Applications of Stacking/Blending ensemble learning approaches for evaluating flash flood susceptibility

Yao, Jing; Zhang, Xiaoxiang; Luo, Weicong; Liu, Changjun; Ren, Lei

doi:10.1016/j.jag.2022.102932

Cited by 28 publications

(15 citation statements)

References 25 publications

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“…When all of the attributes are real-valued, rotation forest outperformed the most frequent alternatives. In the present study better result of ensemble models has been found in case of M5P-RTF than other ensemble models applied in different fields like AdaBoost, Bagging, Dagging, MultiBoost, RTF, and RSS where ANN is base classifier in landslide modelling (Pham et al 2017;Wang et al 2020), staking and blending where KNN, RF and SVM were used as base classifiers in flood susceptibility modelling (Yao et al 2022). This work's findings will undoubtedly aid in the formulation of drought relief measures in Odisha and will serve as a reference for future drought research, particularly in terms of strategy creation.…”

Section: Discussionsupporting

confidence: 48%

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Saha

Kundu

Paul

et al. 2023

Stoch Environ Res Risk Assess

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Drought is one of the major barriers to the socio-economic development of a region. To manage and reduce the impact of drought, drought vulnerability modelling is important. The use of an ensemble machine learning technique i.e. M5P, M5P -Dagging, M5P-Random SubSpace (RSS) and M5P-rotation forest (RTF) to assess the drought vulnerability maps (DVMs) for the state of Odisha in India was proposed for the first time. A total of 248 drought-prone villages (samples) and 53 drought vulnerability indicators (DVIs) under exposure (28), sensitivity (15) and adaptive capacity (10) were used to produce the DVMs. Out of the total samples, 70% were used for training the models and 30% were used for validating the models. Finally, the DVMs were authenticated by the area under curve (AUC) of receiver operating characteristics, precision, mean-absolute-error, root-mean-square-error, K-index and Friedman and Wilcoxon rank test. Nearly 37.9% of the research region exhibited a very high to high vulnerability to drought. All the models had the capability to model the drought vulnerability. As per the Friedman and Wilcoxon rank test, significant differences occurred among the output of the ensemble models. The accuracy of the M5P base classifier improved after ensemble with RSS and RTF meta classifiers but reduced with Dagging. According to the validation statistics, M5P-RFT model achieved the highest accuracy in modelling the drought vulnerability with an AUC of 0.901. The prepared model would help planners and decision-makers to formulate strategies for reducing the damage of drought.

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Section: Discussionsupporting

confidence: 48%

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Saha

Kundu

Paul

et al. 2023

Stoch Environ Res Risk Assess

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“…The combined flash flood and non-flash flood samples are then randomly divided into two sets, the training dataset and the test dataset, in a 7:3 ratio (Huang et al, 2022b). (3) To achieve the best prediction accuracy, the model parameters are adjusted by cross-validation (Yao et al, 2022). (4) The model's predicted flash flood susceptibility index is imported into ArcGIS 10.2 software to generate flash flood susceptibility maps.…”

Section: Research Frameworkmentioning

confidence: 99%

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

Chen

Zhang²,

Yang³

et al. 2023

Front. Earth Sci.

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The prediction performance of several machine learning models for regional flash flood susceptibility is characterized by variability and regionality. Four typical machine learning models, including multilayer perceptron (MLP), logistic regression (LR), support vector machine (SVM), and random forest (RF), are proposed to carry out flash flood susceptibility modeling in order to investigate the modeling rules of different machine learning models in predicting flash flood susceptibility. The original data of 14 environmental factors, such as elevation, slope, aspect, gully density, and highway density, are chosen as input variables for the MLP, LR, SVM, and RF models in order to estimate and map the distribution of the flash flood susceptibility index in Longnan County, Jiangxi Province, China. Finally, the prediction performance of various models and modeling rules is evaluated using the ROC curve and the susceptibility index distribution features. The findings show that: 1) Machine learning models can accurately assess the region’s vulnerability to flash floods. The MLP, LR, SVM, and RF models all predict susceptibility very well. 2) The MLP (AUC=0.973, MV=0.1017, SD=0.2627) model has the best prediction performance for flash flood susceptibility, followed by the SVM (AUC=0.964, MV=0.1090, SD=0.2561) and RF (AUC=0.975, MV=0.2041, SD=0.1943) models, and the LR (AUC=0.882, MV=0.2613, SD=0.2913) model. 3) To a large extent, environmental factors such as elevation, gully density, and population density influence flash flood susceptibility.

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“…The latter focuses on the samples mispredicted by the previous basic model in the next basic model and then combines them to output the results 12 . Stacking and blending usually integrate different types of models to seek better performance through the advantages of different models 13 . The difference is that the former is trained through holdout cross‐validation, and the latter is trained through k ‐fold cross‐validation.…”

Section: Introductionmentioning

confidence: 99%

“…12 Stacking and blending usually integrate different types of models to seek better performance through the advantages of different models. 13 The difference is that the former is trained through holdout cross-validation, and the latter is trained through k-fold cross-validation. Random forest (RF) is a well-known algorithm using the bagging idea.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves

et al. 2022

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Background: Oilseed rape, as one of the most important oil crops, is an important source of vegetable oil and protein for mankind. As a non-essential element for plant growth, heavy metal cadmium (Cd) is easily absorbed by plants. Cd will inhibit the photosynthesis of plants, destroy the cell structure, slow the growth of plants, and affect their development and yield. It is necessary to develop a method based on visible near-infrared (NIR) hyperspectral imaging (HSI) technology to quickly and nondestructively determine the Cd content in rape leaves.Results: Two-layer estimation models were established by combining visible-NIR HSI with ensemble learning methods (stacking and blending). One layer used support vector regression, extreme learning machine, decision tree, and random forest (RF) as basic learners, and the other layer used support vector regression or RF as a meta learner. Different models were used to analyze the spectra of rape treated with five Cd concentrations to obtain the best prediction method. The results showed that the best model to predict Cd content was the stacking ensemble model with RF as the meta learner, with coefficient of determination for prediction of 0.9815 and root-mean-square error for prediction of 5.8969 mg kg −1 . A pseudo-color image was developed using this stacking model to visualize the content and distribution of Cd. Conclusion:The combination of visible-NIR HSI technology and the stacking ensemble learning method is a feasible method to detect the Cd content in rape leaves, which has the potential of being rapid and nondestructive.

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Applications of Stacking/Blending ensemble learning approaches for evaluating flash flood susceptibility

Cited by 28 publications

References 25 publications

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models

Modeling rules of regional flash flood susceptibility prediction using different machine learning models

Hyperspectral technique combined with stacking and blending ensemble learning method for detection of cadmium content in oilseed rape leaves

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