Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

By Binh Thai Pham, D. Tien Bui, Indra Prakash, M. Dholakia. 2017

577 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management, Monitoring, Policy and Law, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/4fdfc11221c9a2a2aa2492799dd80b45fd42f76c

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In Artificial Intelligence

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In Mechanical Engineering

Citations in the corpus

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN) (2018, methodology): “In addition, the integration of these models into a hybrid model was also found to improve the base model accuracy (Pham et al. 2017).”
A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping (2018, result): “Pham et al. (2017a) compared the performances of AdaBoost, Bagging, MultiBoost, SVM, RotFor fuzzy rule-based Classifier Ensemble, and fuzzy unordered rules induction algorithms.”
A novel hybrid artificial intelligence approach for flood susceptibility assessment (2017, methodology): “Machine learning ensembles and hybrid methods have recently been shown to perform better than conventional methods (Pham et al., 2016b, 2017); however, these methods have not been used for flood susceptibility modeling.”
A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, methodology): “Recently, new ensemble methods have been used in LSM studies due to their novelty and comprehensive evaluation of landslide-related independent variables in each class of independent layers (Hong et al. 2015; Pham et al. 2017b).”
Application and Comparison of Decision Tree-Based Machine Learning Methods in Landside Susceptibility Assessment at Pauri Garhwal Area, Uttarakhand, India (2017, background): “Sensitivity is the probability of landslide pixels classified correctly as Blandslide^ class whereas specificity is the probability of non-landslide pixels classified correctly as Bnon-landslide^ class (Pham et al. 2017b).”
A Survey on ensemble learning under the era of deep learning (2021, methodology): “…of hypersensitive sites (Liu et al. 2016a) 191 2016 (Wei et al. 2018) 91 2018 TA4 Landslide susceptibility mapping (Chen et al. 2017) 176 2017 (Pham et al. 2017) 290 2017 (Dou et al. 2020) 70 2020 TA5 Short-term load forecasting (Li et al. 2020) 23 2020 TA6 Multi-class imbalance learning (Bi…”
Novel Ensemble Forecasting of Streamflow Using Locally Weighted Learning Algorithm (2021, methodology): “[43] that this approach be used to prevent over-fitting issues and to cope with the most unnecessary datasets.”