Developing a framework through Analytical Models on Google Earth Engine for Landslide Susceptibility Assessment in the Daunia area, southern Italy

Generating a landslide susceptibility map taking into account casual factors like slope geometry, soil/rock types, river location, groundwater conditions, rainfall data and human activities, also including the infrastructures at risk, in order to accurately evaluate the proneness to landslides at fine spatial resolution is a highly-demanding task. Using high-quality data from 3298 different generations of landslides and non-landslides, a framework using Google Earth Engine has been efficiently developed for evaluating landslide susceptibility in the Daunia area of the Italian Southern Apennines, a sector extensively affected by gravitational phenomena of different typologies in Apulia region (Southern Italy). Casual factors including internal (predisposing) and external (preparatory and triggering) factors have been considered to be used within Spatial Data Modellers (SDM). Further, a cloud computing platform via algorithmic models, easily to update, has been created to derive a susceptibility map at the regional scale, especially useful in areas with highly complicated topography. To this purpose, different methods have been compared, including Fuzzy logic methods (Gamma, Product, Sum, And, and Or), as well as machine learning algorithms, such as RF (random forest) and GTB (GradientTreeBoost). The results have been represented via classification and regression modes. A performance analysis has been also carried out and the best modeling performance is observed to belong to the RF algorithms, as provided by Root Mean Squared Error (RMSE): 0.05, R-squared: 0.9825 (regression mode), and Accuracy: 0.8409 (84.09%), 95% CI : (0.8102, 0.8683), P-Value : < 2.2e-16, Kappa : 0.7626 (classification mode), Kappa statistic measures the agreement between the observed accuracy and the accuracy that would be expected by chance. A Kappa of 0.7626 indicates substantial agreement. Considering the Pearson correlation matrix heatmap, visually representing the Pearson correlation coefficients between pairs of variables, it is observed that rainfall, lithology and slope geometry can have the strongest impact on the occurrence of landslides in Daunia. The framework developed in this study is supposed to be applied not only in the region under study, but also in other landslide-prone areas around the world.