the landslide susceptibility in the central zone of Guerrero State in Mexico, a spatial model has been designed and implemented, which automatically generates cartography. Conditioning factors as geomorphological, geological, and anthropic variables were considered, and as a detonating factor, the effect of the accumulated rain. The use of an inventory map of landslides that occurred in the past (IL) was also necessary, which was produced by an unsupervised detection method. Before the design of the model, an analysis of the contribution of each factor, related to the landslide inventory map, was performed by the Jackknife test. The designed model consists of a susceptibility index (SI) calculated pixel by pixel by the accumulation of the individual contribution of each factor, and the final index allows the susceptibility cartography to slide in the study area. The evaluation of the obtained map was performed by applying an analysis of the frequency ratio (FR) graphic, and an analysis of the receiver operating characteristic (ROC) curve was developed. Studies like this can help different safeguarding institutions, locating the areas where there is a greater vulnerability according to the considered factors, and integrating disaster attention management or prevention plans

Identifying landslide hazards in a tropical mountain environment, using geomorphologic and probabilistic approaches

The objective of this study is the performance, assessing, comparison and validation of a set of three landslide hazard maps: The geomorphological, the multicriteria evaluation (MCE) and the probabilistic (weights of evidence); in order to evaluate its accuracy, advantages and limitations, and finally state its reliability. These approaches were tested in a tropical mountain environment located in the central Venezuelan Andes. The scale of this study is regional. A landslide inventory map was generated through aerial-photointerpretation and by the processing of two sets of Landsat imagery via contrast-widening color composite, given as result the outline of 493 landslide polygons, then given the main role played for a digital elevation model (DEM) as data input, a DEM for the study area was built through remotely sensed data obtained from the shuttle radar topographical mission (SRTM) and optical stereographic imagery provided by the advanced spaceborne thermal emission and reflection radiometer (ASTER) system. Because of the comparative nature of this study, these data was preliminary processed via density analysis in order to establish a common background on the landsliding process - passive factors relationship, which was used later to set up the criteria applied in the geomorphological and multicriteria evaluation (MCE) approaches. As a way of validation, the accuracy and error rate of the three landslide hazard maps were performed by its comparison to the landslide inventory map. It was concluded that although the geomorphological approach achieved a better landslide predictive power for this study area at a regional scale, the remaining procedures can play a complementary role, for example the MCE plays a crucial role in an early assessment of landslide hazard which highlights the needs and improving necessary to achieve a better probabilistic approach, which can be later incorporated in a more objective geomorphological assessment. Results also showed that any methodology can be improved and even empowered by the development of better and more integrated standards for factor maps collection rather that the simplification of them, in that way, further studies at regional scale must explore the remotely sensed imagery capacities for generation of data bases addressing regional susceptibility to landsliding process