Data Envelopment Analysis: A knowledge-driven method for mineral prospectivity mapping

Hosseini, Seyed Ali; Abedi, Maysam

doi:10.1016/j.cageo.2015.06.006

Cited by 27 publications

(4 citation statements)

References 33 publications

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“…Therefore, machine learning methods such as regression approaches can be used for modeling the relationships between predicted and predictor variables to determine the weight of each predictor variable in this approach. While in the knowledge‐driven approach, the predicted variable is not available and cannot be obtained by observations; therefore, it is evaluated by experts' knowledge through Multi‐Criteria Decision‐Making (MCDM) methods (Hosseini & Abedi, 2015; McKay & Harris, 2016; Rajabi et al, 2014; Stevens & Pfeiffer, 2011). The MCDM, as a knowledge‐driven approach, is very useful in modeling semi‐structured problems with less‐explored areas such as spatial decision‐making problems.…”

Section: Methodology Concepts and Methodsmentioning

confidence: 99%

Quantifying walking capability: a novel aggregated index based on spatial perspective and analyses

Motieyan

Kaviari

Mostofi

2022

Papers in Regional Science

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Urbanization and modernization of services in the world triggers decrement in the physical activities of citizens. The most common solution for this decrement is improving walking capacity in neighborhoods to engage the citizens in walking activity. In this research, a novel model is presented to quantify walkability based on several necessary criteria and indicators considering the physical and non-physical conditions. For this model, promising sciences, including GIS, RS, and MCDM, are utilized to compute indicators and aggregate them in the form of a spatial index in a central region of Tehran, Iran, at the neighborhood scale. For evaluating the result, the obtained walkability level is compared to the physical structure of neighborhoods, previous works, housing price along with some other socio-economic parameters, and municipal action plan in the neighborhoods. These evaluations show that the outcome of the proposed model is a robust tool for walkability planning.

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Section: Methodology Concepts and Methodsmentioning

confidence: 99%

Quantifying walking capability: a novel aggregated index based on spatial perspective and analyses

Motieyan

Kaviari

Mostofi

2022

Papers in Regional Science

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“…Banker et al (1984) developed a model assumed to be variable returns-to-scale, known as BCC. In recent decades, DEA has rapidly expanded into new application areas (Cook and Seiford, 2009; Hatami-Marbinia et al , 2011; Shabani et al , 2015; Hosseini and Abedi, 2015; Wu et al , 2016; Chen et al , 2017; Rosenthal and Weiss, 2017; etc). The following model shows an input-oriented CCR model.…”

Section: Used Techniquesmentioning

confidence: 99%

Selecting Six Sigma project: a comparative study of DEA and LDA techniques

Hadi-Vencheh

Yousefi²

2018

IJLSS

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Purpose Nowadays, most of the organizations have focused through the world on Six Sigma to reduce the costs, improve the productivity and enhance concerned individuals’ satisfaction, especially customers’ satisfaction. Annually, these organizations define and execute thousands of Six Sigma projects which involve a great deal of investments. But are all of these projects successful and do the organizations benefit from the above advantages? The purpose of this study is to proposing a methodology to to answer this question that: How can we reduce the risk of failure in Six Sigma projects? The first step to reduce the risk of failure in Six Sigma projects is selecting optimal ones which have the most profits and the least expected risks. Design/methodology/approach First, the effective criteria are recognized and defined in selecting Six Sigma projects. Then, a new data envelopment analysis (DEA) model is proposed for project selection process. A real example is resolved by the presented model. Finally, the authors use linear discriminate analysis (LDA) to examine the validity of obtained results from the proposed model. Findings The results show that the proposed model is a suitable tool for selecting Six Sigma Projects. The findings demonstrate that the selected projects by suggested integrated DEA model are those confirmed by LDA. Originality/value The paper, using a real case study, provides a mathematical model to enhance decision quality in Six Sigma project selection. Applying the specific DEA model is remarkable itself, which joined to a pioneering procedure to use LDA to validity evaluation of the results.

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“…The purpose of these models (e.g., C-means) is to identify the underlying distribution of predictor variables and further use them to guide MPM (Abedi et al 2013b;Jahangiri et al 2018;Wang et al 2020). The major techniques of the knowledge-driven approach include the Boolean logic (Zaidi et al 2015), index overlay (Aryafar and Roshanravan 2020) , Dempster-Shafer belief theory (Carranza et al 2008) , fuzzy logic (Abedi et al 2013a), wildcat mapping (Carranza and Hale 2002), outranking methods (Abedi et al 2013a), and data envelopment analysis (Hosseini et al 2015), self-organizing map (SOM), and K-means clustering (Zuo 2017).…”

Section: Introductionmentioning

confidence: 99%

Machine learning predictive approaches for Cu-Au mineral prospectivity map in theSonajil, NW of Iran: An improvement by Bayesian semi-supervised algorithm

Mohammadjafar

Majid

Jahangiri

et al. 2023

Preprint

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Most machine learning monitored algorithms used in the preparation of mineral potential prediction maps, require noise-free data to achieve high-performance and reliable results. To discover the hidden structures of a data set, unsupervised clustering methods are highly efficient. Therefore, in this paper, a combination of supervised and unsupervised methods was attempted using training and testing data to prepare a potential map with high accuracy in the Sonajil copper-gold deposit, located in NW of Iran. Here a semi-supervised Bayesian algorithm utilized to map the mineral landscape. At First, raster layers of 10 exploratory features were prepared. Then, 27 exploratory boreholes drilled were divided into 4 classes C1 to C4 based on the copper concentration, and from each class, two boreholes were selected and 100-meter buffering was performed around these boreholes to extract 1113 training data. Then, the existing data were clustered by the FCM method and the whole data and clustering data were entered into the Bayesian algorithm to evaluate the accuracy of Bayesian classifier method in 5 different clusters. The results show an increase in the average accuracy while using the clustered data instead of whole data for MPM mapping. Also, in case of not using cluster 5 data, it has the highest accuracy of 96%. To validate the Bayesian semi-supervised method, boreholes that were not used in data training were employed. Overall results based on the classification of boreholes and prepared potential map indicate accuracy of the Bayesian semi-supervised method as an optimal result for further exploration and delimiting more mineralization targets.

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Data Envelopment Analysis: A knowledge-driven method for mineral prospectivity mapping

Cited by 27 publications

References 33 publications

Quantifying walking capability: a novel aggregated index based on spatial perspective and analyses

Quantifying walking capability: a novel aggregated index based on spatial perspective and analyses

Selecting Six Sigma project: a comparative study of DEA and LDA techniques

Machine learning predictive approaches for Cu-Au mineral prospectivity map in theSonajil, NW of Iran: An improvement by Bayesian semi-supervised algorithm

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