Non-path dependent urban growth potential mapping using a data-driven evidential belief function

2019 ◽  
pp. 239980831988021
Author(s):  
Reza Arasteh ◽  
Rahim Ali Abbaspour ◽  
Abdolrassoul Salmanmahiny
Keyword(s):  
Urban Growth ◽  
Belief Function ◽  
Growth Potential ◽  
Data Driven ◽  
Evidential Belief Function ◽  
Potential Mapping ◽  
Path Dependent

Groundwater Productivity Potential Mapping Using Evidential Belief Function

Ground Water ◽  
2014 ◽  
Vol 52 (S1) ◽  
pp. 201-207 ◽  
Author(s):  
Inhye Park ◽  
Yongsung Kim ◽  
Saro Lee
Keyword(s):  
Belief Function ◽  
Evidential Belief Function ◽  
Potential Mapping ◽  
Productivity Potential

scholarly journals A computational framework to establish data-driven constitutive models for time- or path-dependent heterogeneous solids

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weijian Ge ◽  
Vito L. Tagarielli
Keyword(s):  
Constitutive Models ◽  
Computational Procedure ◽  
Data Driven ◽  
Dissipated Energy ◽  
Computational Framework ◽  
Heterogeneous Solids ◽  
Non Linear ◽  
Recorded Data ◽  
Path Dependent ◽  
Computational Homogenisation

AbstractWe propose and implement a computational procedure to establish data-driven surrogate constitutive models for heterogeneous materials. We study the multiaxial response of non-linear n-phase composites via Finite Element (FE) simulations and computational homogenisation. Pseudo-random, multiaxial, non-proportional histories of macroscopic strain are imposed on volume elements of n-phase composites, subject to periodic boundary conditions, and the corresponding histories of macroscopic stresses and plastically dissipated energy are recorded. The recorded data is used to train surrogate, phenomenological constitutive models based on neural networks (NNs), and the accuracy of these models is assessed and discussed. We analyse heterogeneous composites with hyperelastic, viscoelastic or elastic–plastic local constitutive descriptions. In each of these three cases, we propose and assess optimal choices of inputs and outputs for the surrogate models and strategies for their training. We find that the proposed computational procedure can capture accurately and effectively the response of non-linear n-phase composites subject to arbitrary mechanical loading.

scholarly journals Groundwater productivity potential mapping using frequency ratio and evidential belief function and artificial neural network models: focus on topographic factors

2018 ◽  
Vol 20 (6) ◽  
pp. 1436-1451 ◽  
Author(s):  
Jeong-Cheol Kim ◽  
Hyung-Sup Jung ◽  
Saro Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Frequency Ratio ◽  
Groundwater Resources ◽  
Specific Capacity ◽  
Belief Function ◽  
Neural Network Models ◽  
Evidential Belief Function ◽  
Artificial Neural ◽  
Productivity Potential

Abstract This study analysed groundwater productivity potential (GPP) using three different models in a geographic information system (GIS) for Okcheon city, Korea. Specifically, we have used variety topography factors in this study. The models were based on relationships between groundwater productivity (for specific capacity (SPC) and transmissivity (T)) and hydrogeological factors. Topography, geology, lineament, land-use and soil data were first collected, processed and entered into the spatial database. T and SPC data were collected from 86 well locations. The resulting GPP map has been validated in under the curve analysis area using well data not used for model training. The GPP maps using artificial neural network (ANN), frequency ratio (FR) and evidential belief function (EBF) models for T had accuracies of 82.19%, 81.15% and 80.40%, respectively. Similarly, the ANN, FR and EBF models for SPC had accuracies of 81.67%, 81.36% and 79.89%, respectively. The results illustrate that ANN models can be useful for the development of groundwater resources.

scholarly journals A comparative assessment of fuzzy logic and evidential belief function models for mapping artesian zone boundary in an arid region, Iraq

2017 ◽  
Vol 20 (2) ◽  
pp. 497-519 ◽  
Author(s):  
Alaa M. Al-Abadi ◽  
Suaad A. Al-Bhadili ◽  
Maitham A. Al-Ghanimy
Keyword(s):  
Fuzzy Logic ◽  
Arid Region ◽  
Prediction Accuracy ◽  
Assessment Tool ◽  
Belief Function ◽  
Data Availability ◽  
Zone Boundary ◽  
Inference System ◽  
Conditioning Factors ◽  
Evidential Belief Function

Abstract This paper discusses and compares the potential application of the evidential belief function model and fuzzy logic inference system technique for spatial delineation of a groundwater artesian zone boundary in an arid region of central Iraq. First, a flowing well inventory of a total of 93 perennial flowing wells was constructed and randomly partitioned into two data sets: 70% (65 wells) for training and 30% (28 wells) for validation. Twelve groundwater conditioning factors were considered in the geospatial analysis depending on data availability and literature review. The random forest (RF) algorithm was first applied to investigate the most important conditioning factors in groundwater potential analysis. The most important factors with training flowing wells were used to develop predictive models. The prediction accuracy of the developed models was checked using the area under the relative operating characteristic curve. Results showed that the best model with a higher prediction accuracy of 86% was a fuzzy AND model followed by the evidential model with 84%. The main conclusion of this study is that the integrated use of the adapted models with RF offer a rapid assessment tool in groundwater exploration and can be helpful in groundwater management.

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