基于DRNN和ARIMA模型的森林火灾面积时空综合预测方法

Spatio-Temporal Integrated Forecast Method of Forest Fire Area Based on DRNN and ARIMA Model

森林火灾是一个跨空间发展的动态过程,不易被传统的分析方法和静态神经网络有效处理。提出一种基于动态回归神经网络(DRNN)和自回归集成移动平均(ARIMA)组合模型的森林火灾时空综合预测方法。该方法先用ARIMA对时空数据的时序进行预测,再用DRNN捕获时空数据间隐藏的空间相关,最后用统计回归将时间和空间预测结果组合起来,得到时空综合预测结果。以广东省森林火灾面积预测为例,说明其原理和建模过程,并对预测结果的精度进行验证。结果表明:由于考虑了数据间的空间关系,该时空综合预测模型可以对森林火灾面积进行较准确有效的预测,比单纯应用ARIMA模型预测精度高,是预测森林火灾等跨空间动态变化问题的有效工具。

Forest fire is not easily handled by traditional analysis methods and steady-state neural network because it is a dynamic process over space. A spatial-temporal integrated forecast method of forest fire was proposed in this paper by combining dynamic recurrent neural network （DRNN） and autoregressive integrated moving average（ARIMA） model. The approach first forecasts time series by ARIMA model, and reveals the hidden spatial correlations among forest fire data by DRNN, and then combines the spatial and temporal forecast results based on statistic regressions to produce the final spatial-temporal integrated forecast result. The principle and modeling procedure of the model were illustrated with a case study of forest fire area forecast in Guangdong, and then the forecast accuracy was validated. The results showed that the forest fire area could be forecasted exactly and effectively by the spatial-temporal integrated forecast model because the spatial correlations among data were taken into consideration. Compared with the pure ARIMA model, the forecast precision of the model was apparently improved. The integrated model was also proved to be good efficient in forecasting dynamic change of events over space such as a forest fire.