Nonlinear regression technique to assess the landslide susceptibility of the Kalapahar hill, Guwahati, Assam State (India).

Maria Giuseppina Persichillo (a), Parag Jyoti Dutta (b), Massimiliano Bordoni (a), Claudia Meisina (a), Carlotta Bartelletti (c), Michele Barsanti (d), Roberto Giannecchini (c), Giacomo D'Amato Avanzi (c), Yuri Galanti (c) & Andrea Cevasco(e)
(a) Department of Earth and Environmental Sciences, University of Pavia, Via Ferrata, 1, 27100, Pavia, Italy. E-mail: mariagiuseppin.persichillo01@universitadipavia.it (b) Department of Geology, Cotton College, Guwahati – 781001 (Assam) (c) Department of Earth Sciences, University of Pisa, Via S. Maria, 53, 56126, Pisa, Italy (d) Department of Civil and Industrial Engineering, University of Pisa, Largo L. Lazzarino, 2, 56122 Pisa, Italy (e) Department of Earth, Environment and Life Sciences, University of Genova, Corso Europa, 26, 16132, Genova, Italy

In the past few decades, the increasing frequency of landslides has become a concern for Guwahati city (capital of the State of Assam), especially around the low lying hills. In particular, the Kalapahar hill is one of the major landslide-prone areas of Guwahati, due to its peculiar geomorphological aspects.. The area is characterized by steep slopes underlying loose unconsolidated soil, which lead to frequent slope failure, especially during the monsoon season. Moreover, the intense urbanization of the hills has led to slope instability, making these areas more vulnerable. This paper provides a preliminary study on landslide susceptibility assessment, representing a first step towards landslide risk reduction. In particular, a semi-parametric nonlinear regression method, namely the GAM (Generalized Additive Model), was applied for landslide susceptibility mapping.

Landslides susceptibility assessment, nonlinear regression, Generalized additive model, susceptibility mapping.