Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran

Farrokhzad, F.; Barari, Amin; Ibsen, Lars Bo; Choobbasti, Asskar Janalizadeh

doi:10.2478/v10096-011-0034-7

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…Babol, a city in the Mazandaran province in the northern part of Iran, is our study area. The city is located approximately 20 kilometers south of the Caspian Sea, on the west bank of Babolrood River [20]. The geological log of Babol city is presented in Figure 13.…”

Section: Case Study Babol Site Effect Analysismentioning

confidence: 99%

A Study of the Effect of Soil Improvement Based on the Numerical Site Response Analysis of Natural Ground in Babol City

Janalizadechoobbasti

Naghizaderokni

Talebi

2016

OJCE

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A series of numerical calculations have been performed to investigate the effect of soil improvement on seismic site response. Seismic site response analyses were also performed using data collected from a study area in Babol city. The improved site is a composite ground and has more or less different mechanical properties than the natural ground. In this research, the influence of the elastic modulus of the pile, the pile distance ratio, ground motion input, distance to fault rupture, and PGA of the earthquakes on seismic response characteristics are especially investigated. The results reveal that the values of the PGA and amplification factor on the surface of the natural and improved grounds depend strongly on the fundamental period of the site, the predominant period, and the intensity of the ground motion input. The acceleration response spectra also are affected by the characteristics of ground motion input and soil layers. Changing the pile distance ratio doesn't have a significant effect on the seismic response of the site.

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Section: Case Study Babol Site Effect Analysismentioning

confidence: 99%

A Study of the Effect of Soil Improvement Based on the Numerical Site Response Analysis of Natural Ground in Babol City

Janalizadechoobbasti

Naghizaderokni

Talebi

2016

OJCE

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“…The degree of error in the derivative function, which is calculated for stimulation of these neurons in layer k, is multiplied. This error is used to adjust the network weights in the next step (Farrokhzad et al 2011b). ■ Quick propagation method: This method uses the variables as a group, while in the back propagation method the weight changes after every application of the training data.…”

Section: Methodsmentioning

confidence: 99%

Mapping of soil layers using artificial neural network (case study of Babol, northern Iran)

Choobbasti

Farrokhzad

Mashaie³

et al. 2015

J. S. Afr. Inst. Civ. Eng.

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Over the last few years, artificial neural networks (ANNs) have been used successfully for modelling all aspects of geotechnical engineering problems. ANNs are a form of artificial intelligence which attempt to mimic the function of the human brain and nervous system. ANNs are well suited to model the complex behaviour of most geotechnical engineering problems. The purpose of this paper was to assess the layering of subsurface soil using ANNs. Assessing the structure of soil layers on a site, depending on the extent of the study area, requires drilling several boreholes and performing several tests which demand considerable time and money. Increasing the knowledge of soil layer properties between boreholes leads to improved understanding of the near-surface geology. ANNs learn from data examples presented to them in order to capture the subtle functional data relationships, even if the underlying relationships are unknown or the physical meaning is difficult to explain. This paper focuses on the information gathered from the boreholes in a range of 40 square kilometres of Babol City in the north of Iran. The data was collected and classified in order to determine the characteristics of the soil layers. To later classify the different layers at different depths and to determine the thickness of each layer at a specified depth, multi-layer neural networks were trained separately. To quantify the neural network performance in estimating the changes of soil layers, some data from the test boreholes was presented to the network for the first time, and the results of neural networks were compared with actual data obtained from site investigations. The results show a high degree of accuracy in prediction by ANN models.

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“…They estimated the possibility of landslides with an accuracy rate of 83%. They have used other soft computing techniques as a capable tool for landslide hazard zonation (Arora et al 2004;Farrokhzad et al 2011). In another case, Oh and Pradhan (2011) used ANFIS tool with four membership functions, including triangular, trapezoidal, generalized bell and polynomial; and introduced ANFIS as a very useful tool in regional landslide susceptibility assessment.…”

Section: Introductionmentioning

confidence: 99%

Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping

Nguyen

Mehrabi

Kalantar

et al. 2019

Geomatics, Natural Hazards and Risk

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As a prevalent disaster, landslides cause severe loss of property and human life worldwide. The specific objective of this study is to evaluate the capability of artificial neural network (ANN) synthesized with artificial bee colony (ABC) and particle swarm optimization (PSO) evolutionary algorithms, in order to draw the landslide susceptibility map (LSM) at Golestan province, Iran. The required spatial database was created from 12 landslide conditioning factors. The area under curve (AUC) criterion was used to assess the integrity of employed predictive approaches. In this regard, the calculated AUCs of 90.10%, 85.70%, 80.30% and 76.60%, respectively, for SI, PSO-ANN, ABC-ANN and ANN showed that all models have enough accuracy for simulating the LSM, although SI presents the best performance. The landslide vulnerability map obtained by PSO-ANN model is more accurate than other intelligent techniques. In addition, training the ANN with ABC and PSO optimization algorithms conduced to enhancing the reliability of this model. Note that, a total of 76.72%, 23.96%, 30.55% and 5.37% of the study area were labeled as perilous (High and Very high susceptibility classes), respectively by SI, PSO-ANN, ABC-ANN and ANN results.

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Predicting subsurface soil layering and landslide risk with Artificial Neural Networks: a case study from Iran

Cited by 6 publications

References 16 publications

A Study of the Effect of Soil Improvement Based on the Numerical Site Response Analysis of Natural Ground in Babol City

A Study of the Effect of Soil Improvement Based on the Numerical Site Response Analysis of Natural Ground in Babol City

Mapping of soil layers using artificial neural network (case study of Babol, northern Iran)

Potential of hybrid evolutionary approaches for assessment of geo-hazard landslide susceptibility mapping

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