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In this study, an inventory of storm-triggered debris flows performed in the area of the San Vicente volcano (El Salvador, CA) was used to calibrate predictive models and prepare a landslide susceptibility map. The storm event struck the area in November 2009 as the result of the simultaneous action of low-pressure system 96E and Hurricane Ida. Multivariate Adaptive Regression Splines (MARS) was employed to model the relationships between a set of environmental variables and the locations of the debris flows. Validation of the models was performed by splitting 100 random samples of event and non-event 10 m pixels into training and test subsets. The validation results revealed an excellent (area under the receiver operating characteristic (ROC) curve (AUC) = 0.80) and stable (AUC std. dev. = 0.01) ability of MARS to predict the locations of the debris flows which occurred in the study area. However, when using the Youden’s index as probability threshold to discriminate between pixels predicted as positives and negatives, MARS exhibits a moderate ability to identify stable cells (specificity = 0.66). The final debris flow susceptibility map, which was prepared by averaging for each pixel the score of the 100 MARS repetitions, shows where future debris flows are more likely to occur, and thus may help in mitigating the risk associated with these landslides.
Claudio Mercurio; Chiara Martinello; Edoardo Rotigliano; Abel Alexei Argueta-Platero; Mario Ernesto Reyes-Martínez; Jacqueline Yamileth Rivera-Ayala; Christian Conoscenti. Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA). Earth 2021, 2, 66 -85.
AMA StyleClaudio Mercurio, Chiara Martinello, Edoardo Rotigliano, Abel Alexei Argueta-Platero, Mario Ernesto Reyes-Martínez, Jacqueline Yamileth Rivera-Ayala, Christian Conoscenti. Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA). Earth. 2021; 2 (1):66-85.
Chicago/Turabian StyleClaudio Mercurio; Chiara Martinello; Edoardo Rotigliano; Abel Alexei Argueta-Platero; Mario Ernesto Reyes-Martínez; Jacqueline Yamileth Rivera-Ayala; Christian Conoscenti. 2021. "Mapping Susceptibility to Debris Flows Triggered by Tropical Storms: A Case Study of the San Vicente Volcano Area (El Salvador, CA)." Earth 2, no. 1: 66-85.
In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model.
Chiara Martinello; Chiara Cappadonia; Christian Conoscenti; Valerio Agnesi; Edoardo Rotigliano. Optimal slope units partitioning in landslide susceptibility mapping. Journal of Maps 2020, 1 -11.
AMA StyleChiara Martinello, Chiara Cappadonia, Christian Conoscenti, Valerio Agnesi, Edoardo Rotigliano. Optimal slope units partitioning in landslide susceptibility mapping. Journal of Maps. 2020; ():1-11.
Chicago/Turabian StyleChiara Martinello; Chiara Cappadonia; Christian Conoscenti; Valerio Agnesi; Edoardo Rotigliano. 2020. "Optimal slope units partitioning in landslide susceptibility mapping." Journal of Maps , no. : 1-11.
In this study, the ability of five topographic indices to predict the gully trajectories observed in two adjacent watersheds located in Sicily (Italy) was evaluated. Two of these indices, named MSPI and MTWI, as far as we know, have never been employed to this aim. They were obtained by multiplying the stream power index (SPI) and the topographic wetness index (TWI), respectively, by the convergence index (CI). The predictive ability of the topographic indices was measured by using both cut-off independent (AUC: area under the receiver operating characteristic curve) and dependent statistics (Cohen's kappa index κ, sensitivity, specificity). These statistics were calculated also for 100 MARS (multivariate adaptive regression splines) and 100 LR (logistic regression) model runs, which used as predictors the topographic variables (i.e. contributing area, slope steepness, plan curvature and convergence index) combined into the five indices. Performance statistics of both topographic indices and statistical models were calculated using 100 random samples of 2 m grid cells, which were extracted only from flow concentration lines. This was done in order to focus the validation process on where gully erosion is more likely to occur. MSPI achieved the best predictive skill (AUC > 0.93; κ > 0.71) among the topographic indices and exhibited similar and better accuracy than local (i.e. trained and validated in the same watershed) and transferred (i.e. trained in one watershed and tested in the other one) LR models, respectively. On the other hand, MSPI performed similarly to transferred MARS runs (AUC > 0.92; κ > 0.71) but slightly worse than local MARS runs (AUC > 0.95; κ > 0.77). Based on the results of this experiment, it can be inferred that (i) including CI helps in detecting hollow areas where gullies are more likely to occur and (ii) MPSI can be a valid alternative to a data driven approach for mapping gully erosion susceptibility in areas where a gully inventory is not available, which is necessary to calibrate statistical models.
Christian Conoscenti; Edoardo Rotigliano. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models. Geomorphology 2020, 359, 107123 .
AMA StyleChristian Conoscenti, Edoardo Rotigliano. Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models. Geomorphology. 2020; 359 ():107123.
Chicago/Turabian StyleChristian Conoscenti; Edoardo Rotigliano. 2020. "Predicting gully occurrence at watershed scale: Comparing topographic indices and multivariate statistical models." Geomorphology 359, no. : 107123.
Soil erosion is a serious problem affecting numerous countries, especially, gully erosion. In the current research, GIS techniques and MARS (Multivariate Adaptive Regression Splines) algorithm were considered to evaluate gully erosion susceptibility mapping among others. The study was conducted in a specific section of the Gorganroud Watershed in Golestan Province (Northern Iran), covering 2142.64 km2 which is intensely influenced by gully erosion. First, Google Earth images, field surveys, and national reports were used to provide a gully-hedcut evaluation map consisting of 307 gully-hedcut points. Eighteen gully erosion conditioning factors including significant geoenvironmental and morphometric variables were selected as predictors. To model sensitivity of gully erosion, Multivariate Adaptive Regression Splines (MARS) was used while the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), drawing ROC curves, efficiency percent, Yuden index, and kappa were used to evaluate model efficiency. We used two different scenarios of the combination of the number of replications, and sample size, including 90%/10% and 80%/20% with 10 replications, and 70%/30% with five, 10, and 15 replications for preparing gully erosion susceptibility mapping (GESM). Each one involves a various subset of both positive (presence), and negative (absence) cases. Absences were extracted as randomly distributed individual cells. Therefore, the predictive competency of the gully erosion susceptibility model and the robustness of the procedure were evaluated through these datasets. Results did not show considerable variation in the accuracy of the model, with altering the percentage of calibration to validation samples and number of model replications. Given the accuracy, the MARS algorithm performed excellently in predictive performance. The combination of 80%/20% using all statistical measures including SST (0.88), SPF (0.83), E (0.79), Kappa (0.58), Robustness (0.01), and AUC (0.84) had the highest performance compared to the other combinations. Consequently, it was found that the performance of MARS for modelling gully erosion susceptibility is quite consistent while changes in the testing and validation specimens are executed. The intense acceptable prediction capability of the MARS model verifies the reliability of the method employed for use of this model elsewhere and gully erosion studies since they are qualified to quickly generating precise and exact GESMs (gully erosion sensitivity maps) to make decisions and management edaphic and hydrologic features.
Narges Javidan; Ataollah Kavian; Hamid Reza Pourghasemi; Christian Conoscenti; Zeinab Jafarian. Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water 2019, 11, 2319 .
AMA StyleNarges Javidan, Ataollah Kavian, Hamid Reza Pourghasemi, Christian Conoscenti, Zeinab Jafarian. Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios. Water. 2019; 11 (11):2319.
Chicago/Turabian StyleNarges Javidan; Ataollah Kavian; Hamid Reza Pourghasemi; Christian Conoscenti; Zeinab Jafarian. 2019. "Gully Erosion Susceptibility Mapping Using Multivariate Adaptive Regression Splines—Replications and Sample Size Scenarios." Water 11, no. 11: 2319.
Climate change is one of the most important issues of anthropogenic activities. The increasing drought conditions can cause water shortage and heat waves and can influence the agricultural production or the water supply of cities. The Carpathian region is also affected by this phenomenon; thus, we aimed at identifying the tendencies between 1960 and 2010 applying the CarpatClim (CC) database. We calculated the trends for each grid point of CC, plotted the results on maps, and applied statistical analysis on annual and seasonal level. We revealed that monthly average temperature, maximum temperature and evapotranspiration had similar patterns and had positive trends in all seasons except autumn. Precipitation also had a positive trend, but it had negative values in winter. The geospatial analysis disclosed an increasing trend from West to East and from north to west. A simple binary approach (value of 1 above the upper quartile in case of temperature and evapotranspiration, value of 1 below the lower quartile; 0 for the rest of the data) helped to identify the most sensitive areas where all the involved climatic variables exceeded the threshold: Western Hungary and Eastern Croatia. Results can help to prepare possible mitigation strategies to climate change and both landowners and planners can draw the conclusions.
Szilárd Szabó; Noémi Mária Szopos; Boglárka Bertalan-Balázs; Elemér László; Dragan D. Milošević; Christian Conoscenti; István Lázár. Geospatial analysis of drought tendencies in the Carpathians as reflected in a 50-year time series. Hungarian Geographical Bulletin 2019, 68, 269 -282.
AMA StyleSzilárd Szabó, Noémi Mária Szopos, Boglárka Bertalan-Balázs, Elemér László, Dragan D. Milošević, Christian Conoscenti, István Lázár. Geospatial analysis of drought tendencies in the Carpathians as reflected in a 50-year time series. Hungarian Geographical Bulletin. 2019; 68 (3):269-282.
Chicago/Turabian StyleSzilárd Szabó; Noémi Mária Szopos; Boglárka Bertalan-Balázs; Elemér László; Dragan D. Milošević; Christian Conoscenti; István Lázár. 2019. "Geospatial analysis of drought tendencies in the Carpathians as reflected in a 50-year time series." Hungarian Geographical Bulletin 68, no. 3: 269-282.
This research introduces a scientific methodology for gully erosion susceptibility mapping (GESM) that employs geography information system (GIS)-based multi-criteria decision analysis. The model was tested in Semnan Province, Iran, which has an arid and semi-arid climate with high susceptibility to gully erosion. The technique for order of preference by similarity to ideal solution (TOPSIS) and the analytic hierarchy process (AHP) multi-criteria decision-making (MCDM) models were integrated. The important aspect of this research is that it did not require gully erosion inventory maps for GESM. Therefore, the proposed methodology could be useful in areas with missing or incomplete data. Fifteen variables reflecting topographic, hydrologic, geologic, environmental and soil characteristics were selected as proxies for gully erosion conditioning factors (GECFs). The experiment was conducted using 200 sample points that were selected randomly in the study area, and the weights of criteria (GECFs) were obtained using the AHP model. In the next step, the TOPSIS model was applied, and the weight of each alternative (sample points) was obtained. Kriging and inverse distance-weighted (IDW) methods were used for interpolation and GESM. Natural break method was used for classifying gully erosion susceptibility into five classes, from very low to very high. The area under the ROC curve (AUC) was used for validation. AHP results showed that distance to stream (0.14), slope degree (0.13) and distance to road (0.12) played major roles in controlling gully erosion in the study area. The values of points obtained by using the TOPSIS model ranged from 0.321 to 0.808. Verification results showed that kriging had higher prediction accuracy than IDW. The GESM results obtained by this methodology can be used by decision makers and managers to plan preventive measures and reduce damages due to gully erosion.
Alireza Arabameri; Biswajeet Pradhan; Khalil Rezaei; Christian Conoscenti. Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. CATENA 2019, 180, 282 -297.
AMA StyleAlireza Arabameri, Biswajeet Pradhan, Khalil Rezaei, Christian Conoscenti. Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques. CATENA. 2019; 180 ():282-297.
Chicago/Turabian StyleAlireza Arabameri; Biswajeet Pradhan; Khalil Rezaei; Christian Conoscenti. 2019. "Gully erosion susceptibility mapping using GIS-based multi-criteria decision analysis techniques." CATENA 180, no. : 282-297.
Landslides are among the most dangerous natural processes. Debris avalanches and debris flows in particular have often caused casualties and severe damage to infrastructures in a wide range of environments. The assessment of susceptibility to these phenomena may help policy makers in mitigating the associated risk and thus it has attracted special attention in the last decades. In this experiment, we assessed susceptibility to debris-avalanche and -flow landslides by using a stochastic approach. Two different modeling techniques were employed: i) Multivariate Adaptive Regression Splines (MARS) and ii) Logistic Regression (LR). Both MARS and LR allow for calculating the probability of landslide occurrence by building statistical relationships between a set of environmental variables and the target variable, i.e. presence/absence of the landslide event. The target variable was extracted from an inventory of debris-avalanche and - flow landslides which were triggered by the tropical storm that hit the area of Mocoa (Colombia) on 1 April 2017. As predictor variables, we employed nine terrain attributes derived from a 5-m resolution DEM (i.e. elevation, slope angle, northness, eastness, upslope slope angle, convergence index, topographic position index, valley depth and topographic wetness index), in addition to lithology, distance from faults and presence/absence of soil creep processes. In our experiment, we used three different landslide datasets which contain i) the highest point of each recognized landslide crown-lines (dataset LIP), ii) the highest 10% of cells of each landslide area (dataset SOURCE), and iii) the entire landslide areas, which include initiation and accumulation zones (dataset MASS). In order to evaluate their predictive ability, LR and MARS models were submitted to k-fold spatial cross-validation strategy, which consists in extracting random training and test subsets from k spatially disjoint sub-areas. The results of model validation, expressed in terms of Area Under the ROC Curve (AUC), demonstrate better predictive performance of MARS models with respect to LR models, for all the three landslide datasets. The mean AUC values calculated for the datasets LIP, SOURCE and MASS of the MARS models are 0.776, 0.788 and 0.768, respectively, whereas AUC values of the LR models are 0.748, 0.751 and 0.703, respectively. Model validation also shows that the predictive skill of the models is better when landslide data are sampled from the highest portions of the landslides (dataset SOURCE). Maps of susceptibility to debris-avalanche and -flow landslides for the Mocoa area were produced by using both LR and MARS and the three landslide datasets. The analysis of the distribution of events versus the susceptibility classes of the maps confirm that MARS and the dataset SOURCE provide the best ability to discriminate between event and non-event cells.
Germán Vargas-Cuervo; Edoardo Rotigliano; Christian Conoscenti. Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017. Geomorphology 2019, 339, 31 -43.
AMA StyleGermán Vargas-Cuervo, Edoardo Rotigliano, Christian Conoscenti. Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017. Geomorphology. 2019; 339 ():31-43.
Chicago/Turabian StyleGermán Vargas-Cuervo; Edoardo Rotigliano; Christian Conoscenti. 2019. "Prediction of debris-avalanches and -flows triggered by a tropical storm by using a stochastic approach: An application to the events occurred in Mocoa (Colombia) on 1 April 2017." Geomorphology 339, no. : 31-43.
The main topic of this research was to evaluate the effect in the performance of stochastic landslide susceptibility models, produced by differences between the triggering events of the calibration and validation datasets. In the Caldera Ilopango area (El Salvador), MARS (multivariate adaptive regression splines)-based susceptibility modeling was applied using a set of physical–environmental predictors and two remotely recognized landslide inventories: one dated at 2003 (1503 landslides), which was the result of a normal rainfall season, and one which was produced by the combined effect of the Ida hurricane and the 96E tropical depression in 2009 (2237 landslides). Both the event inventories included shallow debris—flow or slide landslides, which involved the weathered mantle of the pyroclastic rocks that largely outcrop in the study area. To this aim, different model building and validation strategies were applied (self-validation, forward and backward chrono-validations), and their performances evaluated both through cutoff-dependent and -independent metrics. All of the tested models produced largely acceptable AUC (area under curve) values, albeit a loss in the predictive performance from self-validation to chrono-validation was observed. Besides, in terms of positive/negative predictions, some critical differences arose: using the 2009 extreme landslide inventory for calibration resulted in higher sensitivity but lower specificity; conversely, using the 2003 normal trigger landslide calibration inventory led to higher specificity but lower sensitivity, with relevant increase in type-II errors. These results suggest the need for investigating the extent of such effects, taking multitrigger intensities inventories as a standard procedure for susceptibility assessment in areas where extreme events potentially occur.
E. Rotigliano; Chiara Martinello; M. A. Hernandéz; V. Agnesi; C. Conoscenti. Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies. Environmental Earth Sciences 2019, 78, 210 .
AMA StyleE. Rotigliano, Chiara Martinello, M. A. Hernandéz, V. Agnesi, C. Conoscenti. Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies. Environmental Earth Sciences. 2019; 78 (6):210.
Chicago/Turabian StyleE. Rotigliano; Chiara Martinello; M. A. Hernandéz; V. Agnesi; C. Conoscenti. 2019. "Predicting the landslides triggered by the 2009 96E/Ida tropical storms in the Ilopango caldera area (El Salvador, CA): optimizing MARS-based model building and validation strategies." Environmental Earth Sciences 78, no. 6: 210.
The main purpose was to compare discrimination and reliability of four machine learning models to create gully erosion susceptibility map (GESM) in a part of Ekbatan Dam Basin, Hamedan, western Iran. Extensive field surveys using GPS, and the visual interpretation of satellite images, used to prepare a digital map of the spatial distribution of gullies. 130 locations were sampled to elucidate the spatial distribution of the soil surface properties. Topographic attributes were provided from digital elevation model (DEM). The land use and normalized difference vegetation index (NDVI) maps were created by satellite imagery. The functional relationships between gully erosion and controlling factors were calculated using the random forest (RF), support vector machine (SVM), Naïve Bayes (NB), and generalized additive model (GAM) models. The performance of models was evaluated by 10-fold cross-validation based on efficiency, Kappa coefficient, receiver operating characteristic curve (ROC), mean absolute error (MAE), and root mean square error (RMSE). The results showed that the RF model had the highest amount of efficiency, Kappa coefficient, and AUC and the lowest amounts of MAE and RMSE compared with SVM, NB, and GAM. The RF model showed the highest predictive performance (mean AUC = 92.4%), followed by SVM (mean AUC = 90.9%), GAM (mean AUC = 89.9%), and NB (mean AUC = 87.2%) models. Overall accuracy of the models ranged from excellent (NB, GAM) to outstanding (RF, SVM) classes. The capacity of all models for creating GESM was quite stable when the calibration and validation samples were changed through10-fold cross-validation technique. According to variable importance analysis performed by RF model, the most important variables are distance from rivers, calcium carbonate equivalent (CCE), and topographic position index (TPI). The obtained maps can help identifying areas at risk of gully erosion and facilitate the implementation of plans for soil conservation and sustainable management.
Younes Garosi; Mohsen Sheklabadi; Christian Conoscenti; Hamid Reza Pourghasemi; Kristof Van Oost. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of The Total Environment 2019, 664, 1117 -1132.
AMA StyleYounes Garosi, Mohsen Sheklabadi, Christian Conoscenti, Hamid Reza Pourghasemi, Kristof Van Oost. Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion. Science of The Total Environment. 2019; 664 ():1117-1132.
Chicago/Turabian StyleYounes Garosi; Mohsen Sheklabadi; Christian Conoscenti; Hamid Reza Pourghasemi; Kristof Van Oost. 2019. "Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion." Science of The Total Environment 664, no. : 1117-1132.
Geospatial computation, data transformation to a relevant statistical software, and step-wise quantitative performance assessment can be cumbersome, especially when considering that the entire modelling procedure is repeatedly interrupted by several input/output steps, and the self-consistency and self-adaptive response to the modelled data and the features therein are lost while handling the data from different kinds of working environments. To date, an automated and a comprehensive validation system, which includes both the cutoff-dependent and –independent evaluation criteria for spatial modelling approaches, has not yet been developed for GIS based methodologies. This study, for the first time, aims to fill this gap by designing and evaluating a user-friendly model validation approach, denoted as Performance Measure Tool (PMT), and developed using freely available Python programming platform. The considered cutoff-dependent criteria include receiver operating characteristic (ROC) curve, success-rate curve (SRC) and prediction-rate curve (PRC), whereas cutoff-independent consist of twenty-one performance metrics such as efficiency, misclassification rate, false omission rate, F-score, threat score, odds ratio, etc. To test the robustness of the developed tool, we applied it to a wide variety of geo-environmental modelling approaches, especially in different countries, data, and spatial contexts around the world including, the USA (soil digital modelling), Australia (drought risk evaluation), Vietnam (landslide studies), Iran (flood studies), and Italy (gully erosion studies). The newly proposed PMT is demonstrated to be capable of analyzing a wide range of environmental modelling results, and provides inclusive performance evaluation metrics in a relatively short time and user-convenient framework whilst each of the metrics is used to address a particular aspect of the predictive model. Drawing on the inferences, a scenario-based protocol for model performance evaluation is suggested.
Omid Rahmati; Aiding Kornejady; Mahmood Samadi; Ravinesh C. Deo; Christian Conoscenti; Luigi Lombardo; Kavina Dayal; Ruhollah Taghizadeh-Mehrjardi; Hamid Reza Pourghasemi; Sandeep Kumar; Dieu Tien Bui. PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches. Science of The Total Environment 2019, 664, 296 -311.
AMA StyleOmid Rahmati, Aiding Kornejady, Mahmood Samadi, Ravinesh C. Deo, Christian Conoscenti, Luigi Lombardo, Kavina Dayal, Ruhollah Taghizadeh-Mehrjardi, Hamid Reza Pourghasemi, Sandeep Kumar, Dieu Tien Bui. PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches. Science of The Total Environment. 2019; 664 ():296-311.
Chicago/Turabian StyleOmid Rahmati; Aiding Kornejady; Mahmood Samadi; Ravinesh C. Deo; Christian Conoscenti; Luigi Lombardo; Kavina Dayal; Ruhollah Taghizadeh-Mehrjardi; Hamid Reza Pourghasemi; Sandeep Kumar; Dieu Tien Bui. 2019. "PMT: New analytical framework for automated evaluation of geo-environmental modelling approaches." Science of The Total Environment 664, no. : 296-311.
In north of Iran, flood is one of the most important natural hazards that annually inflict great economic damages on humankind infrastructures and natural ecosystems. The Kiasar watershed is known as one of the critical areas in north of Iran, due to numerous floods and waste of water and soil resources, as well as related economic and ecological losses. However, a comprehensive and systematic research to identify flood-prone areas, which may help to establish management and conservation measures, has not been carried out yet. Therefore, this study tested four methods: evidential belief function (EBF), frequency ratio (FR), Technique for Order Preference by Similarity To ideal Solution (TOPSIS) and Vlse Kriterijumsk Optimizacija Kompromisno Resenje (VIKOR) for flood hazard susceptibility mapping (FHSM) in this area. These were combined in two methodological frameworks involving statistical and multi-criteria decision making approaches. The efficiency of statistical and multi-criteria methods in FHSM were compared by using area under receiver operating characteristic (AUROC) curve, seed cell area index and frequency ratio. A database containing flood inventory maps and flood-related conditioning factors was established for this watershed. The flood inventory maps produced included 132 flood conditions, which were randomly classified into two groups, for training (70%) and validation (30%). Analytical hierarchy process (AHP) indicated that slope, distance to stream and land use/land cover are of key importance in flood occurrence in the study catchment. In validation results, the EBF model had a better prediction rate (0.987) and success rate (0.946) than FR, TOPSIS and VIKOR (prediction rate 0.917, 0.888, and 0.810; success rate 0.939, 0.904, and 0.735, respectively). Based on their frequency ratio and seed cell area index values, all models except VIKOR showed acceptable accuracy of classification.
Alireza Arabameri; Khalil Rezaei; Artemi Cerdà; Christian Conoscenti; Zahra Kalantari. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Science of The Total Environment 2019, 660, 443 -458.
AMA StyleAlireza Arabameri, Khalil Rezaei, Artemi Cerdà, Christian Conoscenti, Zahra Kalantari. A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Science of The Total Environment. 2019; 660 ():443-458.
Chicago/Turabian StyleAlireza Arabameri; Khalil Rezaei; Artemi Cerdà; Christian Conoscenti; Zahra Kalantari. 2019. "A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran." Science of The Total Environment 660, no. : 443-458.
In the studies of landslide susceptibility assessment which have been developed in recent years, statistical methods have increasingly been applied. Among all, the BLR (Binary Logistic Regression) certainly finds a more extensive application while MARS (Multivariate Adaptive Regression Splines), despite the good performance and the innovation of the strategies of analysis, only recently began to be employed as a statistical tool for predicting landslide occurrence. The purpose of this research was to evaluate the predictive performance and identify possible drawbacks of the two statistical techniques mentioned above, focusing in particular on the prediction of debris flows. To this aim, we employed an inventory of debris flows triggered by the passage of the hurricane IDA and the low-pressure system associated with it 96E, on November 7thand 8th2009 in the Caldera Ilopango, El Salvador (CA). Two validation strategies have been applied to both statistical techniques thus obtaining four models (BLR(I), MARS(I), BLR(II), MARS(II)) to be compared in pairs. Model performance was assessed in terms of AUC (area under the receiver operating characteristic (ROC) curve), Sensitivity, Specificity, Positive Prediction Value and Negative Prediction Value. Moreover, to evaluate the robustness of the modeling procedure, 50 replicates were created for each model and the standard deviation was calculated for each of them. The results show that both techniques allow for obtaining good or excellent performances so that it is not possible to define one of the two techniques as absolutely better. However, the validation procedure reveals slightly better performance of the MARS models, with greater sensitivity and greater discrimination among TNs.
Edoardo Rotigliano; Chiara Martinello; Valerio Agnesi; Christian Conoscenti. Evaluation of debris flow susceptibility in El Salvador (CA): a comparison between Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression (BLR). Hungarian Geographical Bulletin 2018, 67, 361 -373.
AMA StyleEdoardo Rotigliano, Chiara Martinello, Valerio Agnesi, Christian Conoscenti. Evaluation of debris flow susceptibility in El Salvador (CA): a comparison between Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression (BLR). Hungarian Geographical Bulletin. 2018; 67 (4):361-373.
Chicago/Turabian StyleEdoardo Rotigliano; Chiara Martinello; Valerio Agnesi; Christian Conoscenti. 2018. "Evaluation of debris flow susceptibility in El Salvador (CA): a comparison between Multivariate Adaptive Regression Splines (MARS) and Binary Logistic Regression (BLR)." Hungarian Geographical Bulletin 67, no. 4: 361-373.
Younes Garosi; Mohsen Sheklabadi; Hamid Reza Pourghasemi; Ali Asghar Besalatpour; Christian Conoscenti; Kristof Van Oost. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma 2018, 330, 65 -78.
AMA StyleYounes Garosi, Mohsen Sheklabadi, Hamid Reza Pourghasemi, Ali Asghar Besalatpour, Christian Conoscenti, Kristof Van Oost. Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping. Geoderma. 2018; 330 ():65-78.
Chicago/Turabian StyleYounes Garosi; Mohsen Sheklabadi; Hamid Reza Pourghasemi; Ali Asghar Besalatpour; Christian Conoscenti; Kristof Van Oost. 2018. "Comparison of differences in resolution and sources of controlling factors for gully erosion susceptibility mapping." Geoderma 330, no. : 65-78.
In the recent years, many researches dealt with the impact of human and climate change on the morpho‐evolution of Mediterranean catchments characterized by high ecological and cultural value. In this paper, we speculated how humans can influence hillslope degradation by reviewing the relationships between denudation processes and land use changes in some representative areas located in different Italian regions (i.e., Liguria, Tuscany, Basilicata and Sicily). The selected study cases are characterized by different climatic and geological features, land use and land management and can be considered indicative of the hillslope degradation issues that affected the Apennines during the last century. We compared and discussed the main outcomes from previous studies, with the aim of identifying the main drivers leading to hillslope degradation and to shed light on the role of human action. We revealed that hillslope degradation can be mainly related to deforestation for land reclamation, cropland abandonment and the increase of hazardous rainfall. Moreover, we focused on how human impact can have both positive and negative feedbacks. In some cases (e.g., badlands), the land levelling has produced an initial inhibition of land degradation, while after intensive agricultural practices accelerated soil depletion has occurred, favouring erosion processes. Analogously, terracing contrasted erosion until the entire terrace system was maintained but abandoned terraced slopes can increase the magnitude of geo‐hydrological phenomena in response to high‐intensity rainfall. On the other hand, both rural landscape and related erosional landforms can be appreciated as elements of landscape diversity and contribute to tourism development.
Pierluigi Brandolini; Giacomo Pepe; Domenico Capolongo; Chiara Cappadonia; Andrea Cevasco; Christian Conoscenti; Antonella Marsico; Francesca Vergari; Maurizio Del Monte. Hillslope degradation in representative Italian areas: Just soil erosion risk or opportunity for development? Land Degradation & Development 2018, 29, 3050 -3068.
AMA StylePierluigi Brandolini, Giacomo Pepe, Domenico Capolongo, Chiara Cappadonia, Andrea Cevasco, Christian Conoscenti, Antonella Marsico, Francesca Vergari, Maurizio Del Monte. Hillslope degradation in representative Italian areas: Just soil erosion risk or opportunity for development? Land Degradation & Development. 2018; 29 (9):3050-3068.
Chicago/Turabian StylePierluigi Brandolini; Giacomo Pepe; Domenico Capolongo; Chiara Cappadonia; Andrea Cevasco; Christian Conoscenti; Antonella Marsico; Francesca Vergari; Maurizio Del Monte. 2018. "Hillslope degradation in representative Italian areas: Just soil erosion risk or opportunity for development?" Land Degradation & Development 29, no. 9: 3050-3068.
In this work, we assessed gully erosion susceptibility in two adjacent cultivated catchments of Sicily (Italy) by employing multivariate adaptive regression splines and a set of geo-environmental variables. To explore the influence of hydrological connectivity on gully occurrence, we measured the changes of performance occurred when adding one by one nine predictors reflecting terrain connectivity to a base model that included contributing area and slope gradient. Receiver operating characteristic (ROC) curves and the area under the ROC curve were used to evaluate model performance. Gully predictive models were trained in both the catchments and submitted to internal (in the calibration catchment) and external (in the adjacent one) validation, using samples extracted both from all cells of the catchments and only from cells located along flow concentration axes. Model evaluation on the entire catchments shows outstanding predictive performance of models that either include or do not include the predictors selected to reflect potential hydrological connectivity. Conversely, area under the ROC curve values measured on flow concentration axes reveals that almost all the additional predictors improve the performance of the base model, but the most enhanced increase of accuracy occurs when upstream drainage density of each landscape position is included as predictor of gully occurrence. Copyright © 2017 John Wiley & Sons, Ltd.
Christian Conoscenti; Valerio Agnesi; Mariaelena Cama; Edoardo Rotigliano; Nathalie Alamaru Caraballo-Arias. Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity. Land Degradation & Development 2017, 29, 724 -736.
AMA StyleChristian Conoscenti, Valerio Agnesi, Mariaelena Cama, Edoardo Rotigliano, Nathalie Alamaru Caraballo-Arias. Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity. Land Degradation & Development. 2017; 29 (3):724-736.
Chicago/Turabian StyleChristian Conoscenti; Valerio Agnesi; Mariaelena Cama; Edoardo Rotigliano; Nathalie Alamaru Caraballo-Arias. 2017. "Assessment of Gully Erosion Susceptibility Using Multivariate Adaptive Regression Splines and Accounting for Terrain Connectivity." Land Degradation & Development 29, no. 3: 724-736.
The Capo San Vito peninsula is located along the north-westernmost sector of the Sicilian coastline. It is characterized by a complex geomorphological setting, where a large variety of coastal, gravity-induced and karst landforms allow the visitor to easily detect the interactions between Quaternary tectonics and climate changes as well as morphodynamic processes responsible for shaping the landscape. Thanks to natural reserves, the peninsula preserves a typical Mediterranean natural environment, marked by spectacular and suggestive landforms.
Valerio Agnesi; Christian Conoscenti; Cipriano Di Maggio; Edoardo Rotigliano. Geomorphology of the Capo San Vito Peninsula (NW Sicily): An Example of Tectonically and Climatically Controlled Landscape. World Geomorphological Landscapes 2017, 455 -465.
AMA StyleValerio Agnesi, Christian Conoscenti, Cipriano Di Maggio, Edoardo Rotigliano. Geomorphology of the Capo San Vito Peninsula (NW Sicily): An Example of Tectonically and Climatically Controlled Landscape. World Geomorphological Landscapes. 2017; ():455-465.
Chicago/Turabian StyleValerio Agnesi; Christian Conoscenti; Cipriano Di Maggio; Edoardo Rotigliano. 2017. "Geomorphology of the Capo San Vito Peninsula (NW Sicily): An Example of Tectonically and Climatically Controlled Landscape." World Geomorphological Landscapes , no. : 455-465.
Pantelleria is a volcanic island located in the Strait of Sicily, 95 km far from the Sicilian coastline and 67 km from Cape Bon (Tunisia). The volcanological history of the island begins approximately 324 ka BP and the last eruptive event was a submarine eruption that occurred on 1891 A.D. Eruptive activity was characterized by seven very intense explosive events, the latest being the Green Tuff (44 ka). They have all produced ignimbrite sheets that covered large sectors of the island. The landscape of the island mirrors the variety of the eruptive styles and their interplay with volcano-tectonics. The most evident geomorphological features are represented by: (i) the mantle-like distribution of the Green Tuff ignimbrite; (ii) the arcuate remnants of the two large caldera collapses, and (iii) the intracalderic scoria cones, lava domes and lava fields. A very dense distribution of dry walls, built since Roman times, perfectly integrate the volcanic landscape, preventing from erosion and rock falls.
Silvio G. Rotolo; Valerio Agnesi; Christian Conoscenti; Giovanni Lanzo. Pantelleria Island (Strait of Sicily): Volcanic History and Geomorphological Landscape. World Geomorphological Landscapes 2017, 479 -487.
AMA StyleSilvio G. Rotolo, Valerio Agnesi, Christian Conoscenti, Giovanni Lanzo. Pantelleria Island (Strait of Sicily): Volcanic History and Geomorphological Landscape. World Geomorphological Landscapes. 2017; ():479-487.
Chicago/Turabian StyleSilvio G. Rotolo; Valerio Agnesi; Christian Conoscenti; Giovanni Lanzo. 2017. "Pantelleria Island (Strait of Sicily): Volcanic History and Geomorphological Landscape." World Geomorphological Landscapes , no. : 479-487.
Soil erosion by water constitutes a serious problem affecting various countries. In the last few years, a number of studies have adopted statistical approaches for erosion susceptibility zonation. In this study, the Stochastic Gradient Treeboost (SGT) was tested as a multivariate statistical tool for exploring, analyzing and predicting the spatial occurrence of rill-interrill erosion and gully erosion. This technique implements the stochastic gradient boosting algorithm with a tree-based method. The study area is a 9.5 km2 river catchment located in central-northern Sicily (Italy), where water erosion processes are prevalent, and affect the agricultural productivity of local communities. In order to model soil erosion by water, the spatial distribution of landforms due to rill-interrill and gully erosion was mapped and 12 environmental variables were selected as predictors. Four calibration and four validation subsets were obtained by randomly extracting sets of negative cases, both for rill-interrill erosion and gully erosion models. The results of validation, based on receiving operating characteristic (ROC) curves, showed excellent to outstanding accuracies of the models, and thus a high prediction skill. Moreover, SGT allowed us to explore the relationships between erosion landforms and predictors. A different suite of predictor variables was found to be important for the two models. Elevation, aspect, landform classification and land-use are the main controlling factors for rill-interrill erosion, whilst the stream power index, plan curvature and the topographic wetness index were the most important independent variables for gullies. Finally, an ROC plot analysis made it possible to define a threshold value to classify cells according to the presence/absence of the two erosion processes. Hence, by heuristically combining the resulting rill-interrill erosion and gully erosion susceptibility maps, an integrated water erosion susceptibility map was created. The adopted method offers the advantages of an objective and repeatable procedure, whose result is useful for local administrators to identify the areas most susceptible to water erosion and best allocate resources for soil conservation strategies.
Silvia Eleonora Angileri; Christian Conoscenti; Volker Hochschild; Michael Maerker; Edoardo Rotigliano; Valerio Agnesi. Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy). Geomorphology 2016, 262, 61 -76.
AMA StyleSilvia Eleonora Angileri, Christian Conoscenti, Volker Hochschild, Michael Maerker, Edoardo Rotigliano, Valerio Agnesi. Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy). Geomorphology. 2016; 262 ():61-76.
Chicago/Turabian StyleSilvia Eleonora Angileri; Christian Conoscenti; Volker Hochschild; Michael Maerker; Edoardo Rotigliano; Valerio Agnesi. 2016. "Water erosion susceptibility mapping by applying Stochastic Gradient Treeboost to the Imera Meridionale River Basin (Sicily, Italy)." Geomorphology 262, no. : 61-76.
A statistical approach was employed to model the spatial distribution of rainfall-triggered landslides in two areas in Sicily (Italy) that occurred during the winter of 2004–2005. The investigated areas are located within the Belice River basin and extend for 38.5 and 10.3 km2, respectively. A landslide inventory was established for both areas using two Google Earth images taken on October 25th 2004 and on March 18th 2005, to map slope failures activated or reactivated during this interval. Geographic Information Systems (GIS) were used to prepare 5 m grids of the dependent variables (absence/presence of landslide) and independent variables (lithology and 13 DEM-derivatives). Multivariate Adaptive Regression Splines (MARS) were applied to model landslide susceptibility whereas receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate model performance. To evaluate the robustness of the whole procedure, we prepared 10 different samples of positive (landslide presence) and negative (landslide absence) cases for each area. Absences were selected through two different methods: (i) extraction from randomly distributed circles with a diameter corresponding to the mean width of the landslide source areas; and (ii) selection as randomly distributed individual grid cells. A comparison was also made between the predictive performances of models including and not including the lithology parameter. The models trained and tested on the same area demonstrated excellent to outstanding fit (AUC > 0.8). On the other hand, predictive skill decreases when measured outside the calibration area, although most of the landslides occur where susceptibility is high and the overall model performance is acceptable (AUC > 0.7). The results also showed that the accuracy of the landslide susceptibility models is higher when lithology is included in the statistical analysis. Models whose absences were selected using random circles showed a significantly better performance when learning and validation samples were extracted from the same area; whereas, conversely, no significant difference was observed when testing the models outside the training area.
Christian Conoscenti; Edoardo Rotigliano; Mariaelena Cama; Nathalie Almaru Caraballo Arias; Luigi Lombardo; Valerio Agnesi. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy. Geomorphology 2016, 261, 222 -235.
AMA StyleChristian Conoscenti, Edoardo Rotigliano, Mariaelena Cama, Nathalie Almaru Caraballo Arias, Luigi Lombardo, Valerio Agnesi. Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy. Geomorphology. 2016; 261 ():222-235.
Chicago/Turabian StyleChristian Conoscenti; Edoardo Rotigliano; Mariaelena Cama; Nathalie Almaru Caraballo Arias; Luigi Lombardo; Valerio Agnesi. 2016. "Exploring the effect of absence selection on landslide susceptibility models: A case study in Sicily, Italy." Geomorphology 261, no. : 222-235.
The main assumption on which landslide susceptibility assessment by means of stochastic modelling lies is that the past is the key to the future. As a consequence, a stochastic model able to classify past known landslide events should be able to predict a future unknown scenario as well. However, storm-triggered multiple debris flow events in the Mediterranean region could pose some limits on the operative validity of such an expectation, as they are typically resultant of a randomness in time recurrence and magnitude and a great spatial variability, even at the scale of small catchments. This is the case for the 2007 and 2009 storm events, which recently hit north-eastern Sicily with different intensities, resulting in largely different disaster scenarios. The study area is the small catchment of the Itala torrent (10 km2), which drains from the southern Peloritani Mountains eastward to the Ionian Sea, in the territory of the Messina province (Sicily, Italy). Landslides have been mapped by integrating remote and field surveys, producing two event inventories which include 73 debris flows, activated in 2007, and 616 debris flows, triggered by the 2009 storm. Logistic regression was applied in order to obtain susceptibility models which utilize a set of predictors derived from a 2 m cell digital elevation model and a 1 : 50 000 scale geologic map. The research topic was explored by performing two types of validation procedures: self-validation, based on the random partition of each event inventory, and chrono-validation, based on the time partition of the landslide inventory. It was therefore possible to analyse and compare the performances both of the 2007 calibrated model in predicting the 2009 debris flows (forward chrono-validation), and vice versa of the 2009 calibrated model in predicting the 2007 debris flows (backward chrono-validation). Both of the two predictions resulted in largely acceptable performances in terms of fitting, skill and reliability. However, a loss of performance and differences in the selected predictors arose between the self-validated and the chrono-validated models. These are interpreted as effects of the non-linearity in the domain of the trigger intensity of the relationships between predictors and slope response, as well as in terms of the different spatial paths of the two triggering storms at the catchment scale.
M. Cama; L. Lombardo; C. Conoscenti; V. Agnesi; E. Rotigliano. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy). Natural Hazards and Earth System Sciences 2015, 15, 1785 -1806.
AMA StyleM. Cama, L. Lombardo, C. Conoscenti, V. Agnesi, E. Rotigliano. Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy). Natural Hazards and Earth System Sciences. 2015; 15 (8):1785-1806.
Chicago/Turabian StyleM. Cama; L. Lombardo; C. Conoscenti; V. Agnesi; E. Rotigliano. 2015. "Predicting storm-triggered debris flow events: application to the 2009 Ionian Peloritan disaster (Sicily, Italy)." Natural Hazards and Earth System Sciences 15, no. 8: 1785-1806.