Prediction of daily lightning- and human-caused fires in British Columbia

2012 ◽  
Vol 21 (4) ◽  
pp. 342 ◽  
Author(s):  
S. Magnussen ◽  
S. W. Taylor
Keyword(s):  
British Columbia ◽  
Forest Cover ◽  
Atmospheric Stability ◽  
Model Performance ◽  
Binary Logistic Regression ◽  
Fire Weather Index ◽  
Explanatory Variables ◽  
Daily Number ◽  
Modelling Framework ◽  
Model Predictions

Daily records of the location and timing of human- and lightning-caused fires in British Columbia from 1981 to 2000 were used to estimate the probability of fire occurrence within 950 20 × 20-km spatial units (~950 000 km2) using a binary logistic regression modelling framework. Explanatory variables included lightning strikes, forest cover, surface weather observations, atmospheric stability indices and fuel moisture codes of the Canadian Fire Weather Index System. Because the influence of the explanatory variables in the models varied from year to year, model coefficients were estimated for each year. The arithmetic mean of the model coefficients was used for making daily predictions in a future year. A confidence interval around the mean or a quantile was derived from the ensemble of 20 model predictions. A leave-1-year-out cross-validation procedure was used to assess model performance for random years. The daily number of lightning-caused fires was reasonably well predicted at the provincial level (R = 0.83) and slightly less well predicted for a smaller (75 000 km2) administrative region. The daily number of human-caused fires was less well predicted at both the provincial (R = 0.55) and the regional level. The ability to estimate confidence intervals from the ensemble of model predictions is an advantage of the year-specific approach.

scholarly journals Modelação espacial da adequabilidade de habitat a espécies invasoras: o Carpobrotus Edulis em terreno não dunar

Finisterra ◽  
2012 ◽  
Vol 43 (86) ◽  
Author(s):  
César Capinha
Keyword(s):  
Native Species ◽  
Threshold Model ◽  
Model Performance ◽  
Binary Logistic Regression ◽  
Carpobrotus Edulis ◽  
Modelling Framework ◽  
Exotic Invasive Species ◽  
Presence Data ◽  
Probability Threshold ◽  
Alien Invader

The use of habitat modelling for exotic invasive species can be extremely useful for identifying their potential impacts and for assisting in the design of eradication strategies. Even though the latter builds on theoretical assumptions that are quite different from those involved in the modelling of the habitat of native species, these two modelling methods are in fact quite similar. This article presents a habitat suitability modelling framework for Carpobrotus edulis, an alien invader plant in Serra do Bouro, Portugal. Several land surveys have been carried out in the study area in order to record the presence of this plant. The criteria for recording a presence were that the plant did not show any signs of weakness and that there were mat formations covering at least 5m2. Pseudo-absences were also obtained in a completely random way. The model was calibrated using a binary logistic regression. The performance of this model usually considered superior to that of models that rely on presence data only. Additionally, an evaluation technique based on the minimum area of higher adequacy is also presented. This technique assumes that, for a given probability threshold, model performance is higher whenever it has the same number of correct presences for a smaller predicted area. Using a 0.7 probability threshold, the model correctly predicted 80% of the total presences using only 8% of the study area. The model suggests that the main factor contributing to the expansion of Carpobrotus edulis has been the abandonment of agriculture in the study area. In addition, proximity to the shoreline and above-average erosion potential in the study area both seem to benefit the plant’s expansion. Conversely, steeper and longer slopes, and greater distances from the shoreline, were found to be significant contributors to the plant’s absence.

scholarly journals To Swab or Not to Swab? The Lesson Learned in Italy in the Early Stage of the COVID-19 Pandemic

2021 ◽  
Vol 11 (9) ◽  
pp. 4042
Author(s):  
Paola Berchialla ◽  
Maria Teresa Giraudo ◽  
Carmen Fava ◽  
Andrea Ricotti ◽  
Giuseppe Saglio ◽  
...  
Keyword(s):  
Early Phase ◽  
Early Stage ◽  
Respiratory Illness ◽  
Explanatory Variables ◽  
Daily Number ◽  
Testing Strategies ◽  
Serious Infections ◽  
The Impact ◽  
Epidemiological Link ◽  
Severe Respiratory Illness

Testing for the SARS-CoV-2 infection is critical for tracking the spread of the virus and controlling the transmission dynamics. In the early phase of the pandemic in Italy, the decentralized healthcare system allowed regions to adopt different testing strategies. The objective of this paper is to assess the impact of the extensive testing of symptomatic individuals and their contacts on the number of hospitalizations against a more stringent testing strategy limited to suspected cases with severe respiratory illness and an epidemiological link to a COVID-19 case. A Poisson regression modelling approach was adopted. In the first model developed, the cumulative daily number of positive cases and a temporal trend were considered as explanatory variables. In the second, the cumulative daily number of swabs was further added. The explanatory variable, given by the number of swabs over time, explained most of the observed differences in the number of hospitalizations between the two strategies. The percentage of the expected error dropped from 70% of the first, simpler model to 15%. Increasing testing to detect and isolate infected individuals in the early phase of an outbreak improves the capability to reduce the spread of serious infections, lessening the burden of hospitals.

scholarly journals Modelling Exposure by Spraying Activities—Status and Future Needs

2021 ◽  
Vol 18 (15) ◽  
pp. 7737
Author(s):  
Stefan Hahn ◽  
Jessica Meyer ◽  
Michael Roitzsch ◽  
Christiaan Delmaar ◽  
Wolfgang Koch ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Model Performance ◽  
Relevant Information ◽  
Systematic Evaluation ◽  
Model Accuracy ◽  
Efficient Manner ◽  
User Perspective ◽  
Model Predictions ◽  
Spray Applications

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

Deciphering Links Between Sexual Violence and Castes in India

2021 ◽  
pp. 2455328X2110325
Author(s):  
Yogendra Musahar
Keyword(s):  
Regression Model ◽  
Sexual Violence ◽  
Economic Status ◽  
Family Health ◽  
Uttar Pradesh ◽  
Binary Logistic Regression ◽  
Wealth Index ◽  
Bivariate Analysis ◽  
Binary Logistic Regression Model ◽  
Explanatory Variables

The recent incident, the gang rape and murder of a 19-year-old woman in Hathras, a small village in Uttar Pradesh of India, once again sparks a debate on links between sexual violence and castes in India. This article aims to examine the links between sexual violence and castes in India. This study utilizes the national representative National Family Health Survey 4 (NFHS-4, 2015–16) data. A bivariate analysis was carried out to analyse the data. A binary logistic regression model was applied to predict the effect of explanatory variables, viz. type of place of residence, years of schooling complete, economic status in terms of wealth index and finally castes on predicted variable, i.e. sexual violence. The binary regression model indicates that there were links between sexual violence and castes. For secured and dignified life of women, caste-based sexual violence must be annihilated.

scholarly journals Evaluating disease prediction models using a cohort whose covariate distribution differs from that of the target population

2017 ◽  
Vol 28 (1) ◽  
pp. 309-320 ◽  
Author(s):  
Scott Powers ◽  
Valerie McGuire ◽  
Leslie Bernstein ◽  
Alison J Canchola ◽  
Alice S Whittemore
Keyword(s):  
Prediction Models ◽  
Risk Model ◽  
Disease Incidence ◽  
Model Performance ◽  
Target Population ◽  
R Package ◽  
Model Assessment ◽  
Weighted Estimates ◽  
Cancer Prediction ◽  
Model Predictions

Personal predictive models for disease development play important roles in chronic disease prevention. The performance of these models is evaluated by applying them to the baseline covariates of participants in external cohort studies, with model predictions compared to subjects' subsequent disease incidence. However, the covariate distribution among participants in a validation cohort may differ from that of the population for which the model will be used. Since estimates of predictive model performance depend on the distribution of covariates among the subjects to which it is applied, such differences can cause misleading estimates of model performance in the target population. We propose a method for addressing this problem by weighting the cohort subjects to make their covariate distribution better match that of the target population. Simulations show that the method provides accurate estimates of model performance in the target population, while un-weighted estimates may not. We illustrate the method by applying it to evaluate an ovarian cancer prediction model targeted to US women, using cohort data from participants in the California Teachers Study. The methods can be implemented using open-source code for public use as the R-package RMAP (Risk Model Assessment Package) available at http://stanford.edu/~ggong/rmap/ .

scholarly journals Spatially varying constraints of human-caused fire occurrence in British Columbia, Canada

2017 ◽  
Vol 26 (3) ◽  
pp. 219 ◽  
Author(s):  
Philip E. Camp ◽  
Meg A. Krawchuk
Keyword(s):  
British Columbia ◽  
Spatial Patterns ◽  
Fire Management ◽  
Fire Occurrence ◽  
Explanatory Variables ◽  
Human Footprint ◽  
Key Factor ◽  
Key Drivers ◽  
Spatially Varying

Human-caused wildfires are controlled by human and natural influences, and determining their key drivers is critical for understanding spatial patterns of wildfire and implementing effective fire management. We examined an array of explanatory variables that account for spatial controls of human-caused fire occurrence from 1990 to 2013 among six ecosystem zones that vary in human footprint and environmental characteristics in British Columbia, Canada. We found that long-term patterns of human-caused fire in ecosystem zones with a larger human footprint were strongly controlled by biophysical variables explaining conditions conducive to burning, whereas fire occurrence in remote ecosystem zones was controlled by various metrics of human activity. A metric representing the wildland–urban interface was a key factor explaining human-caused fire occurrence regardless of ecosystem zone. Our results contribute to the growing body of research on the varying constraints of spatial patterns of fire occurrence by explicitly examining human-caused fire and the heterogeneity of constraints based on human development.

scholarly journals The Suitability of the Satellite Metrological Inputs Source on the Hydrological Model in a Small Urban Catchment

2016 ◽  
Author(s):  
Song Song ◽  
Youpeng Xu ◽  
Jiali Wang ◽  
Jinkang Du ◽  
Jianxin Zhang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Yangtze River Delta ◽  
Precipitation Data ◽  
Model Quality ◽  
Small Catchment ◽  
Explanatory Variables ◽  
Distributed Models ◽  
The Impact

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

scholarly journals Comments on "Researcher bias: The use of machine learning in software defect prediction"

2015 ◽  
Author(s):  
Chakkrit Tantithamthavorn ◽  
Shane McIntosh ◽  
Ahmed E Hassan ◽  
Kenichi Matsumoto
Keyword(s):  
Prediction Model ◽  
Strong Association ◽  
Model Performance ◽  
Strong Relationship ◽  
Defect Prediction ◽  
Explanatory Variables ◽  
Software Defect ◽  
The Impact ◽  
The Relationship ◽  
Selection Of

Shepperd et al. (2014) find that the reported performance of a defect prediction model shares a strong relationship with the group of researchers who construct the models. In this paper, we perform an alternative investigation of Shepperd et al. (2014)’s data. We observe that (a) researcher group shares a strong association with the dataset and metric families that are used to build a model; (b) the strong association among the explanatory variables introduces a large amount of interference when interpreting the impact of the researcher group on model performance; and (c) after mitigating the interference, we find that the researcher group has a smaller impact than the metric family. These observations lead us to conclude that the relationship between the researcher group and the performance of a defect prediction model may have more to do with the tendency of researchers to reuse experimental components (e.g., datasets and metrics). We recommend that researchers experiment with a broader selection of datasets and metrics to combat potential bias in their results.

scholarly journals Managing Woodland Caribou in West-Central British Columbia

Rangifer ◽  
1996 ◽  
Vol 16 (4) ◽  
pp. 119 ◽  
Author(s):  
Deborah B. Cichowski
Keyword(s):  
British Columbia ◽  
Management Strategy ◽  
Forest Cover ◽  
Planning Process ◽  
Timber Harvesting ◽  
Range Management ◽  
Management Options ◽  
Winter Habitat ◽  
Specific Level ◽  
Winter Range

Initial long term planning for logging on the Tweedsmuir-Entiako caribou winter range began in the early 1980s. Because little information was available on which to base winter range management, the British Columbia Fish and Wildlife Branch began studies on radio-collared caribou in 1983, and an intensive study on caribou winter habitat requirements was conducted from 1985 to 1988. Terrestrial lichens were identified as the primary winter food source for the caribou, and in 1987, caribou winter range ecosystem maps, which emphasized abundance of terrestrial lichens, were produced. The ecosystem maps and information from the caribou study, including potential direct and indirect effects of timber harvesting on the caribou population, were used to develop a management strategy for the winter range. The management strategy comprised two levels of management: a landscape level (Caribou Management Zones); and a site-specific level (caribou habitat/timber values). Timber information associated with BC Ministry of Forests forest cover maps was integrated using a Geographic Information System. Six winter range management options were proposed ranging from harvesting low value caribou habitats only throughout the winter range to total protection of the entire winter range. Impacts of those options on both the caribou population and on the timber supply were evaluated. The options were reviewed through a public planning process, the Entiako Local Resource Use Plan, and recommendations from that process were forwarded to the British Columbia Protected Areas Strategy.

scholarly journals Modelling of hardfacing layers deposition parameters using robust machine learning algorithms

2021 ◽  
Vol 2130 (1) ◽  
pp. 012016
Author(s):  
K Zając ◽  
K Płatek ◽  
P Biskup ◽  
L Łatka
Keyword(s):  
Gas Metal Arc Welding ◽  
Model Performance ◽  
Base Material ◽  
Neural Model ◽  
Machine Learning Algorithms ◽  
Influence Of Process Parameters ◽  
Generic Structure ◽  
Modelling Framework ◽  
Metal Arc Welding ◽  
Welding Processes

Abstract The study presents a data-driven framework for modelling parameters of hardfacing deposits by GMAW using neural models to estimate the influence of process parameters without the need of creating experimental samples of the material and detailed measurements. The process of GAS Metal Arc Welding (GMAW) hardfacing does sometimes create non-homogenous structures in the material not only in deposited material, but also in the heat-affected zone (HAZ) and base material. Those structures are not fully deterministic, so the modelling method should account for this unpredictable component and only learn the generic structure of the hardness of the resulting material. Artificial neural networks (ANN) were used to create a model of the process using only measured samples without any knowledge of equations governing the process. Robust learning was used to decrease the influence of outliers and noise in the measured data on the neural model performance. The proposed method relies on modification of the loss function and several of them are compared and evaluated as an attempt to construct general framework for analysing the hardness as a function of electric current and arc velocity. The proposed method can create robust models of the hardfacing layers deposition or other welding processes and predict the properties of resulting materials even for unseen parameters based on experimental data. This modelling framework is not typically used for metallurgy, and it requires further case studies to verify its generalisability.

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