Burned area prediction with semiparametric models

Abstract

Wildfires are one of the main causes of forest destruction, especially in Galicia (north-west Spain), where the area burned by forest fires in spring and summer is quite high. This work uses two semiparametric time-series models to describe and predict the weekly burned area in a year: autoregressive moving average (ARMA) modelling after smoothing, and smoothing after ARMA modelling. These models can be described as a sum of a parametric component modelled by an autoregressive moving average process and a non-parametric one. To estimate the non-parametric component, local linear and kernel regression, B-splines and P-splines were considered. The methodology and software were applied to a real dataset of burned area in Galicia for the period 1999–2008. The burned area in Galicia increases strongly during summer periods. Forest managers are interested in predicting the burned area to manage resources more efficiently. The two semiparametric models are analysed and compared with a purely parametric model. In terms of error, the most successful results are provided by the first semiparametric time-series model.

Predictive Modeling of Wildfire Occurrence and Damage in a Tropical Savanna Ecosystem of West Africa

“…These forecasts were based essentially on historical values (business as usual scenario) and could quickly become obsolete in the case of adopting innovative fire prevention strategies or emerging better or worse environmental conditions in the study area. These results were close to those of Boubeta et al [62] and Viganó et al [63], who respectively used the ARIMA models for the prediction of burned areas in Spain and the occurrence of forest fires in the Pantanal in Brazil. To increase the confidence and relevance of our results, it would be very useful for similar analyses to be conducted within the study area or in other watersheds of the country, such as Bandama and Comoé, which are also undergoing wildfires.…”

“…In the framework of this study, we forecasted fire activity (number of wildfires and burned areas) using seasonal ARIMA models. To apply this method, the following three stages proposed by Box and Jenkins should be followed: Identification, estimation, and diagnosis [58,62,[76][77][78]. The…”

“…In the framework of this study, we forecasted fire activity (number of wildfires and burned areas) using seasonal ARIMA models. To apply this method, the following three stages proposed by Box and Jenkins should be followed: Identification, estimation, and diagnosis [58,62,[76][77][78]. The stationarity of the time series was evaluated using the Augmented Dickey-Fuller (ADF) and KPSS (Kwiatkowski-Phillips-Schmidt-Shin) tests, preceded by a Box-Cox transformation aimed at stabilizing the variance.…”

“…Boubeta et al [62] used two semi-parametric time series models to describe and predict weekly burned areas in a year using ARIMA modeling combined with smoothing. Viganó et al [63] used ARIMA models for predicting the occurrence of forest fires in the Pantanal in Brazil.…”

Evolution of Burned Area in Forest Fires under Climate Change Conditions in Southern Spain Using ANN

“…Actually, despite the large number of wildfires in Spain in the last decades, only a small number of cases, the "huge fires" [77], are responsible for the majority of losses. Likewise, recent Spanish studies [29,78] propose similar classes to obtain homogenous series [29]. Moreover, in the studied region, as can be seen in Table 1, fires larger than 30,000 m 2 covered nearly 80% of the total burned area in the region during the studied period, and their prediction and evolution was therefore the goal of this study.…”