Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS

By D. Tien Bui, Biswajeet Pradhan, Haleh Nampak, Quang-Thanh Bui, Quynh Tran, Quoc-Phi Nguyen. 2016

328 citations recorded by the source corpus.

Subfields: Atmospheric Science, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e2d1964604588d8b9ad89e1b19c420a19d654707

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Citations in the corpus

A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area (2017, methodology): “…et al., 2016; Teodoro et al., 2015; Teodoro and Duarte, 2013), due to its capacity to collect and process spatial data of large regions with different scales and resolutions (Bonham-Carter, 2014; Chuvieco et al., 2010; Tien Bui et al., 2016d; Verde and Zêzere, 2010; Wittenberg and Malkinson, 2009).”
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, methodology): “Although there are a variety of error indices that can be used to assess the predictive capability of the models, many studies advocate the use of RMSE as a standard metric for model errors in geosciences [129].”
Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods (2018, methodology): “The statistical evaluation measures of overall accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) were applied to measure comparative performance of our models (Tien Bui et al. 2016).”
Prediction of Compressive Strength of Geopolymer Concrete Using Entirely Steel Slag Aggregates: Novel Hybrid Artificial Intelligence Approaches (2019, unknown): “[60], and another modeling parameter of the PSO, the inertia weight, was set as 0.”
Spatial prediction of groundwater spring potential mapping based on an adaptive neuro-fuzzy inference system and metaheuristic optimization (2018, methodology): “In addition, the mean squared error (MSE) was further used (Bui et al., 2016) as follows: MSE= ∑n i=1(Oi −Ei) 2 N , (8) whereOi andEi are observation (target) and prediction (output) values in both training and testing datasets, and N is the total samples in the training or the testing dataset.”
A novel hybrid artificial intelligence approach for flood susceptibility assessment (2017, methodology): “Flood conditioning factors Factors, that affect flood susceptibility mapping, may vary, depending on the watershed characteristics (Bui et al., 2016a) and determination of these factors is vital for flood modeling (Sanyal and Lu, 2004).”