J. Chacón

Bio: J. Chacón is an academic researcher from University of Granada. The author has contributed to research in topics: Landslide & Poison control. The author has an hindex of 18, co-authored 44 publications receiving 1479 citations.

Engineering geology maps: landslides and geographical information systems

Abstract: IAEG Commission No. 1—Engineering Geological Maps—is developing a guide to hazard maps. Scientists from 17 countries have participated. This paper is one of a series that presents the results of that work. It provides a general review of GIS landslide mapping techniques and basic concepts of landslide mapping. Three groups of maps are considered: maps of spatial incidence of landslides, maps of spatial–temporal incidence and forecasting of landslides and maps of assessment of the consequences of landslides. With the current era of powerful microcomputers and widespread use of GIS packages, large numbers of papers on the subject are becoming available, frequently founded on different basic concepts. In order to achieve a better understanding and comparison, the concepts proposed by Varnes (Landslide hazard zonation: a review of principles and practice, 1984) and Fell (Some landslide risk zoning schemes in use in Eastern Australua and their application 1992; Landslide risk assessment and acceptable risk. Can Geotech J 31:261–272, 1994) are taken as references. It is hoped this will also add to the international usefulness of these maps as tools for landslide prevention and mitigation. Six hundred and sixty one papers and books related to the topic are included in the references, many of which are reviewed in the text.

Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain)

Abstract: . A procedure to select the controlling factors connected to the slope instability has been defined. It allowed us to assess the landslide susceptibility in the Rio Beiro basin (about 10 km2) over the northeastern area of the city of Granada (Spain). Field and remote (Google EarthTM) recognition techniques allowed us to generate a landslide inventory consisting in 127 phenomena. To discriminate between stable and unstable conditions, a diagnostic area had been chosen as the one limited to the crown and the toe of the scarp of the landslide. 15 controlling or determining factors have been defined considering topographic, geologic, geomorphologic and pedologic available data. Univariate tests, using both association coefficients and validation results of single-variable susceptibility models, allowed us to select the best predictors, which were combined for the unique conditions analysis. For each of the five recognised landslide typologies, susceptibility maps for the best models were prepared. In order to verify both the goodness of fit and the prediction skill of the susceptibility models, two different validation procedures were applied and compared. Both procedures are based on a random partition of the landslide archive for producing a test and a training subset. The first method is based on the analysis of the shape of the success and prediction rate curves, which are quantitatively analysed exploiting two morphometric indexes. The second method is based on the analysis of the degree of fit, by considering the relative error between the intersected target landslides by each of the different susceptibility classes in which the study area was partitioned. Both the validation procedures confirmed a very good predictive performance of the susceptibility models and of the actual procedure followed to select the controlling factors.

Methodology for Landslide Susceptibility Mapping by Means of a GIS. Application to the Contraviesa Area (Granada, Spain)

Abstract: This article presents a method to map landslide susceptibility in rock massifs using Geographical Information Systems (GIS). The method is based on making an inventory of rupture zones of different types of slope movements and then analysing the bivariate correlation of these with the factors that determine instability. After determining the factors that present the highest correlation with each type of movement, a matrix is created to combine these factors and to determine the percentage of the rupture zone in each combination, which provides an expression of the susceptibility of the terrain. The map thus obtained is divided into susceptibility classes. The susceptibility maps (made in 1995) for each type of movement are first calibrated with the inventory of the movements from which they are derived (previous to 1995), and subsequently validated by another inventory elaborated after the susceptibility maps (in 1997). In both cases, significant correlation coefficients were obtained (the Goodman–Kruskal coefficients were over 0.8 and sometimes exceeded 0.9). The relative error (degree of accumulated fit for very low to low susceptibility classes) was always less than 5%,while the relative success rate was always above 50%. These resultsillustrate the adequacy of the method and of the maps obtained.

Evaluation and validation of landslide-susceptibility maps obtained by a GIS matrix method: examples from the Betic Cordillera (southern Spain)

Abstract: This work presents the results of applying the matrix method in a Geo- graphic Information System (GIS) to the drawing of maps of susceptibility to slope movements in different sectors of the Betic Cordillera (southern Spain). In addition, the susceptibility models built by the matrix method were compared with a multi- variate statistical method, and the first method gave the best results. The susceptibility maps drawn by the GIS matrix method were validated by calculating the coefficients of association with the degree of fit between recent slope movements registered in 1997 and the different levels of susceptibility of previously drawn maps (1995-1996) in different representative zones of the Betic Cordillera (southern Spain). The first sector studied showed excellent degrees of fit, with an error of less than 10% for all the slope failures and 3% when considering only failures of natural origin. In the second sector, the relative errors were less than 5%. In the third sector, the error hardly exceeded 6%. The results are discussed in the different zones and for each type of slope movement. In any case, these results evidence the predictive capacity of susceptibility maps drawn in GIS by the matrix method, for a great number of slope movements.

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

Abstract: Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.

Recommendations for the quantitative analysis of landslide risk

Abstract: This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.