BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-europe-2025//speaker//3R MTVM BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-europe-2025-FRJ3M3@talks.staging.osgeo.org DTSTART;TZID=CET:20250716T171500 DTEND;TZID=CET:20250716T172000 DESCRIPTION:Within the Extended Partnership “Multi-Risk sciEnce for resil ienT commUnities undeR a changiNg climate” (RETURN)\, the research group of the Department of Civile\, Chemical and Environmental Engineering of t he University of Genova is developing a system for processing landslide su sceptibility maps in GIS environment.\nThe expected result is a tool meant to be used by administrations and local authorities for ground instabilit y assessment and management. Therefore\, high usability is required\, that implies free and easily available base data\, easily interpretable result s\, and clearly explained limitations of use and reliability level.\nTo ac complish this objective\, some stakes have been established:\n • Only earth landslide are expected to be considered for the processing of susce ptibility maps. They include slow flows\, fast flows\, slides\, areas subj ect to diffuse shallow landslides and those landslides classified as “un determined” and “complex” that generally contain at least some earth movement .\n • The proposed tool should be scalable and transferable . To such purpose\, eight independent predisposing factors were chosen com plying with these requirements and described by data open and available f or the whole Italy. \n • To ensure the quality of input and output da ta\, the procedure is optimized for certain “certified” datasets\n • The procedure has to be transparent and traceable . The logistic regre ssion method was chosen\, which is widely used and described in the litera ture. The resulting maps report probability values that are quite difficul t to understand\, so\, to facilitate usability\, they are subsequently agg regated into qualitative susceptibility classes.\n • Minimum reliabil ity threshold: the procedure has been tuned\, and additional tests are pre sently in course in some study areas\, to ensure an overall reliability de fined through AUC of at least 75%. In case of lower AUC values\, the cause s are investigated\, to foresee possible corrective interventions.\n • In order to allow also people not particularly experienced in GIS to u se the model and to ensure the correct implementation of the planned opera tions\, the writing of the entire procedure in Python code is underway\, a t the moment within GRASS\, then\, possibly as a QGIS plugin. \nThe proced ure was tested in GRASS over areas of about 1\,000 km2\, considering the p ixel as the minimum spatial unit\, with a nominal scale of 1:100\,000 and a raster resolution of 20 m. The first phase consisted of the preprocessin g and discretization of the basic data\, so as to allow a general control of data quality and reduce the possible combinations of factors to a manag eable number. The eight considered factors\, elevation\, slope\, aspect\, water accumulation\, land use/land cover\, lithology and rainfall influenc e\, were then brought into raster format at the set resolution and divided into qualitative (e.g.\, land use type) or ordinal (e.g.\, Elevation Inte rvals\, from 0 to maximum elevation) classes. \nThe resulting maps were co mpared in a bivariate analysis with the Inventory of Landslide Phenomena I n Italy (IFFI)\, and the classes of each factor were reordered on the basi s of conditional probability. The factors were then related to each other and to actual landslides in a multivariate analysis by logistic regression \, defining for each pixel the probability of landslide occurrence. The ob tained values were finally grouped into three qualitative classes to indic ate high\, medium and low landslide susceptibility. \nThe procedure descri bed above resulted in an AUC in calibration generally above the preset thr eshold of 75 percent and was used as a basis for the realization of the ac tual tool\, through a series of refinements currently in progress. \nThe t ests so far have shown that each type of landslide is affected differently by the factors considered\, and the model is more reliable if the differe nt kinematisms are treated separately. Therefore\, for each study area\, t he procedure has to be repeated for all landslide types\, which is time-co nsuming and disk room-consuming. In response to this problem\, the rewriti ng of the model as a python script is in course\, not only for a more effi cient application\, but also to define a standardized procedure by which t o make it accessible to people outside the research team\, and leading to comparable results.\nAs part of the automation of the procedure\, efforts are also being made to define ancillary functions dedicated to solving pro blems that arose during the experimentation. \nWith regard to the definiti on of the statistical sample\, i.e.\, the areas actually in landslide\, th e need has emerged to distinguish\, for the phenomena reported in the IFFI repository\, the detachment area\, i.e.\, the area from which the landsli de actually developed\, and the accumulation area\, i.e.\, the part of the territory affected by the effects of the landslide. Therefore\, to avoid introducing noise into the model due to incorrect perimetry\, a part of th e code is being prepared to separate the two parts on a statistical basis. \nAnother issue that is being addressed is the identification\, for each k inematism and for each territory considered\, of the factors actually dete rmining the development of the landslides\, in order to reduce noise and l ighten the computational processes. Several methods are being tested\, inc luding “Frequency Ratio\,” “Leave One Out\,” and “Stepwise”. O nce the most effective method is determined\, this part will also be intro duced as a script into the model. DTSTAMP:20260527T112602Z LOCATION:PA01 (Quarticle) SUMMARY:Python plugin for statistical analysis of landslides susceptibility over wide areas - Paola Salmona URL:https://talks.staging.osgeo.org/foss4g-europe-2025/talk/FRJ3M3/ END:VEVENT END:VCALENDAR