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A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels

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Abstract

Declining groundwater levels due to the absence of a planning system makes aquifers vulnerable to subsidence. This paper investigates possible hotspots in terms of Subsidence Vulnerability Indices (SVI) by applying the ALPRIFT framework, introduced recently by the authors by mirroring the procedure for the DRASTIC framework. ALPRIFT is suitable to cases, where data is sparse, and is the acronym of seven data layers to be presented in due course. It is a scoring technique, in which each data layer bears an aspect of land subsidence and is prescribed with rates to account for local variability, and with prescribed weights to account for relative significance of the data layer. The inherent subjectivity in prescribed weights is treated in this paper by learning their values from site-specific data by the strategy of using artificial intelligence to learn from multiple models (AIMM). The strategy has two levels: (i) at Level 1, three fuzzy models are used to learn weight values from the local data and from observed target data, and (ii) at Level 2, genetic expression algorithm (GEP) is used to learn further, in which the outputs of the models at Level 1 are reused as its inputs and observed data as its target values. The results show that (i) the Nash-Sutcliff Efficiency (NSE) coefficient for ALPRIFT with measured land subsidence values is approx. 0.21; (ii) NSE is improved to 0.88 by learning the weights at Level 1 using fuzzy logic, and (iii) NSE is further improved to 0.94 by further learning at Level 2 using GEP.

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Acknowledgements

The authors acknowledge gratefully the provision of the data by the Ardabil Regional Water Authority (ARWA) and their cooperation.

Funding

This research is one of the outputs of the Artificial Intelligence Multiple Models research group team which is financially supported by the University of Tabriz through a Grant scheme.

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Authors and Affiliations

  1. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran

    Ata Allah Nadiri

  2. Institute of Environment, University of Tabriz, Tabriz, East Azerbaijan, Iran

    Ata Allah Nadiri & Bakhtiar Feizizadeh

  3. GTEV-ReX Limited, Swindon, UK

    Rahman Khatibi

  4. Department of Earth Sciences, Faculty of Earth Sciences, University of Urmia, Urmia, Western Azerbaijan, Iran

    Pari Khalifi

  5. Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran

    Bakhtiar Feizizadeh

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  1. Ata Allah Nadiri
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Nadiri, A.A., Khatibi, R., Khalifi, P. et al. A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels. Bull Eng Geol Environ 79, 3989–4003 (2020). https://doi.org/10.1007/s10064-020-01781-3

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