Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran

Rezaei, Khalil; Guest, Bernard; Friedrich, Anke; Fayazi, Farajollah; Nakhaei, Mohammad; Beitollahi, Ali; Aghda, Seyed Mahmoud Fatemi

doi:10.2478/s11600-008-0073-3

Cited by 14 publications

(9 citation statements)

References 42 publications

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“…An effective factor in site effect assessment is the thickness of sediments. Rezaei et al (2009) stated that the soil thickness demonstrated a positive relationship to damage rate observations in the Bam earthquake. This layer was produced by 245 geophysical, geotechnical, and sedimentological sampling sites across the city.…”

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

“…Toward the south and centre of the study area, sediment thickness increases over a short distance, to more than 270 m. This defines a subsurface of high sediment thickness that extends across the entire study area from west to east and underlies south-central Bam. Therefore, based on a positive relationship between the damage rate and alluvial thickness (Rezaei et al, 2009; Marie Nolte, 2010), MF for this criterion is depicted in Fig. 4a.…”

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

“…Among the geotechnical properties of soil and sediments, grain size is one of the most important criteria (Assimaki et al, 2006;Phoon et al, 2006). In the study area, Rezaei et al (2009) identified eight sediment types: clay, silt, sand, granules, pebbles, cobbles, and boulders. They stated that the grain size in the shal-low subsurface (< 10 m) decreases across the city from south to north and increases with depth.…”

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

“…In the central part of the city, fine-grained sediments changed laterally to coarse-grained sediments (poorly sorted sand, well-rounded gravel, poorly sorted gravel, and muddy or sandy gravel), which dominated in the south part of the city. As a rule, it can be assumed that the smaller the grain size of sediments, the lower the shear wave velocity and therefore the greater the effect of the seismic wave on the destruction level of buildings (Rezaei et al, 2009;Assimaki et al, 2006;Phoon et al, 2006). Therefore, the MFs for each specific grain size are calculated in Fig.…”

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

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New approaches to modelling of local seismic amplification susceptibility using direct characteristics of influencing criteria: case study of Bam City, Iran

Hassanzadeh¹,

Honarmand²,

Hosseinjanizadeh³

et al. 2019

Nat. Hazards Earth Syst. Sci.

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Abstract. This paper proposes a new model in evaluating local seismic amplification susceptibility by considering direct characteristics of influencing criteria and it deals with uncertainty of modelling through production of fuzzy membership functions for each criterion. For this purpose, relevant criteria were identified by reviewing previous literature. These criteria include alluvial thickness, stiffness and strength of alluvial deposits, type of soil and particle size distribution of alluvial deposits, depth of groundwater, type of rock, topographic irregularities, slope, and type of bedrock. Two methods, analytic hierarchy process (AHP) and fuzzy logic (FL), were applied in order to define priority rank of each criterion and sub-criteria of each criterion through interview data of 10 experts. The criteria and sub-criteria were combined using the weighted linear combination method in GIS to develop a model for assessing local seismic amplification susceptibility in the study area of Bam City, Iran. The model's output demonstrated high to very high seismic amplification levels in central, eastern, northeastern, and northern parts of the study area. The validation results based on overall accuracy and kappa statistics showed 73.6 % accuracy, with 0.74 kappa indicating a good fit to the model's output. This model assists planners and decision makers in determining local seismic amplification susceptibility to be incorporated in designing new development plans of urban and rural areas and in making informed decisions regarding safety measures of existing buildings and infrastructures.

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Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

Section: B Determining Fuzzy Set and Fuzzification Of Thresholds Of mentioning

confidence: 99%

See 2 more Smart Citations

New approaches to modelling of local seismic amplification susceptibility using direct characteristics of influencing criteria: case study of Bam City, Iran

Hassanzadeh¹,

Honarmand²,

Hosseinjanizadeh³

et al. 2019

Nat. Hazards Earth Syst. Sci.

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show abstract

“…The neural networks (Haykin, 1999), once optimized based on data collected from a sufficient number of different rivers, can be expected to be applicable for determining the necessary parameters for comparable river types. Their successful application to various hydrological problems has been shown in many papers (Jain et al, 1999;Maier & Dandy, 2000;Cigizoglu, 2003;Partal & Cigizoglu, 2009;Rezaei et al, 2009). Following standard practice, the testing of the proposed approach uses a set of breakthrough curves not used during the model building process.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents

Piotrowski

Napiorkowski

Rowiński

et al. 2011

Hydrological Sciences Journal

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In order to predict the impact of pollution incidents on rivers, it is necessary to predict the dispersion coefficient and the flow velocity corresponding to the discharge in the river of interest. This paper explores methods for doing this, particularly with a view to applications on ungauged rivers, i.e. those for which little hydraulic or morphometric data are available. An approach based on neural networks, trained on a wide-ranging database of optimized parameter values from tracer experiments and corresponding physical variables assembled for American and European rivers, is proposed. Tests using independent cases showed that the neural networks generally gave more reliable parameter estimates than a second-order polynomial regression approach. The quality of predictions of temporal concentration profiles was heavily influenced by the accuracy of the velocity prediction.Key words longitudinal dispersion; pollutant transport; ungauged river; neural networks; data-based modelling Evaluation des profils temporels de concentration de cours d'eau non jaugés à la suite d'incidents de pollution Résumé Afin de prévoir l'impact des incidents de pollution dans les rivières, il est nécessaire de prévoir le coefficient de dispersion et la vitesse d'écoulement correspondant au débit dans la rivière considérée. Cet article explore les méthodes pour ce faire, en particulier en vue d'applications à des rivières non jaugées, c'est-à-dire pour lesquelles peu de données hydrauliques ou morphométriques sont disponibles. Une approche basée sur des réseaux neuronaux, entraînés avec une importante base de données de valeurs de paramètres optimisés à partir d'expériences de traçage et de variables physiques correspondantes, assemblée pour des rivières américaines et européennes, est proposée. Des tests utilisant des cas indépendants ont montré que les réseaux de neurones ont généralement donné des estimations de paramètres plus fiables qu'une approche de régression polynomiale du second ordre. La qualité des prévisions des profils temporels de concentration a été fortement influencée par la précision de la prévision de la vitesse.

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Semi-Supervised Learning for Grain Size Distribution Interpolation

Kobs

Schäfer

Steininger

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran

Cited by 14 publications

References 42 publications

New approaches to modelling of local seismic amplification susceptibility using direct characteristics of influencing criteria: case study of Bam City, Iran

New approaches to modelling of local seismic amplification susceptibility using direct characteristics of influencing criteria: case study of Bam City, Iran

Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents

Semi-Supervised Learning for Grain Size Distribution Interpolation

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