1,720,999 research outputs found
Combining Experts’ Probabilities via the Product of Odds
No full textWe resolve a useful formulation of the question how a statistician can coherently incorporate the information in a consulted expert’s probability assessment for an event into his/her own posterior probability assertion. Using a framework that recognises the total information available as composed of units available only to each of them along with units available to both, we show that: a sufficient statistic for the all the information available to both the expert and the statistician is the product of their odds ratios in favour of the event; the geometric mean of their two probabilities specifies a contour of pairs of assertions in the unit-square that yield the same posterior probability; the information-combining function is parameterised by an unknown probability for the event conditioned only on the unspecified information that is common to both the statistician and the expert; and that an assessable mixing distribution over this unspecified probability allows an integrable mixture distribution to represent a computable posterior probability. The exact results allow the identification of the subclass of coherent probabilities that are externally Bayesian operators. This subclass is equivalent to that of probabilities that honour the principles of uniformity and compromise
INSYDE-content: a synthetic, multi-variable flood damage model for household contents
Full text linkThis paper introduces INSYDE-content, a novel, probabilistic, multi-variable synthetic model designed to estimate flood damage for household contents on a component-by-component basis. The model addresses a critical gap in current modeling tools, which often overlook the significance of household contents in overall damage assessments. Developed through an expert-based approach and grounded in the scientific and technical literature, INSYDE-content leverages desk-based data to characterize model features, including uncertainty treatment arising from incomplete input data. A validation test on two historical flood events and a sensitivity analysis are performed to assess the model's performance and explore the contribution of input variables to damage estimation, confirming its robustness and interpretability. For illustrative purposes, in this study INSYDE-content has been tailored to the specific hazard, vulnerability and exposure characteristics of Northern Italy; nonetheless, its adaptable structure supports broader applicability across diverse regional settings, provided suitable customization is applied
Exploring the compound nature of coastal flooding by tropical cyclones: A machine learning framework
Full text linkModeling inundation patterns resulting from compound flooding induced by tropical cyclones presents significant challenges due to the complex interplay of drivers and features affecting inundation mechanisms. This study introduces a machine learning framework designed to optimize the prediction of inundation depth by balancing model performance, computational costs and efforts for input data retrieval. Starting from a comprehensive, physics-informed identification of the potential explanatory variables, including features that capture local flood dynamics, as well as topological and geographical characteristics, the proposed methodology leverages a feature selection process based on permutation importance, which emphasizes the reduction in the number of inputs to streamline the modeling process without compromising accuracy. The framework has been tested using Hurricane Harvey as a case study. The analysis revealed performance in inundation depth prediction comparable to that of traditional hydrodynamic models available in the literature. Results demonstrated that focusing on the most informative features improves both model performance and efficiency, thus highlighting the need for careful feature selection for region-specific implementation of data-driven approaches for inundation depth prediction
Leveraging data driven approaches for enhanced tsunami damage modelling: Insights from the 2011 Great East Japan event
No full textThis study aims at developing an empirical, multi-variable tsunami damage model for buildings, based on machine-learning algorithms which leverage about 250.000 ex-post data surveyed by the Japanese Ministry of Land, Infrastructure and Transportation after the 2011 Great East Japan event in the Tōhoku region. By implementing simple geospatial tools, the dataset is integrated with additional explanatory variables, including, among others, factors accounting for the mutual interaction between the inundated structures. Tests on models’ sensitivity to the number and type of input features used for model development reveal the importance, on the predictive performance, of considering usually neglected mechanisms like the shielding effect and the debris impact generation. The analysis for the potential spatial transferability indicates a reduction in the accuracy, thus suggesting a better suitability of empirical models for descriptive purposes, limiting their predictive ability only to region-specific cases
Climate variability and perennial fruit crop yields: insights from Trentino-Alto Adige, Northern Italy
Full text linkEffective adaptation planning for perennial crops, particularly in mountain contexts with climate-sensitive agroecosystems, requires a robust understanding of yield responses to climatic variability. Based on long-term data and a methodological framework covering both linear regression and machine learning techniques, the present study investigates the influence of interannual variability in agro-climatic indices on apple and grape yields in Trentino-Alto Adige, an alpine region in northern Italy. Results reveal that apple yields are more consistently influenced by climate variability than grape yields, with frost occurrence and heat-related indices emerging as key predictors. The machine learning approach, through variable importance metrics and individual conditional expectation plots, provides insights into nonlinear yield responses to critical climatic thresholds, such as sharp declines beyond a certain number of frost days or plateauing gains under sustained heat accumulation. Conversely, grape yields exhibit more heterogeneous and buffered responses, reflecting more complex interactions with climatic conditions. Overall, the study highlights the added value of data-driven approaches with physical interpretability for capturing intricate climate–yield relationships. In regions increasingly exposed to climate pressures, such as the alpine valleys, these tools can support the development of targeted strategies to sustain long-term crop productivity
Regional flood risk analysis for agricultural crops: Insights from the implementation of AGRIDE-c in central Italy
No full textThe development of reliable models for different exposed sectors is key for obtaining more comprehensive flood damage and risk assessments. This study discusses the implementation of the recent conceptual damage model for crops, AGRIDE-c, to a central region of Italy, in order to demonstrate its potential for application and related challenges from a flood risk assessment perspective at the river basin scale. The process for adaptation of model's components to the local characteristics of the investigated area is first described, including the extension of the analytical flood loss model to annual vegetables and perennial crops (here exemplified for grapevine) not covered in the original AGRIDE-c. Moreover, being a multi-variable damage model, with also some input parameters of uncertain estimation, this study examines the implications of modelling assumptions on damage and risk results to obtain general insights from a modeller's perspective. Finally, the issue of spatial transferability is discussed by comparing model outcomes for cereals in the analysed region and in the Po Plain, for which AGRIDE-c was originally developed. The results highlight the importance, in practical applications, (i) to determine a confidence band for loss estimates that can help for more informed decision making processes and (ii) when transferring models, to verify the similarity between the physical and economic contexts of implementation (and, if necessary, adjust model's input and/or assumptions), in order to prevent inaccurate or distorted damage and risk estimations
Machine learning and hydrodynamic proxies for enhanced rapid tsunami vulnerability assessment
Full text linkCoastal communities in various regions of the world are exposed to risk from tsunami inundation, requiring reliable modeling tools for implementing effective disaster preparedness and management strategies. This study advocates for comprehensive multi-variable models and emphasizes the limitations of traditional univariate fragility functions by leveraging a large, detailed dataset of ex-post damage surveys for the 2011 Great East Japan tsunami, hydrodynamic modeling of the event, and advanced machine learning techniques. It investigates the complex interplay of factors influencing building vulnerability to tsunami, with a specific focus on the hydrodynamic effects associated to tsunami propagation on land. Novel synthetic variables representing shielding and debris impact mechanisms prove to be suitable proxies for water velocity, offering a practical solution for rapid damage assessments, especially in post-event scenarios or large-scale analyses. Machine learning then emerges as a promising approach to tackle the complexities of vulnerability assessment, while providing valuable and interpretable insights.Hydrodynamic modelling and machine learning-based methods can effectively model tsunami damage mechanisms and represent an improvement over traditional univariate fragility functions for vulnerability assessments
Are We Correctly Using Discharge Coefficients for Side Weirs? Insights from a Numerical Investigation
No full textA key issue in the design of side weirs is the experimental assessment of the discharge coefficient. This can be determined by laboratory measurements of discharge and water depths at the up- and downstream ends of the weir by using De Marchi’s approach, consisting in the solution of the 1D dynamic equation of spatially varied steady flow with non-uniform discharge, under the assumption of energy conservation. This study originates from a recent alarming proliferation of works that evaluate the discharge coefficient for side weirs without clearly explaining the experimental methodology and/or even incorrectly applying modelling approaches, thus generating possible misinterpretations of the results. In this context, the present paper aims to highlight the effects of using oversimplified and/or heterogenous models (relying on different assumptions) for the experimental determination of the discharge coefficient for side weirs. Furthermore, a sensitivity analysis is performed to detect the most influencing hydraulic and geometric parameters on each considered model. The overall results clearly indicate the wrongness of using or building not homogeneous discharge coefficient datasets to obtain and/or compare predictive experimental discharge coefficient formulas. We finally show how neural networks could provide a possible solution to these heterogeneity issues
A machine learning approach for hurricane-induced flood depth estimation: a case study on Hurricane Harvey
No full textExperimental analysis on flow resistance for different macro-scale roughness arrangements
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