Capability of self-organizing map neural network in geophysical log data classification: Case study from the CCSD-MH

Konaté, Ahmed Amara; Pan, Heping; Fang, Sinan; Asim, Shazia; Ziggah, Yao Yevenyo; Deng, Cong; Khan, N. A.

doi:10.1016/j.jappgeo.2015.04.004

Cited by 29 publications

(2 citation statements)

References 27 publications

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“… (Konaté et al, 2015). Subsequently, for each K trials of cross validation the union of K-1 folds is used as training data for model development while the remaining part is used as the testing data for the resulting model validation (Stone, 1974).…”

Section: Statistical Resampling Techniquementioning

confidence: 99%

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

Ziggah

Tierra

et al. 2019

Earth sci. res. j.

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The popularity of Artificial Neural Network (ANN) methodology has been growing in a wide variety of areas in geodesy and geospatial sciences. Its ability to perform coordinate transformation between different datums has been well documented in literature. In the application of the ANN methods for the coordinate transformation, only the train-test (hold-out cross-validation) approach has usually been used to evaluate their performance. Here, the data set is divided into two disjoint subsets thus, training (model building) and testing (model validation) respectively. However, one major drawback in the hold-out cross-validation procedure is inappropriate data partitioning. Improper split of the data could lead to a high variance and bias in the results generated. Besides, in a sparse dataset situation, the hold-out cross-validation is not suitable. For these reasons, the K-fold cross-validation approach has been recommended. Consequently, this study, for the first time, explored the potential of using K-fold cross-validation method in the performance assessment of radial basis function neural network and Bursa-Wolf model under data-insufficient situation in Ghana geodetic reference network. The statistical analysis of the results revealed that incorrect data partition could lead to a false reportage on the predictive performance of the transformation model. The findings revealed that the RBFNN and Bursa-Wolf model produced a transformation accuracy of 0.229 m and 0.469 m, respectively. It was also realised that a maximum horizontal error of 0.881 m and 2.131 m was given by the RBFNN and Bursa-Wolf. The obtained results per the cadastral surveying and plan production requirement set by the Ghana Survey and Mapping Division are applicable. This study will contribute to the usage of K-fold cross-validation approach in developing countries having the same sparse dataset situation like Ghana as well as in the geodetic sciences where ANN users seldom apply the statistical resampling technique.

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Section: Statistical Resampling Techniquementioning

confidence: 99%

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

Ziggah

Tierra

et al. 2019

Earth sci. res. j.

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“…The capability of a neural network in predicting geological features based on physical data motivated us to apply this tool to convert physical properties obtained from 3D inversion to produce a 3D lithological model. A neural network is a supervised multi-variable classification technique that has been successfully applied to physical log data to predict lithofacies (Baldwin, Bateman and Wheatley 1990;Wong, Jian and Taggart 1995;Farmer and Adams 1998;Qi and Carr 2006;Maiti, Tiwari and Kumpel 2007;Tiwari 2009, 2010) and lithological units (Benaouda et al 1999;Ojha and Maiti 2013;Konaté et al 2015;Mahmoodi, Smith and Tinkham 2016).…”

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confidence: 99%

Using constrained inversion of gravity and magnetic field to produce a 3D litho‐prediction model

Mahmoodi

Smith

Spicer³

2017

Geophysical Prospecting

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Geologically constrained inversion of gravity and magnetic field data of the Victoria property (located in Sudbury, Canada) was undertaken in order to update the present three‐dimensional geological model. The initial and reference model was constructed based on geological information from over 950 drillholes to constrain the inversion. In addition, downhole density and magnetic susceptibility measured in six holes were statistically analysed to derive lower and upper bounds on the physical properties attributed to the lithological units in the reference model. Constrained inversion of the ground gravity and the airborne magnetic data collected at the Victoria property were performed using GRAV3D and MAG3D, respectively. A neural network was trained to predict lithological units from the physical properties measured in six holes. Then, the trained network was applied on the three‐dimensional distribution of physical properties derived from the inversion models to produce a three‐dimensional litho‐prediction model. Some of the features evident in the lithological model are remnants of the constraints, where the data did not demand a significant change in the model from the initial constraining model (e.g., the thin pair of diabase dykes). However, some important changes away from the initial model are evident; for example, a larger body was predicted for quartz diorite, which may be related to the prospective offset dykes; a new zone was predicted as sulfide, which may represent potential mineralisation; and a geophysical subcategory of metabasalt was identified with high magnetic susceptibility and high density. The litho‐prediction model agrees with the geological expectation for the three‐dimensional structure at Victoria and is consistent with the geophysical data, which results in a more holistic understanding of the subsurface lithology.

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Proposing a composite index and maturity model for urban sustainability in the Brazilian context: A machine learning and data envelopment analysis approach

Furlan,

Lima,

Paião Junior

et al. 2024

Sustainable Development

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Despite efforts towards the accomplishment of the UN 2030 Agenda, the challenges of burgeoning populations, income inequality, difficulty in accessing basic services, among others, remain in several cities around the world. New approaches to measure and assess the sustainability of cities can support the development of actions to improve the different dimensions of sustainability. The research aims to propose an urban sustainability index and a maturity model to evaluate the sustainability of cities and monitor it over time. To achieve this objective, a maturity model was developed based on three different techniques: Data Envelopment Analysis, Artificial Neural Networks, and Analysis of Variance. The proposed index and the maturity model were applied to evaluate a sample of 504 Brazilian cities. The main results observed are: (a) the presence of five distinct levels of city performance (maturity), grouped via machine learning and validated via inferential statistics; (b) no city was considered fully sustainable and only 4.76% of the cities studies are at the highest level of urban sustainability maturity; (c) from a joint application of the three quantitative techniques and specific targets for each indicator could be identified, and the performance of cities classified over time. Based on the results, it is hoped that policy makers will have more objective and standardized tools to collect useful information and be able to reinforce critical strategies or chart new policies towards sustainable urban development. It is also hoped that the joint application of the techniques can shed light on new urban sustainability assessment models.

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Capability of self-organizing map neural network in geophysical log data classification: Case study from the CCSD-MH

Cited by 29 publications

References 27 publications

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

Coordinate Transformation between Global and Local Datums Based on Artificial Neural Network with K-Fold Cross-Validation: A Case Study, Ghana

Using constrained inversion of gravity and magnetic field to produce a 3D litho‐prediction model

Proposing a composite index and maturity model for urban sustainability in the Brazilian context: A machine learning and data envelopment analysis approach

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