Modeling the spread of spatio-temporal phenomena through the incorporation of ANFIS and genetically controlled cellular automata: a case study on forest fire

Abstract

Virtual representation and simulation of spatio-temporal phenomena is a promising goal for the production of an advanced digital earth. Spread modeling, which is one of the most helpful analyses in the geographic information system (GIS), plays a prominent role in meeting this objective. This study proposes a new model that considers both aspects of static and dynamic behaviors of spreadable spatio-temporal in cellular automata (CA) modeling. Therefore, artificial intelligence tools such as adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) were used in accordance with the objectives of knowledge discovery and optimization. Significant conditions in updating states are considered so traditional CA transition rules can be accompanied with the impact of fuzzy discovered knowledge and the solution of spread optimization. We focused on the estimation of forest fire growth as an important case study for decision makers. A two-dimensional cellular representation of the combustion of heterogeneous fuel types and density on non-flat terrain were successfully linked with dynamic wind and slope impact. The validation of the simulation on experimental data indicated a relatively realistic head-fire shape. Further investigations showed that the results obtained using the dynamic controlling with GA in the absence of static modeling with ANFIS were unacceptable.