Modelling of fire count data: fire disaster risk in Ghana

Abstract

Stochastic dynamics involved in ecological count data require distribution fitting procedures to model and make informed judgments. The study provides empirical research, focused on the provision of an early warning system and a spatial graph that can detect societal fire risks. It offers an opportunity for communities, organizations, risk managers, actuaries and governments to be aware of, and understand fire risks, so that they will increase the direct tackling of the threats posed by fire. Statistical distribution fitting method that best helps identify the stochastic dynamics of fire count data is used. The aim is to provide a fire-prediction model and fire spatial graph for observed fire count data. An empirical probability distribution model is fitted to the fire count data and compared to the theoretical probability distribution of the stochastic process of fire count data. The distribution fitted to the fire frequency count data helps identify the class of models that are exhibited by the fire and provides time leading decisions. The research suggests that fire frequency and loss (fire fatalities) count data in Ghana are best modelled with a Negative Binomial Distribution. The spatial map of observed fire frequency and fatality measured over 5 years (2007–2011) offers in this study a first regional assessment of fire frequency and fire fatality in Ghana.

Faktor-Faktor yang Memengaruhi Jumlah Korban Meninggal Akibat Banjir dengan Pendekatan Model Zero Inflated Poisson

“…Penelitian mengenai jumlah kejadian dan korban dari bencana kebakaran sebelumnya pernah dilakukan oleh (Boadi, et al, 2015) dengan studi kasus wilayah negara Ghana. Sama seperti data korban banjir penelitian ini juga menggunakan data cacah sebagai variabel dependennya, sehingga (Boadi, et al, 2015) mengusulkan untuk menggunakan model negative binomial untuk mengatasi overdispersi pada model poisson-nya. Sama halnya dengan (Boadi, et al, 2015), (Siregar, 2019) juga melakukan analisis terhadap data berjenis cacah, yaitu kejadian kasus penyakit Tetanus Neonatarum di Jawa Timur.…”

“…Sama seperti data korban banjir penelitian ini juga menggunakan data cacah sebagai variabel dependennya, sehingga (Boadi, et al, 2015) mengusulkan untuk menggunakan model negative binomial untuk mengatasi overdispersi pada model poisson-nya. Sama halnya dengan (Boadi, et al, 2015), (Siregar, 2019) juga melakukan analisis terhadap data berjenis cacah, yaitu kejadian kasus penyakit Tetanus Neonatarum di Jawa Timur. Pada penelitiannya model poisson (Siregar, 2019) juga mengalami overdispersi yang disebabkan oleh, kasus penyakit Tetanus Neonatarum adalah langka (rare case).…”

“…Pada penelitiannya model poisson (Siregar, 2019) juga mengalami overdispersi yang disebabkan oleh, kasus penyakit Tetanus Neonatarum adalah langka (rare case). Sehingga, alih-alih menggunakan model alternatif negatif binomial seperti (Boadi, et al, 2015), (Siregar, 2019) menerapkan model Zero-Inflated Poisson untuk mengatasi masalah overdispersi pada model poissonnya.…”

Understanding wildfires in mainland Spain. A comprehensive analysis of fire regime features in a climate-human context

“…In this sense, we employed Negative Binomial to model number of fires (N) and log linear distribution in burned area variables (B500, BL). NB is found particularly suitable to deal with zero-inflated response variables as is the case of N (Boadi, Harvey, & Gyeke-dako, 2015). On the other hand, we have applied log linear family in burned area fire features (Hernandez, Keribin, Drobinski, & Turquety, 2015).…”

Improving emergency preparedness to cascading disasters: A case‐driven risk ontology modelling

“…It is conductive to describe potential threat in the target scenario and build the corresponding risk portrait. Historical cases can be the important resources to build single‐disaster or multi‐disaster risk model with the support of the related data analysis, such as landslide risk modelling with statistical method (Remondo, Bonachea, & Cendrero, 2005), fire risk description based on empirical probability distribution model (Boadi, Harvey, & Gyeke‐dako, 2015), multiple disaster risk modelling using the RiskScape system (Schmidt et al., 2011) and spatial distribution analysis of five types of natural disaster risk using GIS methods (El Morjanil, Ebener, Boos, Abdel Ghaffar, & Musani, 2007). These studies are all general analysis to build a comprehensive disaster risk model for a specific district from a macro perspective.…”