A logistic model for predicting daily people-caused forest fire occurrence in Ontario

1987 ◽  
Vol 17 (5) ◽  
pp. 394-401 ◽  
Author(s):  
D. L. Martell ◽  
S. Otukol ◽  
B. J. Stocks
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Probability Distribution ◽  
Field Test ◽  
Forest Fire ◽  
Logistic Model ◽  
Northern Region ◽  
Fire Season ◽  
Fire Occurrence ◽  
Analysis Techniques

The authors describe the development of a procedure that can be used to predict daily people-caused forest fire occurrence in the Northern Region of the province of Ontario. The procedure is based on the use of logistic regression analysis techniques to predict the probability of a fire day and the assumption that a Poisson probability distribution can be used to model daily people-caused forest fire occurrence. The results of a field test that was conducted during the summer portion of the 1984 fire season indicate the procedure works well during relatively wet periods.

scholarly journals Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method

Forests ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 5
Author(s):  
Slobodan Milanović ◽  
Nenad Marković ◽  
Dragan Pamučar ◽  
Ljubomir Gigović ◽  
Pavle Kostić ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Suppression ◽  
Standard Procedure ◽  
Predictive Ability ◽  
Fire Risk ◽  
Fire Season ◽  
Fire Occurrence ◽  
Probability Mapping

Forest fire risk has increased globally during the previous decades. The Mediterranean region is traditionally the most at risk in Europe, but continental countries like Serbia have experienced significant economic and ecological losses due to forest fires. To prevent damage to forests and infrastructure, alongside other societal losses, it is necessary to create an effective protection system against fire, which minimizes the harmful effects. Forest fire probability mapping, as one of the basic tools in risk management, allows the allocation of resources for fire suppression, within a fire season, from zones with a lower risk to those under higher threat. Logistic regression (LR) has been used as a standard procedure in forest fire probability mapping, but in the last decade, machine learning methods such as fandom forest (RF) have become more frequent. The main goals in this study were to (i) determine the main explanatory variables for forest fire occurrence for both models, LR and RF, and (ii) map the probability of forest fire occurrence in Eastern Serbia based on LR and RF. The most important variable was drought code, followed by different anthropogenic features depending on the type of the model. The RF models demonstrated better overall predictive ability than LR models. The map produced may increase firefighting efficiency due to the early detection of forest fire and enable resources to be allocated in the eastern part of Serbia, which covers more than one-third of the country’s area.

Modelling seasonal variation in daily people-caused forest fire occurrence

1989 ◽  
Vol 19 (12) ◽  
pp. 1555-1563 ◽  
Author(s):  
D. L. Martell ◽  
E. Bevilacqua ◽  
B. J. Stocks
Keyword(s):  
Logistic Regression ◽  
Seasonal Variation ◽  
Forest Fire ◽  
Regression Models ◽  
Northern Region ◽  
Fire Occurrence ◽  
Periodic Functions ◽  
Logistic Regression Models ◽  
Independent Test ◽  
Calendar Dates

Periodic functions of Julian calendar dates were used to incorporate seasonal variation into logistic regression models designed to predict daily people-caused forest fire occurrence in the Northern Region of the province of Ontario. Three years of independent test data were used to evaluate predictions produced by the models.

What drives forest fire in Fujian, China? Evidence from logistic regression and Random Forests

2016 ◽  
Vol 25 (5) ◽  
pp. 505 ◽  
Author(s):  
Futao Guo ◽  
Guangyu Wang ◽  
Zhangwen Su ◽  
Huiling Liang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Regional Scale ◽  
Fire Risk ◽  
Driving Factors ◽  
Fire Season ◽  
Fire Occurrence ◽  
Climate Factors ◽  
Local Factors ◽  
Risk Zones

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

scholarly journals Logistic model to selection of energy cane clones

2020 ◽  
Vol 50 (9) ◽  
Author(s):  
Juliane Borella ◽  
Jonathan William Trautenmüller ◽  
Bruno Portela Brasileiro ◽  
Ricardo Augusto de Oliveira ◽  
João Carlos Bespalhok Filho
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Logistic Model ◽  
Clonal Selection ◽  
Selection Strategy ◽  
Binary Logistic Regression Analysis ◽  
Lower Grade ◽  
Energy Cane ◽  
Selection Of

ABSTRACT: Logistic regression analysis is a technique that may aid genetic breeding programs in the selection of clones, especially in the early stages where experimental accuracy is low. This research aimed to identify the most important agronomic traits for energy cane clonal selection, and to verify the efficiency of the logistic model in predicting the genotypes to be selected. Evaluations were carried out on 220 clones in the first ratoon. The data were subjected to binary logistic regression analysis. Stalk number per meter was the most important trait in the selection of energy cane clones. In addition, plants with lower grade for smut incidence had a greater chance of being selected. The predictive capacities of the qualitative and quantitative models were 94% and 88%, respectively. The use of a qualitative model proved to be effective at predicting the number of energy cane genotypes to be selected and could be used as a selection strategy.

scholarly journals Geospatial information on geographical and human factors improved anthropogenic fire occurrence modeling in the Chinese boreal forest

2016 ◽  
Vol 46 (4) ◽  
pp. 582-594 ◽  
Author(s):  
Futao Guo ◽  
Selvaraj Selvalakshmi ◽  
Fangfang Lin ◽  
Guangyu Wang ◽  
Wenhui Wang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Human Factors ◽  
Boreal Forest ◽  
Forest Fire ◽  
Management Practices ◽  
Explanatory Power ◽  
Driving Factors ◽  
Fire Occurrence ◽  
Geospatial Information ◽  
Anthropogenic Fire

We applied a classic logistic regression (LR) model together with a geographically weighted logistic regression (GWLR) model to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, “all variables”, “significant variables”, and “cross-validation”, were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line, and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, the GWLR model performs better than the LR model in terms of model prediction accuracy, model residual reduction, and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it.

scholarly journals MUSLIM COMMUNITIES PREFERENCE THAT DO NOT USE SHARIA PAWNSHOP

2019 ◽  
Vol 5 (2) ◽  
pp. 181
Author(s):  
Siti Sabilah Khoiriyah ◽  
Metti Paramita ◽  
Raden Ali Pangestu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Research Method ◽  
Quantitative Approach ◽  
Significance Level ◽  
Analysis Techniques ◽  
Muslim Communities

This study aims to determine what factors influence the preferences of Muslim communities who do not use sharia pawnshops. The research method uses a quantitative approach with logistic regression analysis techniques. The technique of collecting data using questionnaires originating from 100 Muslim communities in West Bogor District. Based on the results of the analysis of the data obtained it can be seen that only locations that have an influence on the decisions of Muslim communities so that they do not use sharia pawnshops because the location has a significance level smaller than 0.05, which is equal to 0.02.

The use of subjective probability assessments to predict forest fire occurrence

1976 ◽  
Vol 6 (3) ◽  
pp. 348-356 ◽  
Author(s):  
A. A. Cunningham ◽  
D. L. Martell
Keyword(s):  
Forest Fire ◽  
Subjective Probability ◽  
Initial Assessment ◽  
Fire Season ◽  
Fire Occurrence ◽  
Northern Ontario ◽  
Scoring Rule

This paper addresses the problem of predicting forest fire occurrence. A simple methodology is developed to elicit information, from experienced fire managers, for deriving subjective probability assessments concerning the number of fires that will be reported in their districts each day. The approach is based upon the assumption that such individuals can identify and classify similar fire environments with satisfactory consistency. Using the concept of subjective probability, a methodology is developed for combining classified experience with a decision maker's initial assessment concerning assessor behavior. A scoring rule is used to measure the accuracy of the assessments. The results of an experiment which was conducted in northern Ontario during the 1973 fire season are presented.

scholarly journals Fishers’ perception on co-operative services and willingness-to-pay (WTP) for improved fish marketing services at Njarakkal, Ernakulam District, Kerala

2017 ◽  
Vol 64 ◽  
Author(s):  
P. Jeyanthi ◽  
V. Chandrasekar ◽  
Nikita Gopal
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Willingness To Pay ◽  
Fisheries Management ◽  
Cold Storage ◽  
Logistic Model ◽  
Vital Role ◽  
Standard Of Living ◽  
Storage Facility ◽  
Fish Marketing

Fishermen co-operatives play a vital role in providing services which are uniform in nature. But, the perception of fishermen regarding co-operative services is generally subjective. This study assessed the fishermens’ perception at Njarakkal, Ernakulam District, Kerala, regarding the services of co-operatives. It was found from the results that 90% of respondents strongly agreed that they were earning profit by selling fish through co-operatives. More than 80% accepted that co-operatives were the best credit source, binding the fishermen and improving standard of living. About 50% felt that co-operatives had no role in fisheries management. Besides provision of credit, fish auctioning is an important service rendered by fishermen co-operatives. It has been felt that there is lack of proper infrastructure facilities, especially cold storage facility in the domestic fish marketing system. The willingness-to-pay for improved marketing services at Njarakkal, was also evaluated during this study. The willingness-to-pay for setting up cold storage facility was assessed using logistic model. Results revealed that 65% of respondents are willing-to-pay for the cold storage facility. The results of logistic regression analysis showed that member’s satisfaction regarding co-operative activities is the most significant factor which decides their willingness-to-pay for the improved marketing services.

scholarly journals Assessment of Forest Fire Rating Systems in Typical Mediterranean Forest, Crete, Greece

2017 ◽  
Author(s):  
Mohamed Elhag ◽  
Slivena Boteva
Keyword(s):  
Moisture Content ◽  
Forest Fire ◽  
Pinus Halepensis ◽  
Water Status ◽  
Fire Danger ◽  
Forest Understory ◽  
Fire Season ◽  
Fire Occurrence ◽  
Fire Weather Index ◽  
Area Burned

Abstract. The Fire Weather Index (FWI) module was tested under the Mediterranean- type conditions of Crete (Greece) for the two fire seasons 2008–2009. High correlations were found between the Fine Fuel Moisture Code (FFMC) and the Duff Moisture Code (DMC. The Drought Code (DC) was insignificantly correlated with the soil moisture content. No significant correlation was found between the area burned by wildfires and any component of the FWI system during the studied period, unlike fire occurrence with which most of the components were highly correlated. Meanwhile, the Keetch-Byram Drought Index (KBDI) of the American Forest Fire Danger Rating System (NFFDRS) was also examined under the same conditions. It provided a useful means of monitoring general wetting and drying cycles, but is inadequate for indicating daily fire danger throughout the fire season in our region. Weak correlations between the KBDI- the fire occurrence and the area burned were found for the two fire seasons studied-2008–2009. Correlations between the KBDI and litter, duff and soil did not give statistically sound results. On the contrary, the KBDI seemed to predict with high accuracy the moisture content of three annual plants (Piplatherum miliaceum, Parietaria diffusa, Avena sterillis) with a shallow rooting system of Pinus halepensis forest understory in the region. This indicated that the index was adequate, to a certain extent, to represent the upper soil layers' water status, while it is unsuitable to predict needles moisture content of Pinus halepensis, which has a deep rooting system.

scholarly journals Determinan Ketepatan Waktu Pelaporan Keuangan

2018 ◽  
Vol 1 (1) ◽  
pp. 80
Author(s):  
Prasetyo Widodo ◽  
Novrida Qudsi Lutfillah
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Data Analysis ◽  
Empirical Evidence ◽  
Financial Reporting ◽  
Stock Exchange ◽  
Financial Reports ◽  
Audit Opinion ◽  
Analysis Techniques ◽  
Interaction Test

Abstrak Tujuan penelitan ini adalah untuk menguji dan membuktikan adanya bukti empiris mengenai pengaruh profitabilitas terhadap ketepatan waktu pelaporan keuangan dengan menambahkanvariabel moderasi yaitu opini audit pada perusahaan barang dan konsumsi yang terdaftar di Bursa Efek Indonesia (BEI) tahun 2013-2015. Sampel penelitian sebanyak 93 dengan teknik analisis data yaitu analisis regresi logistik yang  menggunakan uji interaksi (Moderated Regression Analysis). Hasil penelitian menunjukkan profitabilitas berpengaruh signifikan terhadap ketepatan waktu pelaporan keuangan. Perusahaan dengan profitabilitas yang tinggi cenderung tepat waktu dalam menyampaikan laporan keuangan dan Opini audit dapat memoderasi pengaruh profitabilitas terhadap ketepatan waktu pelaporan keuangan.   Abstract The purpose of this research is to test and prove the existence of empirical evidence regarding the effect of profitability on the timeliness of financial reporting by adding moderation variables, namely the audit opinion on goods and consumption companies listed on the Indonesia Stock Exchange (IDX) in 2013-2015. The research sample was 93 with data analysis techniques namely logistic regression analysis using the interaction test (Moderated Regression Analysis). The results show that profitability has a significant effect on the timeliness of financial reporting. Companies with high profitability tend to be on time in delivering financial reports and audit opinions can moderate the effect of profitability on the timeliness of financial reporting.

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