Mapping Susceptibility With Open-Source Tools: A New Plugin for QGIS - Titti, Giacomo; Sarretta, Alessandro; Lombardo, Luigi; Crema, Stefano; Pasuto, Alessandro; Borgatti, Lisa (Frontiers in Earth Science, 2022)
In this paper, a new tool for quantitative, data-driven Susceptibility Zoning (SZ) is presented. The SZ-plugin has been implemented as a QGIS plugin to maximize its operational use within the geoscientific community. QGIS is in fact \textcolor{red}{a commonly used} open-source geographic information system. We have scripted the plugin in Python, and developed it as a collection of functions that allow one to pre-process the input data, calculate the susceptibility and then estimate the quality of the classification results. The susceptibility zoning can be carried out via a number of classifiers including Weight of Evidence, Frequency Ratio, Logistic Regression, Random Forest, Support Vector Machine and Decision Tree. The plugin allows to use any kind of mapping units, to fit the model and to test it via a k-fold cross-validation, and visualize the relative \textcolor{red}{Receiving Operating Characteristic (ROC)} curves. Moreover, a new classification method of the Susceptibility Index \textcolor{red}{(SI)} has been implemented in the SZ-plugin. A typical work-flow of the SZ-plugin is described and its application for landslide susceptibility zoning in the north-east of India is reported. The data of the predisposing factors used are open and the analysis has been carried out using a Logistic Regression and Weight of Evidence models. The corresponding Area Under the Curve of the relative ROC curves reflect an optimal model prediction capacity. The user-friendly graphical interface of the plugin has allowed to perform the analysis efficiently in few steps.