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Pregled bibliografske jedinice broj: 897136

Prediction of the Kostanjek Landslide Movements Based on Monitoring Results Using Random Forests Technique


Krkač, Martin; Mihalić Arbanas, Snježana; Arbanas, Željko; Bernat Gazibara, Sanja; Sečanj, Marin
Prediction of the Kostanjek Landslide Movements Based on Monitoring Results Using Random Forests Technique // Advancing Culture of Living with Landslides, Volume 3, Advances in Landslide Technology / Mikoš, Matjaž ; Arbanas, Željko ; Yin, Yueping ; Sassa, Kyoji (ur.).
Cham: Springer, 2017. str. 267-275 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


Naslov
Prediction of the Kostanjek Landslide Movements Based on Monitoring Results Using Random Forests Technique

Autori
Krkač, Martin ; Mihalić Arbanas, Snježana ; Arbanas, Željko ; Bernat Gazibara, Sanja ; Sečanj, Marin

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Advancing Culture of Living with Landslides, Volume 3, Advances in Landslide Technology / Mikoš, Matjaž ; Arbanas, Željko ; Yin, Yueping ; Sassa, Kyoji - Cham : Springer, 2017, 267-275

ISBN
978-3-319-53486-2

Skup
4th World Landslide Forum

Mjesto i datum
Ljubljana, Slovenija, 29.5.-2.6.2017

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Kostanjek landslide ; Movement prediction ; Random forests ; Landslide monitoring

Sažetak
Prediction of landslide movements with practical application for landslide risk mitigation is a challenge for scientists. This study presents a methodology for prediction of landslide movements using random forests, a machine learning algorithm based on regression trees. The prediction method was established based on a time series data gathered by two years of monitoring on landslide movement, groundwater level and precipitation by the Kostanjek landslide monitoring system and nearby meteorological stations in Zagreb (Croatia). Because of complex relations between precipitations and groundwater levels, the process of landslide movement prediction is divided into two separate models: (1) model for prediction of groundwater levels from precipitation data ; and (2) model for prediction of landslide movements from groundwater level data. In a groundwater level prediction model, 75 parameters were used as predictors, calculated from precipitation and evapotranspiration data. In the landslide movement prediction model, 10 parameters calculated from groundwater level data were used as predictors. Model validation was performed through the prediction of groundwater levels and prediction of landslide movements for the periods from 10 to 90 days. The validation results show the capability of the model to predict the evolution of daily displacements, from predicted variations of groundwater levels, for the period up to 30 days.

Izvorni jezik
Engleski

Znanstvena područja
Rudarstvo, nafta i geološko inženjerstvo



POVEZANOST RADA


Ustanove
Građevinski fakultet, Rijeka,
Rudarsko-geološko-naftni fakultet, Zagreb