Space-time variability modelling of landslide susceptibility for strategic infrastructure under changing climate scenarios: The case study of the mega clean energy transmission network (Yangtze River Basin, China) - Bijing Jin; Chao Zhou; Shuhao Liu; Taorui Zeng; Francesco Caleca; Zheng Zhao; Lei Gui; Binbin Zhao; Segoni Samuele; Kunlong Yin; Dario Peduto; Veronica Tofani (Engineering Geology, 2026)

Advancing landslide early warning in the Indian Himalayas: SIGMA-based rainfall thresholds for Chamoli district - Kunal Gupta; Neelima Satyam; Samuele Segoni (Natural Hazards, 2026)

Quantitative landslide risk assessment for Italy - Francesco Caleca; Veronica Tofani; Federico Raspini; Samuele Segoni; Nicola Casagli (Landslides, 2025)
Landslides are among the most prevalent geohazards worldwide, leading to significant loss of human lives and extensive property damage. Italy stands out as one of the European countries most frequently affected by landslides, resulting in significant consequences for its community. Despite numerous studies on landslides across Italy, including efforts in susceptibility modeling and forecasting, a comprehensive national-scale quantitative risk assessment is still absent. This gap reflects a broader trend in landslide risk research, where most studies are confined to limited areas due to methodological and data constraints. In this contribution, we present a quantitative landslide risk assessment for Italy (300,000 km $$^2$$ 2 ), integrating the key components: landslide hazard, landslide intensity, vulnerability, and exposure of elements at risk. We express landslide risk as potential economic losses to human properties, specifically buildings and land use. The analysis considers three primary landslide types: slow-moving landslides, fast-moving landslides, and rockfalls. Furthermore, the assessment incorporates a temporal dimension by estimating potential losses over 2-year, 5-year, and 10-year scenarios. The results reveal that slow-moving landslides generally pose the greatest risk with mean potential economic losses of 0.40 Million € in the 10-year scenario.

Understanding the spatial patterns of major geo-hydrological disasters in Italy using the CatBoost algorithm - Samuele Segoni; Nicola Nocentini; Rajendran Shobha Ajin; Alessio Gatto; Riccardo Fanti (Science of The Total Environment, 2025)

Leveraging artificial intelligence to quantify slope rainfall sensitivity for refining regional landslide rainfall thresholds - Yuhang Zhu; Kunlong Yin; Ye Li; Haoran Yang; Hong Chen; Chao Zhou; Samuele Segoni (Engineering Geology, 2025)

A novel prototype national-scale landslide nowcasting system for Italy combining rainfall thresholds and risk indicators - Samuele Segoni; Nicola Nocentini; Francesco Barbadori; Camilla Medici; Alessio Gatto; Ascanio Rosi; Nicola Casagli (Landslides, 2025)
Regional- and national-scale landslide warning systems are usually based on rainfall thresholds that forecast the possibility of landslide occurrence over wide spatial units called alert zones (AZs). This work proposes a substantial improvement of the state-of-the-art by combining the rainfall threshold outcomes with a set of spatially explicit risk indicators aggregated at the municipality level. The combination of these two different techniques is performed by means of a dynamic matrix, which was purposely calibrated to provide an output in the form of five possible levels of risk (from R0 to R4), which are connected to the growing intensity of expected impacts and a pre-defined confidence in issuing warnings without omitting alarms. Italy (about 300,000 km 2 ) is used as a case study, producing a set of rainfall thresholds differentiated for 150 AZs and providing a specific calibration of the dynamic risk matrix for each of them. The verification of the matrix outputs was satisfactory as no AZs experienced landslides at the R0 level; only two of them had more than 10% of landslides at the R1 level, and most of the AZs had more than 90% of the landslides in the R2 to R4 risk classes. A comparison with a nation-wide dataset of very severe hydrogeological disasters further corroborated the consistency of the model outputs with real scenarios, as most part of the impacts occurred in places and times when the matrix outputs were at the highest levels. The proposed methodology represents a reliable improvement for state-of-the-art territorial warning systems, as it brings two main advances: the spatial resolution is greatly improved, as the basic spatial unit for warning is downscaled from AZs to municipalities (whose average extension, in Italy, is about 1770 and 38 km 2 , respectively); second, the outputs can better address the needs of landslide emergency management, as the warning are specifically addressed to small areas based on the expected impacts (since risk indicators are used in the dynamic matrices), rather than on the mere probability of landslide occurrence.

Shifting from traditional landslide occurrence modeling to scenario estimation with a “glass-box” machine learning - Francesco Caleca; Pierluigi Confuorto; Federico Raspini; Samuele Segoni; Veronica Tofani; Nicola Casagli; Sandro Moretti (Science of The Total Environment, 2024)

Regional-scale spatiotemporal landslide probability assessment through machine learning and potential applications for operational warning systems: a case study in Kvam (Norway) - Nicola Nocentini; Ascanio Rosi; Luca Piciullo; Zhongqiang Liu; Samuele Segoni; Riccardo Fanti (Landslides, 2024)
The use of machine learning models for landslide susceptibility mapping is widespread but limited to spatial prediction. The potential of employing these techniques in spatiotemporal landslide forecasting remains largely unexplored. To address this gap, this study introduces an innovative dynamic (i.e., space–time-dependent) application of the random forest algorithm for evaluating landslide hazard (i.e., spatiotemporal probability of landslide occurrence). An area in Norway has been chosen as the case study because of the availability of a comprehensive, spatially, and temporally explicit rainfall-induced landslide inventory. The applied methodology is based on the inclusion of dynamic variables, such as cumulative rainfall, snowmelt, and their seasonal variability, as model inputs, together with traditional static parameters such as lithology and morphologic attributes. In this study, the variables’ importance was assessed and used to interpret the model decisions and to verify that they align with the physical mechanism responsible for landslide triggering. The algorithm, once trained and tested against landslide and non-landslide data sampled over space and time, produced a model predictor that was subsequently applied to the entire study area at different times: before, during, and after specific landslide events. For each selected day, a specific and space–time-dependent landslide hazard map was generated, then validated against field data. This study overcomes the traditional static applications of machine learning and demonstrates the applicability of a novel model aimed at spatiotemporal landslide probability assessment, with perspectives of applications to early warning systems.

A comparative study of empirical and machine learning approaches for soil thickness mapping in the Joshimath region (India) - Kunal Gupta; Neelima Satyam; Samuele Segoni (CATENA, 2024)

Optimization of rainfall thresholds for landslide early warning through false alarm reduction and a multi-source validation - Nicola Nocentini; Camilla Medici; Francesco Barbadori; Alessio Gatto; Rachele Franceschini; Matteo del Soldato; Ascanio Rosi; Samuele Segoni (Landslides, 2024)
This study proposes an innovative approach to develop a regional-scale landslide forecasting model based on rainfall thresholds optimized for operational early warning. In particular, it addresses two main issues that usually hinder the operational implementation of this kind of models: (i) the excessive number of false alarms, resulting in civil protection system activation without any real need, and (ii) the validation procedure, usually performed over periods too short to guarantee model reliability. To overcome these limitations, several techniques for reducing the number of false alarms were applied in this study, and a multiple validation phase was conducted using data from different sources. An intensity-duration threshold system for each of the five alert zones composing the Liguria region (Italy) was identified using a semiautomatic procedure called MaCumBA, considering three levels of criticality: low, moderate, and high. The thresholds were developed using a landslide inventory collected from online newspapers by a data mining technique called SECaGN. This method was chosen to account for only those events that echo on the Internet and therefore impact society, ignoring landslides occurred in remote areas, not of interest for civil protection intervention, which would adversely affect the model performance because they would result in false alarms. A calibration phase was performed to minimize the impact of false alarms, allowing at least one false alarm per year over the moderate criticality level. In addition, an innovative approach to include antecedent rainfall as the third dimension of the intensity-duration thresholds was applied, generating a consistent reduction in false alarms. The results were validated through an independent landslide inventory and were compared with (i) the alert issued by the regional civil protection agency to observe the improvements achieved with the proposed model and to evaluate to what extent the proposed model is consistent with the assessments of the civil protection and (ii) a dataset of the national states of emergency to verify the suitability of the developed thresholds for alerting citizens. The thresholds obtained showed high predictive capabilities, confirming their suitability for implementation in an operational landslide early warning system.

A prototype landslide early warning system in Rize (Turkey): analyzing recent impacts to design a safer future - Samuele Segoni; Yusuf Serengil; Fatih Aydin (Landslides, 2023)

Characteristics of landslides and debris flows triggered by extreme rainfall in Daoshi Town during the 2019 Typhoon Lekima, Zhejiang Province, China - Xin Liang; Samuele Segoni; Kunlong Yin; Juan Du; Bo Chai; Veronica Tofani; Nicola Casagli (Landslides, 2022)

A methodological approach of QRA for slow-moving landslides at a regional scale - Francesco Caleca; Veronica Tofani; Samuele Segoni; Federico Raspini; Ascanio Rosi; Marco Natali; Filippo Catani; Nicola Casagli (Landslides, 2022)
Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving landslides, aiming at national replicability. This procedure has been applied at the basin scale in the Arno River basin (9100 km 2 , Central Italy), where most landslides are slow-moving. QRA is based on the application of the equation risk = hazard (H) × vulnerability (V) × exposure (E) and on the use of open data with uniform characteristics at the national scale. The study area was divided into a grid with a 1 km 2 cell size, and for each cell, the parameters necessary for the risk assessment were calculated. The obtained results show that the total risk of the study area amounts to approximately 7 billion €. The proposed methodology presents several novelties in the risk assessment for the regional/national scale of the analysis, mainly concerning the identification of the datasets and the development of new methodologies that could be applicable over such large areas. The present work demonstrates the feasibility of the methodology and discusses the obtained results.

Developing a prototype landslide early warning system for Darjeeling Himalayas using SIGMA model and real-time field monitoring - Minu T. Abraham; Neelima Satyam; Biswajeet Pradhan; Samuele Segoni; Abdullah Alamri (Geosciences Journal, 2022)

Definition of 3D rainfall thresholds to increase operative landslide early warning system performances - Ascanio Rosi; Samuele Segoni; Vanessa Canavesi; Antonio Monni; Angela Gallucci; Nicola Casagli (Landslides, 2021)
Intensity–duration rainfall thresholds are commonly used in regional-scale landslide warning systems. In this manuscript, 3D thresholds are defined also considering the mean rainfall amount fallen in each alert zone (MeAR, mean areal rainfall) in Emilia Romagna region (Northern Italy). In the proposed 3D approach, thresholds are represented by a plane instead of a line, and the third dimension allows to indirectly account for the influence of complex rainfall patterns. MeAR values are calculated according to different time periods ranging from 7 to 30 days, and all threshold parameters are calibrated independently for the 8 alert zones in which the region is divided. The approach was validated and compared with classical intensity–duration thresholds, finding that the 3D threshold may be used to get better performances, especially in terms of a consistent reduction of false alarms:− 20 to − 86%, depending on the alert zone and the selected MeAR duration. These results open new encouraging perspectives for the development of the regional warning system that is operated in the study area.

Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization - Samuele Segoni; Giulio Pappafico; Tania Luti; Filippo Catani (Landslides, 2020)
The literature about landslide susceptibility mapping is rich of works focusing on improving or comparing the algorithms used for the modeling, but to our knowledge, a sensitivity analysis on the use of geological information has never been performed, and a standard method to input geological maps into susceptibility assessments has never been established. This point is crucial, especially when working on wide and complex areas, in which a detailed geological map needs to be reclassified according to more general criteria. In a study area in Italy, we tested different configurations of a random forest–based landslide susceptibility model, accounting for geological information with the use of lithologic, chronologic, structural, paleogeographic, and genetic units. Different susceptibility maps were obtained, and a validation procedure based on AUC (area under receiver-operator characteristic curve) and OOBE (out of bag error) allowed us to get to some conclusions that could be of help for in future landslide susceptibility assessments. Different parameters can be derived from a detailed geological map by aggregating the mapped elements into broader units, and the results of the susceptibility assessment are very sensitive to these geology-derived parameters; thus, it is of paramount importance to understand properly the nature and the meaning of the information provided by geology-related maps before using them in susceptibility assessment. Regarding the model configurations making use of only one parameter, the best results were obtained using the genetic approach, while lithology, which is commonly used in the current literature, was ranked only second. However, in our case study, the best prediction was obtained when all the geological parameters were used together. Geological maps provide a very complex and multifaceted information; in wide and complex area, this information cannot be represented by a single parameter: more geology-based parameters can perform better than one, because each of them can account for specific features connected to landslide predisposition.

A step beyond landslide susceptibility maps: a simple method to investigate and explain the different outcomes obtained by different approaches - Ting Xiao; Samuele Segoni; Lixia Chen; Kunlong Yin; Nicola Casagli (Landslides, 2020)
Landslide susceptibility assessment is vital for landslide risk management and urban planning, and the scientific community is continuously proposing new approaches to map landslide susceptibility, especially by hybridizing state-of-the-art models and by proposing new ones. A common practice in landslide susceptibility studies is to compare (two or more) different models in terms of AUC (area under ROC curve) to assess which one has the best predictive performance. The objective of this paper is to show that the classical scheme of comparison between susceptibility models can be expanded and enriched with substantial geomorphological insights by focusing the comparison on the mapped susceptibility values and investigating the geomorphological reasons of the differences encountered. To this aim, we used four susceptibility maps of the Wanzhou County (China) obtained with four different classification methods (namely, random forest, index of entropy, frequency ratio, and certainty factor). A quantitative comparison of the susceptibility values was carried out on a pixel-by-pixel basis, to reveal systematic spatial patterns in the differences among susceptibility maps; then, those patterns were put in relation with all the explanatory variables used in the susceptibility assessments. The lithological and morphological features of the study area that are typically associated to underestimations and overestimations of susceptibility were identified. The results shed a new light on the susceptibility models, identifying systematic errors that could be probably associated either to shortcomings of the models or to distinctive morphological features of the test site, such as nearly flat low altitude areas near the main rivers, and some lithological units.

A review of the recent literature on rainfall thresholds for landslide occurrence - Samuele Segoni; Luca Piciullo; Stefano Luigi Gariano (Landslides, 2018)

Rainfall thresholds for rainfall-induced landslides in Slovenia - Ascanio Rosi; Tina Peternel; Mateja Jemec-Auflič; Marko Komac; Samuele Segoni; Nicola Casagli (Landslides, 2016)

Integration of rainfall thresholds and susceptibility maps in the Emilia Romagna (Italy) regional-scale landslide warning system - Samuele Segoni; Daniela Lagomarsino; Riccardo Fanti; Sandro Moretti; Nicola Casagli (Landslides, 2015)

Statistical and environmental analyses for the definition of a regional rainfall threshold system for landslide triggering in Tuscany (Italy) - Ascanio Rosi; Samuele Segoni; Filippo Catani; Nicola Casagli (Journal of Geographical Sciences, 2012)

Improving basin scale shallow landslide modelling using reliable soil thickness maps - Samuele Segoni; Guglielmo Rossi; Filippo Catani (Natural Hazards, 2012)