Probabilistic prediction of wildfire economic losses to housing in Cyprus using Bayesian network analysis

Loss prediction models are an important part of wildfire risk assessment, but have received only limited attention in the scientific literature. Such models can support decision-making on preventive measures targeting fuels or potential ignition sources, on fire suppression, on mitigation of consequences and on effective allocation of funds. This paper presents a probabilistic model for predicting wildfire housing loss at the mesoscale (1 km2) using Bayesian network (BN) analysis. The BN enables the construction of an integrated model based on causal relationships among the influencing parameters jointly with the associated uncertainties. Input data and models are gathered from literature and expert knowledge to overcome the lack of housing loss data in the study area. Numerical investigations are carried out with spatiotemporal datasets for the Mediterranean island of Cyprus. The BN is coupled with a geographic information system (GIS) and the resulting estimated house damages for a given fire hazard are shown in maps. The BN model can be attached to a wildfire hazard model to determine wildfire risk in a spatially explicit manner. The developed model is specific to areas with house characteristics similar to those found in Cyprus, but the general methodology is transferable to any other area, as well as other damages.

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Do fires discriminate? Socio-economic disadvantage, wildfire hazard exposure and the Australian 2019–20 ‘Black Summer’ fires

Climatic Change ◽

10.1007/s10584-021-03064-6 ◽

2021 ◽

Vol 165 (3-4) ◽

Author(s):

Sonia Akter ◽

R. Quentin Grafton

Keyword(s):

Spatial Analysis ◽

Fire Suppression ◽

Fire Hazard ◽

Geographical Area ◽

Economic Disadvantage ◽

Disadvantaged Communities ◽

Hazard Exposure ◽

South Wales ◽

Wildfire Hazard ◽

Combining Data

AbstractWe examine the relationship between socio-economic disadvantage and exposure to environmental hazard with data from the catastrophic 2019–2020 Australian wildfires (Black Summer) that burnt at least 19 million hectares, thousands of buildings and was responsible for the deaths of 34 people and more than one billion animals. Combining data from the National Indicative Aggregated Fire Extent (NIAFE) and 2016 Socio-Economic Indexes for Areas (SEIFA), we estimate the correlation between wildfire hazard exposure and an index of community-level socio-economic disadvantage. Wildfire hazard exposure is measured as the interaction between the percentage of area burnt and proximity of the fire to settlements. The results reveal a significant positive relationship between fire hazard exposure and socio-economic disadvantage, such that the most socio-economically disadvantaged communities bore a disproportionately higher hazard exposure in the Black Summer than relatively advantaged communities. Our spatial analysis shows that the socio-economic disadvantage and wildfire hazard exposure relationship exists in inner regional, outer regional and remote areas of New South Wales and Victoria, the two worst-hit states of the Black Summer catastrophe. Our spatial analysis also finds that wildfire hazard exposure, even within a small geographical area, vary substantially depending on the socio-economic profiles of communities. A possible explanation for our findings is resource gaps for fire suppression and hazard reduction that favour communities with a greater level of socio-economic advantage.

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Do Fires Discriminate? Socio-Economic Disadvantage, Wildfire Hazard Exposure and the Australian 2019–20 ‘Black Summer’ Fires

10.21203/rs.3.rs-232966/v1 ◽

2021 ◽

Author(s):

Sonia Akter ◽

R. Quentin Grafton

Keyword(s):

Spatial Analysis ◽

Fire Suppression ◽

Fire Hazard ◽

Geographical Area ◽

Economic Disadvantage ◽

Disadvantaged Communities ◽

Hazard Exposure ◽

South Wales ◽

Wildfire Hazard ◽

Combining Data

Abstract We examine the relationship between socio-economic disadvantage and exposure to environmental hazard with data from the catastrophic 2019–2020 Australian wildfires (Black Summer) that burnt at least 19 million hectares, thousands of buildings and was responsible for the deaths of 34 people and more than one billion animals. Combining data from the National Indicative Aggregated Fire Extent (NIAFE) and 2016 Socio-Economic Indexes for Areas (SEIFA), we estimate the correlation between wildfire hazard exposure and an index of community-level socio-economic disadvantage. Wildfire hazard exposure is measured as the interaction between the percentage of area burnt and proximity of the fire to settlements. The results reveal a significant positive relationship between fire hazard exposure and socio-economic disadvantage, such that the most socio-economically disadvantaged communities bore a disproportionately higher hazard exposure in the Black Summer than relatively advantaged communities. Our spatial analysis shows that the socio-economic disadvantage and wildfire hazard exposure relationship exists in inner regional, outer regional and remote areas of New South Wales and Victoria, the two worst-hit states of the Black Summer catastrophe. Our spatial analysis also finds that wildfire hazard exposure, even within a small geographical area, can vary substantially depending on the socio-economic profiles of communities. A possible explanation for our findings is resource gaps for fire suppression and hazard reduction that favours communities with a greater level of socio-economic advantage.

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Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118796938 ◽

2018 ◽

Vol 2672 (45) ◽

pp. 196-209 ◽

Cited By ~ 1

Author(s):

Haozhe Cong ◽

Cong Chen ◽

Pei-Sung Lin ◽

Guohui Zhang ◽

John Milton ◽

...

Keyword(s):

Bayesian Network ◽

Expert Knowledge ◽

Transportation Systems ◽

Practical Significance ◽

Incident Management ◽

Highway Traffic ◽

Traffic Incidents ◽

Traffic Incident ◽

Incident Duration ◽

Traffic Incident Management

Highway traffic incidents induce a significant loss of life, economy, and productivity through injuries and fatalities, extended travel time and delay, and excessive energy consumption and air pollution. Traffic emergency management during incident conditions is the core element of active traffic management, and it is of practical significance to accurately understand the duration time distribution for typical traffic incident types and the factors that influence incident duration. This study proposes a dual-learning Bayesian network (BN) model to estimate traffic incident duration and to examine the influence of heterogeneous factors on the length of duration based on expert knowledge of traffic incident management and highway incident data collected in Zhejiang Province, China. Fifteen variables related to three aspects of traffic incidents, including incident information, incident consequences, and rescue resources, were included in the analysis. The trained BN model achieves favorable performance in several areas, including classification accuracy, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) value. A classification matrix, and significant variables and their heterogeneous influences are identified accordingly. The research findings from this study provide beneficial reference to the understanding of decision-making in traffic incident response and process, active traffic incident management, and intelligent transportation systems.

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Standardizing catch rates: is logbook information by itself enough?

ICES Journal of Marine Science ◽

10.1093/icesjms/fsm179 ◽

2008 ◽

Vol 65 (2) ◽

pp. 255-266 ◽

Cited By ~ 25

Author(s):

J. Bishop ◽

W. N. Venables ◽

C. M. Dichmont ◽

D. J. Sterling

Keyword(s):

Relative Abundance ◽

Prediction Models ◽

Expert Knowledge ◽

External Information ◽

Linear Regression Models ◽

Catch Per Unit Effort ◽

Estimation Model ◽

Catch Rates ◽

Logbook Data ◽

Estimation Models

Abstract Bishop, J., Venables, W. N., Dichmont, C. M., and Sterling, D. J. 2008. Standardizing catch rates: is logbook information by itself enough? – ICES Journal of Marine Science, 65: 255–266. The goal of the work was to maximize the accuracy of standardized catch per unit effort as an index of relative abundance. Linear regression models were fitted to daily logbook data from a multispecies penaeid trawl fishery in which within-vessel changes in efficiency are common. Two model-fitting strategies were compared. The predictive strategy focused on maximizing the explained variance, and the estimation strategy on finding realistic coefficients for important components of changing catchability. Realistic values could not always be obtained, because the regression factors were not orthogonal, and data on the presence of technology were sometimes unreliable or systematically incomplete. It was not possible to separate fishing power from abundance by analysing logbook data alone; it was necessary to incorporate external information within the standardization model. Therefore, the resultant estimation models incorporated external information and expert knowledge by offsets. There was no single best estimation model. Instead, a series of models provided an envelope of possible changes in relative fishing power and prawn abundance since 1970. Compared with the prediction models, the estimation models revealed different trends in relative fishing power and relative abundance.

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Bayesian Network Models with Decision Tree Analysis for Management of Childhood Malaria in Malawi

10.21203/rs.3.rs-60209/v1 ◽

2020 ◽

Author(s):

Sanya B. Taneja ◽

Gerald P. Douglas ◽

Gregory F. Cooper ◽

Marian G. Michaels ◽

Marek J. Druzdzel ◽

...

Keyword(s):

Decision Tree ◽

Bayesian Network ◽

Clinical Features ◽

Predictive Models ◽

Clinical Decision Making ◽

Performance Metrics ◽

Expert Knowledge ◽

Clinical Decision ◽

Automated Method ◽

Childhood Malaria

Abstract Background: Malaria is a major cause of death in children under five years old in low- and middle-income countries such as Malawi. Accurate diagnosis and management of malaria can help reduce the global burden of childhood morbidity and mortality. Trained healthcare workers in rural health centers manage malaria with limited supplies of malarial diagnostic tests and drugs for treatment. A clinical decision support system that integrates predictive models to provide an accurate prediction of malaria based on clinical features could aid healthcare worker in judicious use of testing and treatment. We developed Bayesian network (BN) models to predict the probability of malaria from clinical features and an illustrative decision tree to model the decision to use or not use a malaria rapid diagnostic test (mRDT).Methods: We developed two BN models from data that were collected in a national survey of outpatient encounters of children in Malawi. The target diagnosis is taken as the result of mRDT. The first BN model was created manually with expert knowledge, and the second model was derived using an automated method followed by modifications guided by expert knowledge. The performance of the BN models was compared to other statistical models on a range of performance metrics. We developed a decision tree that integrates predictions from these predictive models with the costs of mRDT and a course of recommended treatment. Results: Compared to the logistic regression and random forest models, the BN models had similar accuracy of 64% but had higher sensitivity at the cost of lower specificity at the default threshold. Sensitivity analysis of the decision tree showed that at low (below 0.04) and high (above 0.4) probabilities of malaria in a child, the preferred decision that minimizes expected costs is not to perform mRDT.Conclusion: In resource-constrained settings, judicious use of mRDT is important. Predictive models in combination with decision analysis can provide personalized guidance on when to use mRDT in the management of childhood malaria. BN models can be efficiently derived from data to support such clinical decision making.

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Modelling suppression difficulty: current and future applications

International Journal of Wildland Fire ◽

10.1071/wf19042 ◽

2020 ◽

Vol 29 (8) ◽

pp. 739 ◽

Cited By ~ 5

Author(s):

Francisco Rodríguez y Silva ◽

Christopher D. O'Connor ◽

Matthew P. Thompson ◽

Juan Ramón Molina Martínez ◽

David E. Calkin

Keyword(s):

Expert Knowledge ◽

Fire Suppression ◽

Fire Behaviour ◽

Support Tools ◽

The Usa ◽

Large Fires ◽

Informational Basis ◽

Fire Containment ◽

Difficulty Index ◽

Made In

Improving decision processes and the informational basis upon which decisions are made in pursuit of safer and more effective fire response have become key priorities of the fire research community. One area of emphasis is bridging the gap between fire researchers and managers through development of application-focused, operationally relevant decision support tools. In this paper we focus on a family of such tools designed to characterise the difficulty of suppression operations by weighing suppression challenges against suppression opportunities. These tools integrate potential fire behaviour, vegetation cover types, topography, road and trail networks, existing fuel breaks and fireline production potential to map the operational effort necessary for fire suppression. We include case studies from two large fires in the USA and Spain to demonstrate model updates and improvements intended to better capture extreme fire behaviour and present results demonstrating successful fire containment where suppression difficulty index (SDI) values were low and containment only after a moderation of fire weather where SDI values were high. A basic aim of this work is reducing the uncertainty and increasing the efficiency of suppression operations through assessment of landscape conditions and incorporation of expert knowledge into planning.

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High-throughput method for detection and quantification of lesions on leaf scale based on trypan blue staining and digital image analysis

Plant Methods ◽

10.1186/s13007-020-00605-5 ◽

2020 ◽

Vol 16 (1) ◽

Cited By ~ 1

Author(s):

Emina Mulaosmanovic ◽

Tobias U. T. Lindblom ◽

Marie Bengtsson ◽

Sofia T. Windstam ◽

Lars Mogren ◽

...

Keyword(s):

Image Analysis ◽

Trypan Blue ◽

Prediction Models ◽

Abiotic Factors ◽

Leafy Vegetables ◽

Leaf Damage ◽

Blue Staining ◽

Economic Losses ◽

Wounded Area ◽

Detection And Quantification

Abstract Background Field-grown leafy vegetables can be damaged by biotic and abiotic factors, or mechanically damaged by farming practices. Available methods to evaluate leaf tissue damage mainly rely on colour differentiation between healthy and damaged tissues. Alternatively, sophisticated equipment such as microscopy and hyperspectral cameras can be employed. Depending on the causal factor, colour change in the wounded area is not always induced and, by the time symptoms become visible, a plant can already be severely affected. To accurately detect and quantify damage on leaf scale, including microlesions, reliable differentiation between healthy and damaged tissue is essential. We stained whole leaves with trypan blue dye, which traverses compromised cell membranes but is not absorbed in viable cells, followed by automated quantification of damage on leaf scale. Results We present a robust, fast and sensitive method for leaf-scale visualisation, accurate automated extraction and measurement of damaged area on leaves of leafy vegetables. The image analysis pipeline we developed automatically identifies leaf area and individual stained (lesion) areas down to cell level. As proof of principle, we tested the methodology for damage detection and quantification on two field-grown leafy vegetable species, spinach and Swiss chard. Conclusions Our novel lesion quantification method can be used for detection of large (macro) or single-cell (micro) lesions on leaf scale, enabling quantification of lesions at any stage and without requiring symptoms to be in the visible spectrum. Quantifying the wounded area on leaf scale is necessary for generating prediction models for economic losses and produce shelf-life. In addition, risk assessments are based on accurate prediction of the relationship between leaf damage and infection rates by opportunistic pathogens and our method helps determine the severity of leaf damage at fine resolution.

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Corrigendum to: Modelling suppression difficulty: current and future applications

International Journal of Wildland Fire ◽

10.1071/wf19042_co ◽

2020 ◽

Vol 29 (8) ◽

pp. 752

Author(s):

Francisco Rodríguez y Silva ◽

Christopher D. O'Connor ◽

Matthew P. Thompson ◽

Juan Ramón Molina Martínez ◽

David E. Calkin

Keyword(s):

Expert Knowledge ◽

Fire Suppression ◽

Fire Behaviour ◽

Support Tools ◽

The Usa ◽

Large Fires ◽

Informational Basis ◽

Fire Containment ◽

Difficulty Index ◽

Made In

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Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

Sustainability ◽

10.3390/su11143828 ◽

2019 ◽

Vol 11 (14) ◽

pp. 3828 ◽

Cited By ~ 2

Author(s):

Jin ◽

Kim ◽

Hyun ◽

Han

Keyword(s):

Life Cycle ◽

Prediction Model ◽

Life Cycle Cost ◽

Construction Projects ◽

Prediction Models ◽

Early Stage ◽

Case Based Reasoning ◽

Probabilistic Prediction ◽

Construction Costs ◽

Early Stages

Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector.

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Predictability of flood events in view of current meteorology and hydrology in the conditions of the Czech Republic

Soil and Water Research ◽

10.17221/2109-swr ◽

2008 ◽

Vol 2 (No. 4) ◽

pp. 156-168 ◽

Cited By ~ 4

Author(s):

L. Březková ◽

M. Šálek ◽

E. Soukalová ◽

M. Starý

Keyword(s):

Czech Republic ◽

Prediction Models ◽

Weather Prediction ◽

Warning System ◽

Economic Losses ◽

Hydrological Models ◽

Flood Events ◽

Common Error ◽

The Czech Republic ◽

Hydrological Forecast

In central Europe, floods are natural disasters causing the greatest economic losses. One way to reduce partly the flood-related damage, especially the loss of lives, is a functional objective forecasting and warning system that incorporates both meteorological and hydrological models. Numerical weather prediction models operate with horizontal spatial resolution of several dozens of kilometres up to several kilometres, nevertheless, the common error in the localisation of the heavy rainfall characteristic maxima is mostly several times as large as the grid size. The distributive hydrological models for the middle sized basins (hundreds to thousands of km<sup>2</sup>) operate with the resolution of hundreds of meters. Therefore, the (in) accuracy of the meteorological forecast can heavily influence the following hydrological forecast. In general, we can say that the shorter is the duration of the given phenomenon and the smaller area it hits, the more difficult is its prediction. The time and spatial distribution of the predicted precipitation is still one of the most difficult tasks of meteorology. Hydrological forecasts are created under the conditions of great uncertainty. This paper deals with the possibilities of the current hydrology and meteorology with regard to the predictability of the flood events. The Czech Hydrometeorological Institute is responsible by law for the forecasting flood service in the Czech Republic. For the precipitation and temperature forecasts, the outputs of the numerical model of atmosphere ALADIN are used. Moreover, the meteorological community has available operational outputs of many weather prediction models, being run in several meteorological centres around the world. For the hydrological forecast, the HYDROG and AQUALOG models are utilised. The paper shows examples of the hydrological flood forecasts from the years 2002–2006 in the Dyje catchment, attention being paid to floods caused by heavy rainfalls in the summer season. The results show that it is necessary to take into account the predictability of the particular phenomenon, which can be used in the decision making process during an emergency.

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