A Tool for Classification and Regression Using Random Forest Methodology: Applications to Landslide Susceptibility Mapping and Soil Thickness Modeling
| Autor: | Nicola Casagli, Samuele Segoni, Filippo Catani, Veronica Tofani, Daniela Lagomarsino |
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| Rok vydání: | 2017 |
| Předmět: |
Landslide susceptibility maps
010504 meteorology & atmospheric sciences Mean squared error Classification and regression Feature selection Random forest Computer science 0211 other engineering and technologies 02 engineering and technology computer.software_genre 01 natural sciences MATLAB 0105 earth and related environmental sciences General Environmental Science computer.programming_language Graphical user interface 021110 strategic defence & security studies business.industry Mode (statistics) Landslide Regression Data mining business computer |
| Zdroj: | Environmental Modeling & Assessment. 22:201-214 |
| ISSN: | 1573-2967 1420-2026 |
| Popis: | Classification and regression problems are a central issue in geosciences. In this paper, we present Classification and Regression Treebagger (ClaReT), a tool for classification and regression based on the random forest (RF) technique. ClaReT is developed in Matlab and has a simple graphic user interface (GUI) that simplifies the model implementation process, allows the standardization of the method, and makes the classification and regression process reproducible. This tool performs automatically the feature selection based on a quantitative criterion and allows testing a large number of explanatory variables. First, it ranks and displays the parameter importance; then, it selects the optimal configuration of explanatory variables; finally, it performs the classification or regression for an entire dataset. It can also provide an evaluation of the results in terms of misclassification error or root mean squared error. We tested the applicability of ClaReT in two case studies. In the first one, we used ClaReT in classification mode to identify the better subset of landslide conditioning variables (LCVs) and to obtain a landslide susceptibility map (LSM) of the Arno river basin (Italy). In the second case study, we used ClaReT in regression mode to produce a soil thickness map of the Terzona catchment, a small sub-basin of the Arno river basin. In both cases, we performed a validation of the results and a comparison with other state-of-the-art techniques. We found that ClaReT produced better results, with a more straightforward and easy application and could be used as a valuable tool to assess the importance of the variables involved in the modeling. |
| Databáze: | OpenAIRE |
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