1,721,093 research outputs found
Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting
Full text linkLandslide susceptibility assessment using machine learning models is a popular and consolidated approach worldwide. The main constraint of susceptibility maps is that they are not adequate for temporal assessments: they are generated from static predisposing factors, allowing only a spatial prediction of landslides. Recently, some methodologies have been proposed to provide spatiotemporal landslides prediction starting from machine learning algorithms (e.g., combining susceptibility maps with rainfall thresholds), but the attempt to obtain a dynamic landslide probability map directly by applying machine learning models is still in the preliminary phase. This work provides a contribution to fix this gap, combining in a Random Forest (RF) algorithm a static indicator of the spatial probability of landslide occurrence (i.e., a classical susceptibility index) and a number of dynamic variables (i.e., seasonality and the rainfall amount cumulated over different reference periods). The RF implementation used in this work allows the calculation of the Out-of-Bag Error and depicts Partial Dependence Plots, two indices that were used to quantify the variables' importance and to comprehend if the model outcomes are consistent with the triggering mechanism observed in the case of study (Metropolitan City of Florence, Italy). The goal of this research is not to set up a landslide probability map, but to 1) understand how to populate training and test datasets with observations sampled over space and time, 2) assess which rainfall variables are statistically more relevant for the identification of the time and location of landslides, and 3) test the dynamic application of RF in a forecasting model for the spatiotemporal prediction of landslides. The proposed dynamic methodology shows encouraging results, consistent with the actual knowledge of the physical mechanism of the triggering of shallow landslides (mainly influenced by short and intense rainfalls) and identifies some benchmark configurations that represents a promising starting point for future regional-scale applications of machine learning models to dynamic landslide probability assessment and early warning
The PLANTARIO project: a useful tool for hydraulic policy, urban planning and flood risk assessment
No full textPoste
Improving basin scale shallow landslide modelling using reliable soil thickness maps
Full text linkSoil thickness is a well-known factor controlling shallow landsliding. Notwithstanding, its spatial organisation over large areas is poorly understood, and in basin scale slope analyses it is often established using simple methods. In this paper, we apply five different soil thickness models in two test sites, and we use the obtained soil thickness maps to feed a slope stability model. Validation quantifies how errors in soil thickness influence the resulting factor of safety and points out which method grants the best results. In particular, in our cases, slope-derived soil thickness patterns produced the worst slope stability assessment, while the use of reliable soil thickness maps obtained by means of a more complex geomorphologically indexed model improved shallow landslides modelling. © 2011 The Author(s)
A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling
No full textClassification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling
Double-threshold validation tool (DTVT): From landslide hazard maps to operational early warning systems
No full textIl Plantario delle aste fluviali in Provincia di Firenze - Un catasto fiumi per il controllo e la mitigazione delle pericolosità di collasso arginale dell’Arno e dei suoi principali affluenti
No full textIl Plantario delle Aste fluviali è il risultato di un progetto, svolto in collaborazione con il Dipartimento di Scienze della Terra dell’Università di Firenze, consistente nella mappatura e informatizzazione in ambiente GIS (acronimo di Geographical Information System) di tutte le emergenze sia fisiografiche, sia inerenti l’edificato, ricadenti nelle pertinenze fluviali dell’Arno e dei suoi principali affluenti per i tratti di rigurgito di piena o comunque recanti Opere Idrauliche di II Categoria ai sensi del RD 523/1904
Urban planning, flood risk and public policy: The case of the Arno River, Firenze, Italy
No full textUrban planning and hydraulic risk management are a worldwide necessity which is best achieved when natural and artificial elements located closely to watercourses are known in great detail. A geodatabase is a practical tool to store and manage such information. When working at small scales, however, any well established methodology exists to map the position and the height of the various elements with centimetric accuracy. For this purpose we propose a methodology that we tested on the Arno river (Italy) and its most urbanized tributaries, a demonstrative case of hydrological risk around large fluvial systems. We surveyed 116 km of river traits to collect GPS measurements and information about all the natural and artificial elements connected to hydraulic risk and fluvial dynamics. The mapped elements include (but are not limited to) buildings, assets, bridges, hydraulic works, weirs, drainage outlets, dikes, riverbanks, structural damages, fluvial bars and eroding banks. All these elements were mapped with high accuracy, in particular a local geoid model, related only to the study area, was developed to obtain orthometric heights affected with errors ≤0.05 m. Consequently a GIS geodatabase was built to visualize the spatial distribution of the mapped elements and to store a series of technical data, including the present preservation condition for man-made objects. The geodatabase provides an overview of the territories connected with the fluvial dynamics, highlighting that in the studied territory, the more is urbanized, the more it is exposed to hydraulic risk. In a similar context, the geodatabase itself represents a useful tool for the management of the hydrological risk and for hydraulic policy and urban planning. © 2011 Elsevier Ltd
Brief communication: Using averaged soil moisture estimates to improve the performances of a regional-scale landslide early warning system
Full text linkWe communicate the results of a preliminary investigation aimed at improving a state-of-the-art RSLEWS (regional-scale landslide early warning system) based on rainfall thresholds by integrating mean soil moisture values averaged over the territorial units of the system. We tested two approaches. The simplest can be easily applied to improve other RSLEWS: it is based on a soil moisture threshold value under which rainfall thresholds are not used because landslides are not expected to occur. Another approach deeply modifies the original RSLEWS: thresholds based on antecedent rainfall accumulated over long periods are substituted with soil moisture thresholds. A back analysis demonstrated that both approaches consistently reduced false alarms, while the second approach reduced missed alarms as well
Energia pulita e rinnovabile dal fiume Arno. I risultati di uno studio di fattibilità in provincia di Firenze
No full textThe paper considers the possibility to use the transversal hydraulic works to produce locally the hydroelectric powe
Landslides triggered by rainfall: A semi-automated procedure to define consistent intensity-duration thresholds
No full textIn this paper, a methodology to automate and standardize the identification of rainfall intensity-duration thresholds for landslides triggering is presented. A newly developed software called MaCumBA (MAssive CUMulative Brisk Analyzer) can be used to analyze rain-gauge records, extract the intensities (I) and durations (D) of the rainstorms associated with the initiation of landslides, plot these values on a diagram and identify thresholds that define the lower bounds of the aforementioned I- D values. Because the methodology is automated, it is possible to process a relevant amount of data in short times, while allowing for user decision input. A back analysis using data from past events that did not trigger landslides can be used to identify the threshold conditions associated with the least amount of false alarms. We applied the methodology in two test sites. A validation procedure returned satisfactory results, demonstrating the potential utility of the proposed methodology in the development of landslide warning systems. © 2013 Elsevier Ltd
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