Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Moayedi, Hossein; Mehrabi, Mohammad; Mosallanezhad, Mansour; Rashid, Ahmad Safuan A.; Pradhan, Biswajeet

doi:10.1007/s00366-018-0644-0

Cited by 259 publications

(98 citation statements)

References 46 publications

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“…Utilizing different linear and nature-inspired algorithms (like blind naked mole-rat (BNMR) and SIRT algorithms) enabled the scientists to better deal with various engineering issues [22,23]. 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.…”

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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“…Like other simulation methods, the development of intelligent models requires two sets of data, namely, training and testing samples [47]. The majority of data are specified to pattern analysis and training the models.…”

Section: Data Division and Preprocessingmentioning

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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“…Likewise, new evolutionary algorithms have been extensively used to improve the applicability of typical intelligent models (Tien Bui et al 2018;Jaafari et al 2019). In this sense, Moayedi et al (2018a), evaluated the effectiveness of MLP neural network against its improved version, (i.e. ANN synthesized with particle swarm optimization algorithm, PSO-ANN), in landslide hazard assessment at Kermanshah province, west of Iran.…”

Section: Literature Review Of Landslide Susceptibility Mappingmentioning

confidence: 99%

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

Li²,

Moayedi

et al. 2019

Geomatics, Natural Hazards and Risk

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The focal point of this study is to assess the efficacy of a state-ofthe-art optimization technique namely, particle swarm optimization (PSO) for enhancing the performance of the artificial neural network (ANN) in modeling the seismic landslides at Ludian districts, China. Twelve geological and hydrological landslide-conditioning factors namely, elevation, lithology, slope degree, slope aspect, stream power index, peak ground acceleration, topographic wetness index, distance to river, distance to road, distance to fault, normalized difference vegetation index and plan curvature were considered within a geographic information system (GIS). After achieving the optimal structure of the multilayer perceptron neural network, the PSO algorithm was applied to improve its efficiency. The landslide susceptibility maps were generated in a GIS environment and area under the curve (AUC) criterion was used to assess the integrity of employed predictive models. The results showed that after applying the PSO algorithm, AUC experiences a significant increase from 0.765 to 0.825 in the validation phase. Moreover, respective AUCs of 0.812 and 0.828 obtained for the training phase of ANN and PSO-ANN reveal the efficiency of the proposed algorithm in improving the ANN accuracy. ARTICLE HISTORY

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Modification of landslide susceptibility mapping using optimized PSO-ANN technique

Cited by 259 publications

References 46 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

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

A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China

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