![]() The daily and spatially distributed flow condition (flowing or dry) is predicted using the J2000 distributed hydrological model coupled with a Random Forest classification model. The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in meso-scaled river networks (between 120 km 2 and 350 km 2). Within the framework of the Horizon 2020 DRYvER (Drying River Networks and Climate Change) project, a hydrological modelling study of flow intermittence in rivers is being carried out in 3 European catchments (Spain, Finland, France) characterized by different climate, geology and anthropogenic use. However, understanding and predicting the hydrological mechanisms that control periodic drying and rewetting in rivers is challenging due to a lack of studies and hydrological observations, particularly in non-perennial rivers. With climate change and increasing anthropogenic water demand, more frequent and prolonged periods of drying in river systems are expected, endangering biodiversity and river ecosystems. Rivers are rich in biodiversity and act as ecological corridors for plant and animal species. Variable importance: The importance measure for a given variable is the mean error increase of a tree when the observed values of this variable are randomly exchanged in the OOB samples.Abstract. OOB error evolution: Activate this option to display the table showing the evolution of the OOB error according to the number of trees. OOB times: Activate this option to display for each observation of the learning sample the number of times it was OOB.Ĭonfusion matrix (classification only): Activate this option to display the table showing the numbers of well- and badly-classified observations for each of the categories. OOB predictions details: Activate this option to display OOB predictions details OOB predictions: Activate this option to display the vector of Out-Of-Bag predictions. OOB error: Activate this option to display the Out-Of-Bag error of the forest. Results for classification and regression random forests in XLSTAT Past that time, if the desired number of trees in the forest could not be built, the algorithm stops and returns the results obtained using the trees built until then. Construction time (in seconds): Enter the maximum time allowed for the construction of all trees in the forest.The construction of a tree does not continue unless the overall impurity is reduced by at least a factor CP. Complexity parameter (classification only): Enter the value of the complexity parameter (CP).Maximum depth: Enter the maximum tree depth.Minimum child size: Enter the minimum number of observations that every newly created node must contain after a possible split in order to allow the splitting.Minimum parent size: Enter the minimum number of observations that a node must contain to be split.Number of trees: Enter the desired number of trees q in the forest. Sample size: Enter the size k of the sample to generate for the tree's construction. Sampling method: Observations are chosen randomly and may occur only once or several times in the sample. The following options are proposed to configure the set-up of a random forest within XLSTAT: Its objective is to increase the independence between the models (trees), in order to obtain a final model with better performance. Random Input Selection: The Random Input variant is an important modification of the bagging. The aggregation step allows then to obtain a robust and more efficient predictor. Bagging for "Bootstrap aggregating" proposed by Breiman (1996), and Random Input introduced by Breiman in (2001).īagging: The idea here is the following: build CART trees from different bootstrap samples, modify the predictions, and so build a varied collection of predictors. Options for classification and regression random forests in XLSTAT The general principle of the method is to aggregate a collection of predictors (here CART trees) in order to obtain a more efficient final predictor. In regression (continuous response variable): The model allows to build a predictive model for a quantitative response variable based on explanatory quantitative and / or qualitative variables.In classification (qualitative response variable): The model allows predicting the belonging of observations to a class, on the basis of explanatory quantitative and/or qualitative variables.The method implements binary decision trees, in particular, CART trees proposed by Breiman et al. Random forests provide predictive models for classification and regression. ![]()
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