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

Bui, Dieu Tien; Pradhan, Biswajeet; Nampak, Haleh; Bui, Quang‐Thanh; Tran, Quynh-An; Nguyen, Quoc-Phi

doi:10.1016/j.jhydrol.2016.06.027

Cited by 298 publications

(72 citation statements)

References 81 publications

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“…In recent years, a new flood modeling approach called "on-off" classification of flood occurrence has been successfully proposed for spatial prediction of flood (or alternatively called a flood susceptibility index; Tien Bui et al, 2016d;Tehrany et al, 2014Tehrany et al, , 2015b. Accordingly, no time series data are required for the model calibration, and the establishment of flood models is based on flood inventories (flood class) and nonflood areas (nonflood class).…”

Section: A Review Of Related Work On Flood Susceptibility Predictionmentioning

confidence: 99%

“…Mukerji et al (2009) constructed flood forecasting models based on an adaptive neuro-fuzzy interference system (ANFIS), genetic algorithm optimized ANFIS; experiments demonstrated that ANFIS attained the most desirable accuracy. Recently, a metaheuristic optimized neuro-fuzzy inference system, named as MONF, has been introduced by Tien Bui et al (2016c); this research pointed out that MONF is more capable than decision tree, ANN, SVM, and conventional ANFIS methods.…”

Section: A Review Of Related Work On Flood Susceptibility Predictionmentioning

confidence: 99%

“…In Vietnam, floods are often triggered by tropical cyclones. More than 71 % of the Vietnam's population and 59 % of the total land area of Vietnam are susceptible to the impacts of these natural hazards (Tien Bui et al, 2016c). Based on a report by Kreft et al (2014), from 1994 to 2013, Vietnam endured an annual economic loss that is equivalent to USD 2.9 billion.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, an accurate model for evaluat-ing flood hazards for land-use planning becomes a crucial need for land-use planning as well as establishment of disaster mitigation strategies. Based on flood prediction models, flood-prone areas can be identified and mapped (Tien Bui et al, 2016c).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Bui

Hoang

2017

Geosci. Model Dev.

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Abstract. In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

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Section: A Review Of Related Work On Flood Susceptibility Predictionmentioning

confidence: 99%

Section: A Review Of Related Work On Flood Susceptibility Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Bui

Hoang

2017

Geosci. Model Dev.

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“…Hence, this algorithm is employed in the present study to estimate Fr with the ANFIS network. In addition to these algorithms, the more recent use of hybrid ANFIS has led to improved ANFIS prediction results (Cus et al 2009;Shoorehdeli et al 2009;Chang et al 2011;Chen 2013;Bui et al 2016aBui et al , 2017b. Therefore, Differential Evolution (DE) is employed in this study and the results are compared with the hybrid algorithm results.…”

Section: Fourth Layermentioning

confidence: 99%

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Ebtehaj

Bonakdari

2017

Appl Water Sci

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Since the flow entering a sewer contains solid matter, deposition at the bottom of the channel is inevitable. It is difficult to understand the complex, three-dimensional mechanism of sediment transport in sewer pipelines. Therefore, a method to estimate the limiting velocity is necessary for optimal designs. Due to the inability of gradient-based algorithms to train Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for non-deposition sediment transport prediction, a new hybrid ANFIS method based on a differential evolutionary algorithm (ANFIS-DE) is developed. The training and testing performance of ANFIS-DE is evaluated using a wide range of dimensionless parameters gathered from the literature. The input combination used to estimate the densimetric Froude number (Fr) parameters includes the volumetric sediment concentration (C V ), ratio of median particle diameter to hydraulic radius (d/R), ratio of median particle diameter to pipe diameter (d/D) and overall friction factor of sediment (k s ). The testing results are compared with the ANFIS model and regression-based equation results. The ANFIS-DE technique predicted sediment transport at limit of deposition with lower root mean square error (RMSE = 0.323) and mean absolute percentage of error (MAPE = 0.065) and higher accuracy (R 2 = 0.965) than the ANFIS model and regression-based equations.

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Regression‐Based Machine Learning Approaches for Daily Streamflow Modeling

Samadi,

Tabas,

Wilson

et al. 2023

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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

Cited by 298 publications

References 81 publications

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

Design of a fuzzy differential evolution algorithm to predict non-deposition sediment transport

Regression‐Based Machine Learning Approaches for Daily Streamflow Modeling

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