Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Moayedi, Hossein; Mehrabi, Mohammad; Kalantar, Bahareh; Abdullahi, Mu’azu Mohammed; Rashid, Ahmad Safuan A.; Foong, Loke Kok; Nguyen, Hoang

doi:10.1080/19475705.2019.1650126

Cited by 80 publications

(31 citation statements)

References 85 publications

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“…Furthermore, many scholars have proven that the incorporation of hybrid metaheuristic algorithms with a regular predictive algorithm can result in computational drawbacks like local minima [24][25][26][27]. Studies such as [28,29] have demonstrated the potential of the ANFIS to be hybridized in order to naturalize hazard modeling, such as landslide and flood hazards. In the field of SSS, different works have explored the use of hybrid models for optimizing typical predictors.…”

Section: Introductionmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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Two novel hybrid predictors are suggested as the combination of artificial neural network (ANN), coupled with spotted hyena optimizer (SHO) and ant lion optimization (ALO) metaheuristic techniques, to simulate soil shear strength (SSS). These algorithms were applied to the ANN for counteracting the computational drawbacks of this model. As a function of ten key factors of the soil (including depth of the sample, percentage of sand, percentage of loam, percentage of clay, percentage of moisture content, wet density, liquid limit, plastic limit, plastic Index, and liquidity index), the SSS was considered as the response variable. Followed by development of the ALO–ANN and SHO–ANN ensembles, the best-fitted structures were determined by a trial and error process. The results demonstrated the efficiency of both applied algorithms, as the prediction error of the ANN was reduced by around 35% and 18% by the ALO and SHO, respectively. A comparison between the results revealed that the ALO–ANN (Error = 0.0619 and Correlation = 0.9348) performs more efficiently than the SHO–ANN (Error = 0.0874 and Correlation = 0.8866). Finally, an SSS predictive formula is presented for use as an alternative to the difficult traditional methods.

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Section: Introductionmentioning

confidence: 99%

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

et al. 2019

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“…As is known, landslide susceptibility mapping is an essential prerequisite for landslide risk management [5,6]. A proper landslide risk assessment entails determining the effective landslide parameters for discovering the spatial relationship between them and occurred landslides.…”

Section: Introductionmentioning

confidence: 99%

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Mehrabi

Pradhan

Moayedi

et al. 2020

Sensors

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Four state-of-the-art metaheuristic algorithms including the genetic algorithm (GA), particle swarm optimization (PSO), differential evolutionary (DE), and ant colony optimization (ACO) are applied to an adaptive neuro-fuzzy inference system (ANFIS) for spatial prediction of landslide susceptibility in Qazvin Province (Iran). To this end, the landslide inventory map, composed of 199 identified landslides, is divided into training and testing landslides with a 70:30 ratio. To create the spatial database, thirteen landslide conditioning factors are considered within the geographic information system (GIS). Notably, the spatial interaction between the landslides and mentioned conditioning factors is analyzed by means of frequency ratio (FR) theory. After the optimization process, it was shown that the DE-based model reaches the best response more quickly than other ensembles. The landslide susceptibility maps were developed, and the accuracy of the models was evaluated by a ranking system, based on the calculated area under the receiving operating characteristic curve (AUROC), mean absolute error, and mean square error (MSE) accuracy indices. According to the results, the GA-ANFIS with a total ranking score (TRS) = 24 presented the most accurate prediction, followed by PSO-ANFIS (TRS = 17), DE-ANFIS (TRS = 13), and ACO-ANFIS (TRS = 6). Due to the excellent results of this research, the developed landslide susceptibility maps can be applied for future planning and decision making of the related area.

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“…The recent advances in metaheuristic science led to the proper optimization of different engineering parameters. Moreover, it was proven that synthesizing these algorithms with typical predictive models led to raising their accuracy through prevailing computational weaknesses [31][32][33]. In the field of shear strength analysis, well-known optimization algorithms such as the cuckoo search optimization (CSO), particle swarm optimization (PSO), and genetic algorithm (GA) were applied for the performance improvement of intelligent approaches of least-squares SVM, support vector regression (SVR), and adaptive neuro-fuzzy inference system (ANFIS) [6,34,35].…”

Section: Introductionmentioning

confidence: 99%

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

2020

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The prediction aptitude of an artificial neural network (ANN) is improved by incorporating two novel metaheuristic techniques, namely, the shuffled frog leaping algorithm (SFLA) and wind-driven optimization (WDO), for the purpose of soil shear strength (simply called shear strength) simulation. Soil information of the Trung Luong national expressway project (Vietnam) including depth of the sample (m), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content, wet density (kg/m3), liquid limit (%), plastic limit (%), plastic index (%), liquidity index, and the shear strength (kPa) was collocated through a field survey. After constructing the hybrid ensembles of SFLA–ANN and WDO–ANN, both models were optimized in terms of complexity using a population-based trial-and error-scheme. The learning quality of the ANN was compared with both improved versions to examine the effect of the used metaheuristic techniques. In this phase, the training error dropped by 14.25% and 28.25% by applying the SFLA and WDO, respectively. This reflects a significant improvement in pattern recognition ability of the ANN. The results of the testing data revealed 25.57% and 39.25% decreases in generalization (i.e., testing) error. Moreover, the correlation between the measured and predicted shear strengths (i.e., the coefficient of determination) rose from 0.82 to 0.89 and 0.92, which indicates the efficiency of both SFLA and WDO metaheuristic techniques in optimizing the ANN.

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Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Cited by 80 publications

References 85 publications

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Spotted Hyena Optimizer and Ant Lion Optimization in Predicting the Shear Strength of Soil

Optimizing an Adaptive Neuro-Fuzzy Inference System for Spatial Prediction of Landslide Susceptibility Using Four State-of-the-art Metaheuristic Techniques

Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron

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