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Spatial variability of rock depth using adaptive neuro-fuzzy inference system (ANFIS) and multivariate adaptive regression spline (MARS)

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Abstract

This article adopts adaptive neuro-fuzzy inference system (ANFIS) and multi adaptive regression spline (MARS) for prediction of spatial variability of reduced level of rock depth (d) in Bangalore. The database consists of 652 d values spread over a 220 km2 area of Bangalore. In two-dimensional analysis, the function \(d = f (x_{\text{1}} ,x_{\text{2}} )\), where x 1, and x 2 are the latitude and longitude of a point corresponding to d value, is to be approximated with which d value at any half space point in Bangalore can be determined. The input variables of ANFIS and MARS are x 1 and x 2. Map of d has been also produced by developed ANFIS and MARS models. The comparison between the MARS and ANFIS models demonstrates that the MARS model is superior to the ANFIS model.

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Acknowledgments

Authors thank T.G. Sitharam for providing the rock depth data.

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Authors and Affiliations

  1. Centre for Disaster Mitigation and Management, VIT University, Vellore, 632014, India

    Pijush Samui

  2. Department of Civil Engineering, Kunsan National University, Kunsan, Jeonbuk, South Korea

    Dookie Kim

  3. School of Information Technology and Engineering, VIT University, Vellore, 632014, India

    R. Viswanathan

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  1. Pijush Samui
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  2. Dookie Kim
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  3. R. Viswanathan
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Correspondence to Pijush Samui.

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Samui, P., Kim, D. & Viswanathan, R. Spatial variability of rock depth using adaptive neuro-fuzzy inference system (ANFIS) and multivariate adaptive regression spline (MARS). Environ Earth Sci 73, 4265–4272 (2015). https://doi.org/10.1007/s12665-014-3711-x

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  • DOI: https://doi.org/10.1007/s12665-014-3711-x

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