A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

Pham, Binh Thai; Prakash, Indra; Chen, Wei; Ly, Haï‐Bang; Ho, Lanh Si; Omidvar, Ebrahim; Phong, Tran Van; Bui, Dieu Tien

doi:10.3390/su11226323

Cited by 42 publications

(22 citation statements)

References 87 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Validation performance is a critical step in a modeling procedure, for which several statistical indices has been suggested and used [13,14,[49][50][51][52]. In this study, we used Area Under Receiver Operating Characteristic (ROC) curve (AUC) [39,[53][54][55][56], Root Mean Squared Error (RMSE) [57][58][59][60][61][62][63][64], Kappa, Accuracy (ACC), Specificity (SPF), Sensitivity (SST), Negative predictive value (NPV), and Positive predictive value (PPV) [65][66][67][68][69]. Detail description of these indices is presented in published literature [61,[70][71][72][73][74][75][76][77].…”

Section: Validation Methodsmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

113

View full text Add to dashboard Cite

The main aim of this study is to assess groundwater potential of the DakNong province, Vietnam, using an advanced ensemble machine learning model (RABANN) that integrates Artificial Neural Networks (ANN) with RealAdaBoost (RAB) ensemble technique. For this study, twelve conditioning factors and wells yield data was used to create the training and testing datasets for the development and validation of the ensemble RABANN model. Area Under the Receiver Operating Characteristic (ROC) curve (AUC) and several statistical performance measures were used to validate and compare performance of the ensemble RABANN model with the single ANN model. Results of the model studies showed that both models performed well in the training phase of assessing groundwater potential (AUC ≥ 0.7), whereas the ensemble model (AUC = 0.776) outperformed the single ANN model (AUC = 0.699) in the validation phase. This demonstrated that the RAB ensemble technique was successful in improving the performance of the single ANN model. By making minor adjustment in the input data, the ensemble developed model can be adapted for groundwater potential mapping of other regions and countries toward more efficient water resource management. The present study would be helpful in improving the groundwater condition of the area thus in solving water borne disease related health problem of the population.

show abstract

Section: Validation Methodsmentioning

confidence: 99%

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Nguyen¹,

Jaafari

et al. 2020

IJERPH

113

View full text Add to dashboard Cite

show abstract

“…The area under this curve is called the AUC, and the model with the highest AUC has the highest relative performance [52][53][54][55][56][57][58][59][60][61]. The AUC values equal to 0.5 indicate random prediction for a model [62][63][64][65][66].…”

Section: Receiver Operating Characteristic (Roc) Curvementioning

confidence: 99%

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Nguyen¹,

Avand

et al. 2020

Applied Sciences

142

View full text Add to dashboard Cite

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans.

show abstract

“…Considering the common process for LSM, these landslide samples are randomly divided into two parts: 304 (70%) samples out of them are used for training and the remaining 130 (30%) samples are retained for the validation of the calibrated susceptibility models (Dou et al 2019;Wang et al 2020). Note that such ratio of training and validating data has been widely used in the literature (Pham et al 2019;Hu et al 2020;Zhao and Chen 2020;Dou et al 2020b) and some internal analyses also found this ratio yield good results.…”

Section: Landslide Inventorymentioning

confidence: 99%

Landslide susceptibility mapping by attentional factorization machines considering feature interactions

Liu

Yang

Huang

et al. 2021

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Landslide susceptibility mapping (LSM) is a commonly used approach to reduce landslide risk. However, conventional LSM methods generally only consider the influence of each single conditioning factor on landslide occurrence or absence, which neglects the interactions of different conditioning factors and may lead to biased LSM results. Therefore, this study aims to use a new machine learning model-attentional factorization machines (AFM)-to explicitly consider the influence of feature interactions in LSM to improve and obtain more reliable LSM results. The Anhua County in China is chosen as the study area. The area under the receiver operating characteristic curve (AUC) and statistical indicators are used to evaluate the performance of LSM models. For comparison, the common LSM models such as the logistic regression (LR) and random forest (RF) models are also used to conduct the LSM. The results show that the performance of AFM is a little better than RF in the AUC metric, whereas the LR model has the worst performance. Compared with general LSM models, AFM considers feature interactions by introducing an attention mechanism to learn the weight of different feature combinations, which not only ensures the model interpretability but also improves the model performance.

show abstract

A Novel Intelligence Approach of a Sequential Minimal Optimization-Based Support Vector Machine for Landslide Susceptibility Mapping

Cited by 42 publications

References 87 publications

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam

Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping

Landslide susceptibility mapping by attentional factorization machines considering feature interactions

Contact Info

Product

Resources

About