Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model

作者：Abolfazl Abdollahi, Biswajeet Pradhan · 发表于：The Science of The Total Environment · 年份：2023 · DOI：10.1016/j.scitotenv.2023.163004 · 被引用次数：182 · 研究领域：Fire effects on ecosystems、Flood Risk Assessment and Management、Landslides and related hazards

One of the worst environmental catastrophes that endanger the Australian community is wildfire. To lessen potential fire threats, it is helpful to recognize fire occurrence patterns and identify fire susceptibility in wildfire-prone regions. The use of machine learning (ML) algorithms is acknowledged as one of the most well-known methods for addressing non-linear issues like wildfire hazards. It has always been difficult to analyze these multivariate environmental disasters because modeling can be influenced by a variety of sources of uncertainty, including the quantity and quality of training procedures and input variables. Moreover, although ML techniques show promise in this field, they are unstable for a number of reasons, including the usage of irrelevant descriptor characteristics when developing the models. Explainable AI (XAI) can assist us in acquiring insights into these constraints and, consequently, modifying the modeling approach and training data necessary. In this research, we describe how a Shapley additive explanations (SHAP) model can be utilized to interpret the results of a deep learning (DL) model that is developed for wildfire susceptibility prediction. Different contributing factors such as topographical, landcover/vegetation, and meteorological factors are fed into the model and various SHAP plots are used to identify which parameters are impacting the prediction model, their relative importance, and the reasoning behind specific decisions. The finding...