Twisting Theory: ANew Artificial Adaptive System for Landslide Prediction

first_pagesettingsOrder Article Reprints Open AccessArticle Twisting Theory: A New Artificial Adaptive System for Landslide Prediction by Paolo Massimo Buscema 1,2,*ORCID,Weldon A. Lodwick 2,Masoud Asadi-Zeydabadi 2,Francis Newman 2,Marco Breda 1ORCID,Riccardo Petritoli 1,Giulia Massini 1,David Buscema 1,Donatella Dominici 3ORCID andFabio Radicioni 4ORCID 1 Semeion Research Center of Sciences of Communication, 00128 Rome, Italy 2 Department of Mathematical and Statistical Sciences, University of Colorado, Denver, CO 80204, USA 3 Department of Civil, Construction-Architectural and Environmental Engineering, University of L’Aquila, 67100 L’Aquila, Italy 4 Department of Engineering, University of Perugia, 06123 Perugia, Italy * Author to whom correspondence should be addressed. Geosciences 2023, 13(4), 115; https://doi.org/10.3390/geosciences13040115 Submission received: 29 December 2022 / Revised: 22 March 2023 / Accepted: 24 March 2023 / Published: 12 April 2023 (This article belongs to the Special Issue Geophysical Risks: The Future of Observatories, The Observatories of the Future) Downloadkeyboard_arrow_down Browse Figures Versions Notes Abstract Landslides pose a significant risk to human life. The Twisting Theory (TWT) and Crown Clustering Algorithm (CCA) are innovative adaptive algorithms that can determine the shape of a landslide and predict its future evolution based on the movement of position sensors located in the affected area. In the first part of this study, the TWT and CCA will be thoroughly explained from a mathematical and theoretical perspective. In the second part, these algorithms will be applied to real-life cases, the Assisi landslide (1995–2008) and the Corvara landslide (2000–2008). A correlation of 0.9997 was attained between the model estimates and the expert’s posterior measurements at both examined sites. The results of these applications reveal that the TWT can accurately identify the overall shape of the landslides and predict their progression, while the CCA identifies complex cause-and-effect relationships among the sensors and represents them in a clear, weighted graph. To apply this model to a wider area and secure regions at risk of landslides, it is important to emphasize its operational feasibility as it only requires the installation of GNSS sensors in a predetermined grid in the target area.