Statistical Analysis of Landslide Susceptibility, Macerata Province (Central Italy)

نویسندگان

چکیده

Every year, institutions spend a large amount of resources to solve emergencies generated by hydrogeological instability. The identification areas potentially subject risks could allow for more effective prevention. Therefore, the main aim this research was assess susceptibility territories where no instability phenomena have ever been detected. In order obtain type result, statistical assessments problem cannot be ignored. case, it chosen analyse landslide using flexible method that is attracting great interest in international scientific community, namely Weight Evidence (WoE). This model-building procedure, calculating susceptibility, used Geographic Information Systems (GIS) software means mathematical operations between rasters and took into account parameters such as geology, acclivity, land use, average annual precipitation extreme events. Thus, innovative links with triggering factors precipitation. resulting map showed low weight identifying most susceptible landslides, although all included contributed accurate estimate, which necessary preserve human life, buildings, heritage any productive activity.

برای دانلود باید عضویت طلایی داشته باشید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

GIS and statistical analysis for landslide susceptibility mapping in the Daunia area, Italy

This study focuses on landslide susceptibility mapping in the Daunia area (Apulian Apennines, Italy) and achieves this by using a multivariate statistical method and data processing in a Geographical Information System (GIS). The Logistic Regression (hereafter LR) method was chosen to produce a susceptibility map over an area of 130 000 ha where small settlements are historically threatened by ...

متن کامل

Landslide hazard assessment in the Collazzone area, Umbria, Central Italy

We present the results of the application of a recently proposed model to determine landslide hazard. The model predicts where landslides will occur, how frequently they will occur, and how large they will be in a given area. For the Collazzone area, in the central Italian Apennines, we prepared a multi-temporal inventory map through the interpretation of multiple sets of aerial photographs tak...

متن کامل

Landslide susceptibility mapping using logistic regression analysis in Latyan catchment

    Every year, hundreds of people all over the world lose their lives due to landslides. Landslide susceptibility map describes the likelihood or possibility of new landslides occurring in an area, and therefore helping to reduce future potential damages. The main purpose of this study is to provide landslide susceptibility map using logistic regression model at Latyan watershed, north Iran. I...

متن کامل

Landslide Susceptibility Analysis Based on Data Field

The Three Gorges are the areas in which the geological disasters are very serious. There often happen great landslide disasters, which brings tremendous threat to normal running of the Three Gorge Dam and the properties and lives of the residents in the reservoir. So landslide susceptibility analysis is an important task of prevention and cure of landslides in the Three Gorges. In this paper, l...

متن کامل

Improving Landslide Forecasting Using ASCAT-Derived Soil Moisture Data: A Case Study of the Torgiovannetto Landslide in Central Italy

Predicting the spatial and temporal occurrence of rainfall triggered landslides represents an important scientific and operational issue due to the high threat that they pose to human life and property. This study investigates the relationship between rainfall, soil moisture conditions and landslide movement by using recorded movements of a rock slope located in central Italy, the Torgiovannett...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: Hydrology

سال: 2021

ISSN: ['2330-7609', '2330-7617']

DOI: https://doi.org/10.3390/hydrology8010005