scholarly journals Modelling Prospective Flood Hazard in a Changing Climate, Benevento Province, Southern Italy

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2405 ◽  
Author(s):  
Luigi Guerriero ◽  
Giuseppe Ruzza ◽  
Domenico Calcaterra ◽  
Diego Di Martire ◽  
Francesco M. Guadagno ◽  
...  
Keyword(s):  
Rural Communities ◽  
Southern Italy ◽  
Flood Hazard ◽  
Probability Model ◽  
Human Action ◽  
Return Level ◽  
Probability Models ◽  
Future Evolution ◽  
Near Future ◽  
Inundation Model

The change of the Earth’s climate and the increasing human action (e.g., increasing impervious areas) are influencing the recurrence and magnitude of flooding events and consequently the exposure of urban and rural communities. Under these conditions, flood hazard analysis needs to account for this change through the adoption of nonstationary approaches. Such methods, showing how flood hazard evolves over time, are able to support a long-term plan of adaptation in hazard changing perspective, reducing expected annual damage in flood prone areas. On this basis, in this paper a reevaluation of flood hazard in the Benevento province of southern Italy, is presented, providing a reduced complexity methodological framework for near future flood hazard prediction under nonstationary conditions. The proposed procedure uses multiple nonstationary probability models and a LiDAR-derived high-resolution inundation model to provide present and future flood scenarios in the form of hazard maps. Such maps are derived using a spatialization routine of stage probability across the inundation model that is able to work at different scales. The analysis indicates that, overall, (i) flood hazard is going to decrease in the next 30 years over the Benevento province and (ii) many areas of the Calore river floodplain are going to be subject to higher return level events. Consequently, many areas would require new guidelines of use as the hazard level decreases. Limitations of the analysis are related to the choice of the probability model and the parameter estimation approach. A further limit is that, currently, this method is not able to account for the presence of mitigation measurements. However, result validation indicates a very high accuracy of the proposed procedure with a matching degree, with a recently observed 225-years flood, estimated in 98%. On this basis, the proposed framework can be considered a very important approach in flood hazard estimation able to predict near future evolution of flood hazard as modulated by the ongoing climate change.

Study on Hazard Analysis Method of Earthquake Secondary Fire Disaster in Urban Area Based on GIS

2012 ◽  
Vol 204-208 ◽  
pp. 3457-3461
Author(s):  
Tian Qi Li ◽  
Fei Geng
Keyword(s):  
Urban Areas ◽  
Hazard Analysis ◽  
Probability Model ◽  
Probability Models ◽  
Housing Density ◽  
Earthquake Excitation ◽  
Fire Disaster ◽  
High Hazard ◽  
The City ◽  
And Diffusion

In order to study the probability of occurrence of secondary fire after the earthquake in urban areas, the probability model of the hazard analysis that the fire occurred and the spread is established and applied. Probability models need to consider the destruction level of buildings under earthquake excitation as well as the probability of the leakage and diffusion of combustible material in the buildings in the corresponding destruction level, combination of weather, season, housing density and other factors to determine the probability of the single building earthquake secondary fire. On this basis , the natural administrative areas in the city as a unit , considering the factors of regional hazard analysis such as population density , property distribution and density within a region , to calculate the hazard indicator and determine the high hazard areas of secondary fire in the city. The Geographic Information System was used as the platform, to division of urban earthquake secondary fire high-hazard areas.

scholarly journals A metric-based assessment of flood risk and vulnerability of rural communities in the Lower Shire Valley, Malawi

2015 ◽  
Vol 370 ◽  
pp. 139-145 ◽  
Author(s):  
A. J. Adeloye ◽  
F. D. Mwale ◽  
Z. Dulanya
Keyword(s):  
Rural Communities ◽  
Spatial Variability ◽  
Flood Risk ◽  
Flood Hazard ◽  
Sub Saharan Africa ◽  
Structured Interviews ◽  
Adaptation Measures ◽  
Flood Depth ◽  
Inundation Area ◽  
Sub Saharan

Abstract. In response to the increasing frequency and economic damages of natural disasters globally, disaster risk management has evolved to incorporate risk assessments that are multi-dimensional, integrated and metric-based. This is to support knowledge-based decision making and hence sustainable risk reduction. In Malawi and most of Sub-Saharan Africa (SSA), however, flood risk studies remain focussed on understanding causation, impacts, perceptions and coping and adaptation measures. Using the IPCC Framework, this study has quantified and profiled risk to flooding of rural, subsistent communities in the Lower Shire Valley, Malawi. Flood risk was obtained by integrating hazard and vulnerability. Flood hazard was characterised in terms of flood depth and inundation area obtained through hydraulic modelling in the valley with Lisflood-FP, while the vulnerability was indexed through analysis of exposure, susceptibility and capacity that were linked to social, economic, environmental and physical perspectives. Data on these were collected through structured interviews of the communities. The implementation of the entire analysis within GIS enabled the visualisation of spatial variability in flood risk in the valley. The results show predominantly medium levels in hazardousness, vulnerability and risk. The vulnerability is dominated by a high to very high susceptibility. Economic and physical capacities tend to be predominantly low but social capacity is significantly high, resulting in overall medium levels of capacity-induced vulnerability. Exposure manifests as medium. The vulnerability and risk showed marginal spatial variability. The paper concludes with recommendations on how these outcomes could inform policy interventions in the Valley.

scholarly journals On the S-instability and degeneracy of discrete deep learning models

2019 ◽  
Vol 9 (3) ◽  
pp. 627-655 ◽  
Author(s):  
Andee Kaplan ◽  
Daniel J Nordman ◽  
Stephen B Vardeman
Keyword(s):  
Probability Model ◽  
Correlated Data ◽  
Data Sequence ◽  
Probability Models ◽  
Large Classes ◽  
Learning Contexts ◽  
Probability Structure ◽  
Model Probability ◽  
Parameter Sequence ◽  
Outcome Result

Abstract A probability model exhibits instability if small changes in a data outcome result in large and, often unanticipated, changes in probability. This instability is a property of the probability model, given by a distributional form and a given configuration of parameters. For correlated data structures found in several application areas, there is increasing interest in identifying such sensitivity in model probability structure. We consider the problem of quantifying instability for general probability models defined on sequences of observations, where each sequence of length $N$ has a finite number of possible values that can be taken at each point. A sequence of probability models, indexed by $N$, and an associated parameter sequence result to accommodate data of expanding dimension. Model instability is formally shown to occur when a certain log probability ratio under such models grows faster than $N$. In this case, a one component change in the data sequence can shift probability by orders of magnitude. Also, as instability becomes more extreme, the resulting probability models are shown to tend to degeneracy, placing all their probability on potentially small portions of the sample space. These results on instability apply to large classes of models commonly used in random graphs, network analysis and machine learning contexts.

Flood hazard mapping incorporating multiple probability models

2020 ◽  
Vol 587 ◽  
pp. 125020 ◽  
Author(s):  
Luigi Guerriero ◽  
Giuseppe Ruzza ◽  
Francesco M. Guadagno ◽  
Paola Revellino
Keyword(s):  
Flood Hazard ◽  
Hazard Mapping ◽  
Probability Models

scholarly journals Bell Could Become the Copernicus of Probability

2016 ◽  
Vol 23 (02) ◽  
pp. 1650008 ◽  
Author(s):  
Andrei Khrennikov
Keyword(s):  
Probability Model ◽  
Quantum Probability ◽  
Bell’S Inequality ◽  
Probability Models ◽  
Bell's Inequality ◽  
Mathematical Probability ◽  
Model Interpretation ◽  
Born’S Rule ◽  
Quantum Phenomena

Our aim is to emphasize the role of mathematical models in physics, especially models of geometry and probability. We briefly compare developments of geometry and probability by pointing to similarities and differences: from Euclid to Lobachevsky and from Kolmogorov to Bell. In probability, Bell could play the same role as Lobachevsky in geometry. In fact, violation of Bell’s inequality can be treated as implying the impossibility to apply the classical probability model of Kolmogorov (1933) to quantum phenomena. Thus the quantum probabilistic model (based on Born’s rule) can be considered as the concrete example of the non-Kolmogorovian model of probability, similarly to the Lobachevskian model — the first example of the non-Euclidean model of geometry. This is the “probability model” interpretation of the violation of Bell’s inequality. We also criticize the standard interpretation—an attempt to add to rigorous mathematical probability models additional elements such as (non)locality and (un)realism. Finally, we compare embeddings of non-Euclidean geometries into the Euclidean space with embeddings of the non-Kolmogorovian probabilities (in particular, quantum probability) into the Kolmogorov probability space. As an example, we consider the CHSH-test.

scholarly journals A new approach for modeling covid-19 death data

2021 ◽  
pp. 1-9
Author(s):  
Muhammad Farooq ◽  
Qamar-uz-zaman ◽  
Muhammad Ijaz
Keyword(s):  
Probability Model ◽  
Developed Countries ◽  
Likelihood Method ◽  
Data Sets ◽  
Probability Models ◽  
New Approach ◽  
Proposed Model ◽  
Rate Functions ◽  
Day By Day ◽  
Death Data

The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW.

scholarly journals Documentation of Wild Edible Leafy Vegetable Traditionally Used By Tribal and Rural Communities of North Maharashtra, India

2021 ◽  
Vol 4 (3) ◽  
pp. 148-159
Author(s):  
Sachin Golait ◽  
Sanjay Auti ◽  
Shankar Laware
Keyword(s):  
Rural Communities ◽  
Sustainable Management ◽  
Leafy Vegetable ◽  
Leafy Vegetables ◽  
Wild Food ◽  
The World ◽  
Food Species ◽  
Wild Resources ◽  
Tribal Region ◽  
Near Future

Number of wild edible plants is commonly used in the traditional diets of tribal people in many parts of the world. North Maharashtra is well known for its tribal region and tribes from this region partially or fully dependent on the wild resources for their nutritional requirements. The present study was designed to document specifically the wild leafy vegetables from North Maharashtra. A total of 62 traditionally used wild leafy vegetable species were collected, identified and documented. Out of 62 species, 61 species belongs to Angiosperms and 1 belong to pteridophyte.  With respect to families Amaranthaceae, Araceae, Asteraceae and Fabaceae were found to be the largest families with 29 species. Herbs are the major source of wild leafy vegetables with 43 species and forest is the home for the majority of wild leafy vegetables. Due to less awareness, loss of vegetation and fast erosion of traditional knowledge many species are on the line of rarity. The study helps to conserve those wild food species and cultivate them on large scales, to uplift their economical status and sustainable management in near future.

scholarly journals Humans 2.0: Writing the future of human evolution

2019 ◽  
Author(s):  
Gemma Marfany
Keyword(s):  
Human Evolution ◽  
Current Knowledge ◽  
Phenotype Correlation ◽  
Future Evolution ◽  
Genotype Phenotype Correlation ◽  
Technological Advances ◽  
The Future ◽  
Potential Impact ◽  
Near Future

Can humans control the future evolution of our species? Based on current knowledge in genetics, one can infer and extrapolate what may happen in the near future. After all, if we are to predict the future, we must first understand the foundations of our present. To answer the first question, I will briefly present what we know about our genome and whether we have enough data to infer who we are (known as the genotype–phenotype correlation), then I will present new technological advances and their potential impact on our evolution.

Sign in / Sign up

Export Citation Format

Share Document