Landslide Susceptibility Mapping With Random Forest Model for Ordu, Turkey

Abstract

Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.

Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

“…There were four ML and one AI algorithm employed in this study. They are distributed random forest (DRF) [30], generalized linear model (GLM) [31], extreme gradient boosting (XGBoost) [32], generalized boosting machine (GBM) [33], and deep learning (DL) [34]- [36]. These algorithms were trained as binomial classifiers as characterized by the training datasets of each method.…”

A Comprehensive Assessment of XGBoost Algorithm for Landslide Susceptibility Mapping in the Upper Basin of Ataturk Dam, Turkey

“…With the advancements in machine learning (ML) algorithms, geospatial technologies and computational power, production of LS maps have become less challenging. Among the common ML algorithms used for the LS mapping in the literature, the artificial neural networks (ANN), logistic regression (LR), deep learning methods, decision trees, random forest (RF), naïve Bayes tree, fuzzy logic and support vector machine (SVM) can be listed (e.g., see [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]). The ML techniques can increase the accuracy of the LS maps [23].…”

Evaluation of Floods and Landslides Triggered by a Meteorological Catastrophe (Ordu, Turkey, August 2018) Using Optical and Radar Data

“…The orthophotos and the grid DSM/DTM have resolutions of 30 cm and 1 m, respectively, with a location accuracy of ca. 15 cm [61].…”

“…20% of the landslides were mapped on Early-Middle Eocene aged sandstone and mudstone, 20% of the landslides were mapped on Maastrichtian-Palaeocene aged mudstone, limestone, sandstone, and marl, and 16% of the landslides mapped on Middle-Late Eocene aged andesite, basalt, and pyroclastic rocks. Six topographic parameters, altitude, slope gradient, slope aspect, plan and profile slope curvatures, topographic wetness index, and a hydrological factor distance to drainage network were evaluated as the landslide preparatory parameters in the region [61]. Descriptive statistics of these parameters for the subarea and the area with landslides were investigated, respectively (Table 1).…”