Luigi Lombardo

Binary logistic regression versus stochastic gradient boosted decision trees in assessing landslide susceptibility for multiple-occurring landslide events: application to the 2009 storm event in Messina (Sicily, southern Italy)

Abstract This study aims to compare binary logistic regression (BLR) and stochastic gradient treeboost (SGT) methods in assessing landslide susceptibility within the Mediterranean region for multiple-occurrence regional landslide events. A test area was selected in the north-eastern sector of Sicily (southern Italy) where thousands of debris flows and debris avalanches triggered on the first October 2009 due to an extreme storm. Exploiting the same set of predictors and the 2009 event landslide archive, BLR- and SGT-based susceptibility models have been obtained for the two catchments separately, adopting a random partition (RP) technique for validation. In addition, the models trained in one catchment have been tested in predicting the landslide distribution in the second, adopting a spatial partition (SP)-based validation. The models produced high predictive performances with a general consistency between BLR and SGT in the susceptibility maps, predictor importance and role. In particular, SGT models reached a higher prediction performance with respect to BLR models for RP-modelling, while for the SP-based models, the difference in predictive skills dropped, converging to equally excellent performances. However, analysing the precision of the probability estimates, BLR produced more robust models around the mean value for each pixel, indicating possible overfitting effects, which affect decision trees to a greater extent. The assessment of the predictor roles allowed identifying the activation mechanisms which are primarily controlled by steep south-facing open slopes located near the coastal area. These slopes are characterised by low/middle altitude downhill from mountain tops, having a medium-grade metamorphic bedrock, under grassland and cultivated (terraced) uses.

Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region : the role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling

SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0-0.3m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63-0.69) and low uncertainty (s.d.<0.76gCkg-1 with RS, and <1.25gCkg-1 without RS). These outputs allowed depicting a time variation of SOC at 1arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.

Integrated geophysical survey for 3D modelling of a coastal aquifer polluted by seawater

Geophysical surveys are carried out in the coastal area of Petrosino (south-western Sicily) to study the time evolution of seawater contamination of the coastal aquifer, probably increased due to human impact. The overexploitation of the aquifer, due to an intensive agricultural use has affected significantly the natural hydro-geochemical state of the basin. The study is based on a processing and integrated analysis of hydrogeological, geochemical and geophysical data. In particular in the last two years seasonal time-lapse electrical resistivity tomographies (ERT), new TDEM soundings and Multi-Analysis Surface Wave soundings (MASW) have been carried out. The interpretation of the total set of previously existing and new geophysical data made it possible to reconstruct a threedimensional model of the electrical resistivity of the aquifer, aimed at defining the extent and geometry of the seawater intrusion. Furthermore, the execution of a series of high-resolution timelapse electrical tomographies and a correlation analysis between geophysical measures and geochemical, geological and hydrogeological data allowed to discriminate the effects of the salt concentration in the groundwater and the porosity and saturation degree of the rock on the time variations of the measured electrical resistivity. Finally, the average porosity of the rocks forming the reservoir was determined.

Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster

AbstractWe develop a stochastic modeling approach based on spatial point processes of log-Gaussian Cox type for a collection of around 5000 landslide events provoked by a precipitation trigger in Sicily, Italy. Through the embedding into a hierarchical Bayesian estimation framework, we can use the integrated nested Laplace approximation methodology to make inference and obtain the posterior estimates of spatially distributed covariate and random effects. Several mapping units are useful to partition a given study area in landslide prediction studies. These units hierarchically subdivide the geographic space from the highest grid-based resolution to the stronger morphodynamic-oriented slope units. Here we integrate both mapping units into a single hierarchical model, by treating the landslide triggering locations as a random point pattern. This approach diverges fundamentally from the unanimously used presence–absence structure for areal units since we focus on modeling the expected landslide count jointly within the two mapping units. Predicting this landslide intensity provides more detailed and complete information as compared to the classically used susceptibility mapping approach based on relative probabilities. To illustrate the model’s versatility, we compute absolute probability maps of landslide occurrences and check their predictive power over space. While the landslide community typically produces spatial predictive models for landslides only in the sense that covariates are spatially distributed, no actual spatial dependence has been explicitly integrated so far. Our novel approach features a spatial latent effect defined at the slope unit level, allowing us to assess the spatial influence that remains unexplained by the covariates in the model. For rainfall-induced landslides in regions where the raingauge network is not sufficient to capture the spatial distribution of the triggering precipitation event, this latent effect provides valuable imaging support on the unobserved rainfall pattern.

Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model

Abstract Grid-based landslide susceptibility models at regional scales are computationally demanding when using a fine grid resolution. Conversely, Slope-Unit (SU) based susceptibility models allows to investigate the same areas offering two main advantages: 1) a smaller computational burden and 2) a more geomorphologically-oriented interpretation. In this contribution, we generate SU-based landslide susceptibility for the Sado Island in Japan. This island is characterized by deep-seated landslides which we assume can only limitedly be explained by the first two statistical moments (mean and variance) of a set of predictors within each slope unit. As a consequence, in a nested experiment, we first analyse the distributions of a set of continuous predictors within each slope unit computing the standard deviation and quantiles from 0.05 to 0.95 with a step of 0.05. These are then used as predictors for landslide susceptibility. In addition, we combine shape indices for polygon features and the normalized extent of each class belonging to the outcropping lithology in a given SU. This procedure significantly enlarges the size of the predictor hyperspace, thus producing a high level of slope-unit characterization. In a second step, we adopt a LASSO-penalized Generalized Linear Model to shrink back the predictor set to a sensible and interpretable number, carrying only the most significant covariates in the models. As a result, we are able to document the geomorphic features (e.g., 95% quantile of Elevation and 5% quantile of Plan Curvature) that primarily control the SU-based susceptibility within the test area while producing high predictive performances. The implementation of the statistical analyses are included in a parallelized R script (LUDARA) which is here made available for the community to replicate analogous experiments.

Estimation of intrinsic aquifer vulnerability with index-overlay and statistical methods: the case of eastern Kopaida, central Greece

The intrinsic vulnerability of a karstic aquifer system in central Greece was jointly assessed with the use of a statistical approach and PI method, as a function of topography, protective cover effectiveness and the degree to which this cover is bypassed due to flow conditions. The input data for the index-overlay PI method were derived from field works and 71 boreholes of the area; the information was obtained, subsequently its critical factors were compiled which included lithology, fissuring and karstification of bedrock, soil characteristics, hydrology, hydrogeology, topography and vegetation. The aforementioned parameters were processed jointly with the aid of a GIS and yielded the final estimation of intrinsic aquifer vulnerability to contamination. Results were compared with an equivalent spatially distributed probability map obtained through a stochastic approach. The calibration and test phase of the latter relied on morphometric conditions derived by terrain analyses of a digital elevation model as well as on geology and land use from thematic maps. This procedure allowed taking into account the topographic influences with respect to a deep system such as the local karstic aquifer of eastern Kopaida basin. Finally, results were validated with ground truth nitrate values obtained from 41 groundwater samples, highlighted the spatial delineation of susceptible areas to contamination in both cases and provided a robust tool for regional planning actions and water resources management schemes.

Integration of two-phase solid fluid equations in a catchment model for flashfloods, debris flows and shallow slope failures

Abstract An integrated, modeling method for shallow landslides, debris flows and catchment hydrology is developed and presented in this paper. Existing two-phase debris flow equations and an adaptation on the infinite slope method are coupled with a full hydrological catchment model. We test the approach on the 4 km 2 Scaletta catchment, North-Eastern Sicily, where the 1-10-2009 convective storm caused debris flooding after 395 shallow landslides. Validation is done based on the landslide inventory and photographic evidence from the days after the event. Results show that the model can recreate the impact of both shallow landslides, debris flow runout, and debris floods with acceptable accuracy (91 percent inventory overlap with a 0.22 Cohens Kappa). General patterns in slope failure and runout are well-predicted, leading to a fully physically based prediction of rainfall induced debris flood behavior in the downstream areas, such as the creation of a debris fan at the coastal outlet.