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Introducing dynamic land subsidence index based on the ALPRIFT framework using artificial intelligence techniques

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Abstract

Land subsidence is mainly caused by excessive groundwater abstraction from aquifers. This study introduces Dynamic Subsidence Vulnerability Index (DSVI) by estimating possible land subsidence time variations by considering changes in groundwater level based on the ALPRIFT framework in Iran’s Hadishahr Plain, which is summarized in three modules. (i) Module I: mapping Subsidence Vulnerability Index (SVI) utilizing the ALPRIFT framework and optimization its weights by the Multiple Artificial Intelligence Models (MAIM) strategy; (ii) Module II: predicting groundwater level by Group Method of Data Handling (GMDH); and Module III: mapping DSVI by combining the results from Modules I and II. A two-pronged strategy is employed in MAIM: In Level 1, multiple models are derived from Sugeno Fuzzy Logic (SFL) and Support Vector Machin (SVM); and in Level 2, the outcomes of Level 1 models are combined by Artificial Neural Networks (ANN). According to the results: (i) ALPRIFT exhibits a correlation coefficient (r) of about 0.55 with corresponding measurements of land subsidence; (ii) using SVM and SFL to optimize the weights, r is raised to 0.83 and 0.74, respectively; (iii) the use of multiple models at Level 2 results in better performance than that of a single model at Level 1; and (iv) on the DSVI map, the central part of the plain is vulnerable at hotspot areas where groundwater is being improperly withdrawn from the Hadishahr Plain aquifer, increasing the risk of subsidence.

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Abbreviations

SVI:

Subsidence Vulnerability Indices

TSVI:

Dynamic SVI

GWL:

Groundwater Level

FL:

Fuzzy Logic

SFL:

Sugeno Fuzzy Logic

InSAR:

Interferometric Synthetic Aperture Radar

GPR:

Ground-Penetrating Radar

OW:

Observation well

CSVI:

Conditioned SVI

MF :

Membership Function

BAF :

Basic ALPRIF framework

RMSE:

Root Mean Squared Error

r:

Correlation coefficient

SVM:

Support Vector Machine

MMs:

Multiple Models

GMDH:

Group Method of Data Handling

ANN:

Artificial Neural Networks

AUC:

Area Under Curve

MAIM:

Multiple Artificial Intelligence Models

GPS:

Global Positioning System

SC:

Subtractive Clutering

MLP :

Multi layer perceptron 

SLC :

single look complex

NSGA-II:

Non-dominated Sorting Genetic Algorithm-II

R2 :

Coefficient of determination

ROC:

Receiver Operating Characteristics

References

  • Anumba CJ, Scot DT (2001)Performance evaluation of a knowledge-basedsystem for subsidence management. Struct Surv 19:222–232

    Article  Google Scholar 

  • Avila-Olivera JA, Garduño-Monroy VH (2008)A GPR study of subsidence-creep -faultprocesses in Morelia, Michoacán, Mexico. Eng Geol 100(1–2):69–81

    Article  Google Scholar 

  • Beck BF (1986)A generalized genetic framework for the development of sinkholes and Karst in Florida, U.S.A. Environ Geol Water Sci 8:5. https://doi.org/10.1007/BF02525554

  • Candela T, Koster K, Stafleu J, Visser W, Fokker P (2020) Towards regionally forecasting shallow subsidence in the Netherlands. Proc. IAHS 382:427–431. https://doi.org/10.5194/piahs-382-427-2020

  • Chen B, Gong H, Chen Y, Li X, Zhou C, Zhu L, Duan L, Zhao X (2020) Land subsidence and its relation with groundwater aquifers in Beijing Plain of China. Science of The Total Environment 735:139111

  • Chiu S (1994)Fuzzy model identification based on cluster estimation. Intell Fuzzy Syst 2:267–278

    Article  Google Scholar 

  • Ciampalini A, Solari L, Giannecchini R, Galanti Y, Moretti S (2019)Evaluation of subsidence induced by long-lastingbuildings load using InSAR technique and geotechnical data: The case study of a Freight Terminal (Tuscany, Italy). Appl Earth Obs Geoinf 82:101925

    Google Scholar 

  • Colazas XC, Strehle RW (1995)Subsidence in theWilmington Oil Field, Long Beach, California, USA. Dev Pet Sci 41:285–335

  • Deb K, Pratap A, Agarwal S, Meyarivan T (2002)A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Article  Google Scholar 

  • Unknown (2018) Report and available data. published by East Azerbaijan Regional Water Authority (EARWA), Iran.

  • Ebtehaj I, Bonakdari H, Zaji H, Azimi AH, Khoshbin F (2015)GMDH-typeneural network approach for modeling the discharge coefficient of rectangular sharp-crestedside weirs. Eng Sci Technol 18:746–757

  • Farlow SJ (1984)Self-organizingmethods in modeling: GMDH type algorithms. Marcel Dekker Inc, CrC Press

  • Galloway DL, Burbey TJ (2011)Regional land subsidence accompanying groundwater extraction. Hydrogeol 19(8):1459–1486

    Article  Google Scholar 

  • Galloway D, Jones D, Ingebritsen SE (1999)Land subsidence in the United State. U.S. Geological Survey, Circular 1182

  • Gambolati G (1975)Numerical models in land subsidence control. Comput Methods Appl Mech Eng 5(2):227–237

    Article  Google Scholar 

  • Gharekhani M, Nadiri AA, Khatibi R, Sadeghfam S (2021)An investigation into time-variantsubsidence potentials using inclusive multiple modelling strategies. J Environ Manage 294:112949

    Article  Google Scholar 

  • Gu K, Shi B, Liu C, Jiang H, Li T, Wu J (2018)Investigation of land subsidence with the combination of distributed fiber optic sensing techniques and microstructure analysis of soils. Eng Geol 240(5):34–47

    Article  Google Scholar 

  • Hanssen RF (2001)Radar Interferometry: Data Interpretation and Error Analysis. Springer Science & Business Media, Berlin

  • Hayashi T, Tokunag T, Aichi M, Shimada J, Taniguchi M (2009)Effects of human activities and urbanization on groundwater environments: an example from the aquifer system of Tokyo and the surrounding area. Sci Total Environ 407(9):3165–3172

    Article  Google Scholar 

  • Hiashi I, Tanaka H (1990)The fuzzy GMDH algorithm by possibility models and its application. Fuzzy Sets Syst 36(2):245–258

    Article  Google Scholar 

  • Ivakhnenko AG (1968)The Group Method of Data Handling –a rival of the Method of Stochastic Approximation. Soviet Automatic Control c/c of Avtomatika 1(3):43–55

    Google Scholar 

  • Ivakhnenko AG (1978)The Group Method of Data Handling in Long-RangeForecasting. Technol Forecast Soc Change 12:213–227

    Article  Google Scholar 

  • Jafari F, Javadi S, Golmohammadi G, Karimi N, Mohammadi K (2016)Numerical simulation of groundwater flow and aquifer-systemcompaction using simulation and InSAR technique: Saveh basin, Iran. Environ Earth Sci 75(9):833

    Article  Google Scholar 

  • Khatibi R, Nadiri AA (2021)Inclusive Multiple Models (IMM)for predicting groundwater levels and treating heterogeneity. Geosci Front 12(2):713–724

    Article  Google Scholar 

  • Khatibi R, Ghorbani MA, Naghshara S, Aydin H, Karimi V (2020)Framework for ‘Inclusive Multiple Modelling’ with Critical Views on Modelling Practices -Applications to Modelling Water Levels of Caspian Sea and Lakes Urmia and Van. Hydro 587:124923

    Article  Google Scholar 

  • Kihm JH, Kim JM, Song SH, Lee GS (2007)Three-dimensionalnumerical simulation of fully coupled groundwater flow and land deformation due to groundwater pumping in an unsaturated fluvial aquifer system. J Hydrol 335(1–2):1–14

  • Kim D, Park GT (2005)GMDH-typeneural network modeling in evolutionary optimization, vol 3533. IEA/AIE, pp 563–570

  • Legates DR, McCabe CJ (1999)Evaluation the use of goodness-of-fitmeasures in hydrologic and hydro climate model validation. Water Resour Res 35(1):233–241

    Article  Google Scholar 

  • Li H, Philip CL, Huang HP (2001)Fuzzy neural intelligent systems: mathematical foundation and the applications in engineering. CRC Press, Boca Rato, p 392

  • Lorphensri O, Ladawadee A, Dhammasarn S (2011)Review of groundwater management and land subsidence in Bangkok, Thailand. In: Taniguchi M (eds)Groundwater and Subsurface Environments. Springer, Tokyo

  • Luo ZJ, Zeng F (2011)Finite element numerical simulation of land subsidence and groundwater exploitation based on visco-elastic-plasticbiot’s consolidation theory. J Hydrodynam 23(5):615–624

    Article  Google Scholar 

  • Malmir M, Javadi S, Moridi A, Neshat A, Razdar B (2021)A new combined framework for sustainable development using the DPSIR approach and numerical modeling. Geosci Front 12(4):101169

    Article  Google Scholar 

  • Moiwo JP, Tao F (2015)Satellite signal shows storage-unloadingsubsidence in North China. Hydrol Earth Syst Sci 12:6043–6075

    Google Scholar 

  • Nadiri AA, Chitsazan N, Tsai FTC, Moghaddam AA (2014)Bayesian artificial intelligence model averaging for hydraulic conductivity estimation. J Hydrol Eng 19:520–532

    Article  Google Scholar 

  • Nadiri AA, Fijani E, Tsai FTC, Moghaddam AA (2013)Supervised committee machine with artificial intelligence for prediction of fluoride concentration. Hydroinformatics 15(4):1474–1490

    Article  Google Scholar 

  • Nadiri AA, Gharekhani M, Khatibi R, Moghaddam A (2017)Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models. Environ Sci Pollut Res 24(9):8562–8577

    Article  Google Scholar 

  • Nadiri AA, Gharekhani M, Khatibi R, Sadeghfam S, Asghari Moghaddam AA (2017)Groundwater vulnerability indices conditioned by Supervised Intelligence Committee Machine (SICM). Sci Total Environ 574:691–706

    Article  Google Scholar 

  • Nadiri AA, Khatibi R, Khalifi P, Feizizadeh B (2020) A study of subsidence hotspots by mapping vulnerability indices through innovatory ‘ALPRIFT’ using artificial intelligence at two levels. Bulletin of Engineering Geology and the Environment 79(8):3989–4003

    Article  Google Scholar 

  • Nadiri AA, Naderi K, Khatibi R, Gharekhani M (2019)Modelling groundwater level variations by learning from multiple models using fuzzy logic. Hydrol Sci J 64:210–226

    Article  Google Scholar 

  • Nadiri AA, Sedghi Z, Khatibi R, Gharekhani M (2017)Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures. Sci Total Environ 593:75–90

    Article  Google Scholar 

  • Nadiri AA, Taheri Z, Khatibi R, Barzegari G, Dideban K (2018)Introducing a new framework for mapping subsidence vulnerability indices (SVIs). Sci Total Environ 628:1043–1057

    Article  Google Scholar 

  • Nappoa N, Pedutob D, Polcaric M, Maria F, Ferrarioa F, Comercid V, Stramondoc S, Michettia AM (2021)Subsidence in Como historic centre (northern Italy): Assessment of building vulnerability combining hydrogeological and stratigraphic features, Cosmo-SkyMedInSAR and damage data. Int J Disast Risk Reduct 56:102115

    Article  Google Scholar 

  • Psimoulis P, Ghilardi M, Fouache E, Stiros S (2007)Subsidence and evolution of the Thessaloniki plain, Greece, based on historical leveling and GPS data. Eng Geol 90(1 2):55–70

    Article  Google Scholar 

  • Rahmani B, Javadi S, Shahdany MH (2019) Evaluation of aquifer vulnerability using PCA technique and various clustering methods. Geocarto International 36(1):1–24

  • Saatsaz M, Sulaiman WNA, Eslamian S, Javadi S (2013)Development of a coupled flow and solute transport modelling for Astaneh-Kouchesfahangroundwater resources, North of Iran. Int J Water 7(1–2):80–103

    Article  Google Scholar 

  • Sadeghfam S, Khatibi R, Dadashi S, Nadiri AA (2020)Transforming subsidence vulnerability indexing based on ALPRIFT into risk indexing using a new fuzzy-catastrophescheme. Environ Impact Assess Rev 82:106352

    Article  Google Scholar 

  • Sadeghfam S, Nourbakhsh Khiyabani F, Khatibi R, Daneshfaraz R (2020)A study of land subsidence problems by ALPRIFT for vulnerability indexing and risk indexing and treating subjectivity by strategy at two levels. Hydroinformatics 22(6):1640–1662

    Article  Google Scholar 

  • Sato PH, Abe K, Otaki O (2007)GPS-measuredland subsidence in Ojiya City, Niigata Prefecture, Japan. Eng Geol 67(3–4):379–390

  • Sfidari E, Kadkhodaie A, Ahmadi B, Ahmadi B, Faraji MA (2018)Prediction of pore facies using GMDH-typeneural networks: a case study from the South Pars gas field, Persian Gulf basin. Geopersia 8(1):43–60

    Google Scholar 

  • Sossong AT (1973)Subsidence experience of Bethlehem Mines Corporation in Central Pennsylvania. In: Hargraves AJ (ed)Subsidence in mines-Proceedingsof symposium, 4th, Wollongong University, February 20-22, 1973. Australasian Institute of Mining and Metallurgy, pp 5.1–5.5

  • Swets JA (1988)Measuring the accuracy of diagnostic systems. Science 240(48–57):1285–1293

    Article  Google Scholar 

  • Tosi L, Teatini P, Strozzi T (2013)Natural versus anthropogenic subsidence of Venice. Sci Rep 3(1):1–9

    Article  Google Scholar 

  • Wang J, Deng Y, Ma R, Liu X, Guo Q, Liu S, Shao Y, Wu L, Zhou J, Yano T, Wana H, Huana X (2018)Model test on partial expansion in stratified subsidence during foundation pit dewatering. J Hydrol 557:489–508

    Article  Google Scholar 

  • Wu J, Shi X, Ye S, Xue Y (2010)Numerical Simulation of Viscoelastoplastic Land Subsidence due to Groundwater Overdrafting in Shanghai, China. J Hydrol Eng 15:3

    Article  Google Scholar 

  • Ye S, Luo Y, Wu J, Teatini P, Wang H, Jiao X (2015)Three dimensional numerical modeling of land subsidence in Shanghai. Proc IA372:443–448

  • Ye S, Luo Y, Wu J, Yan X, Wang H, Jiao X, Teatini P (2016)Three-dimensionalnumerical modeling of land subsidence in Shanghai, China. Hydrogeol J 24:695–709

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Authors and Affiliations

  1. Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, Tabriz, Iran

    Ata Allah Nadiri, Iraj Habibi & Maryam Gharekhani

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

    Ata Allah Nadiri & Sadra Karimzadeh

  3. Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences, Ardabil, Iran

    Ata Allah Nadiri

  4. Medical Geology and Environmental Research Center, University of Tabriz, Tabriz, Iran

    Ata Allah Nadiri

  5. Department of Civil Engineering, Faculty of Engineering, University of Maragheh, Maragheh, Iran

    Sina Sadeghfam

  6. Department of Bioresource Engineering, McGill University, 21111 Lakeshore, Ste Anne de Bellevue, Quebec, H9X 3V9, Canada

    Rahim Barzegar & Sadra Karimzadeh

  7. Department of Geography & Environmental Studies, Wilfrid Laurier University, Waterloo, ON, Canada

    Rahim Barzegar

  8. Department of Remote Sensing and GIS, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran

    Sadra Karimzadeh

Authors
  1. Ata Allah Nadiri
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Correspondence to Ata Allah Nadiri.

Additional information

Communicated by: H. Babaie.

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Highlights

Dynamic Subsidence Vulnerability Indices (DSVI) is summarized in three Modules.

Module I: setting up an SVI and optimizing it by Multiple Artificial Intelligence Models (MAIM) strategy.

Module II: predicting groundwater level by Group Method of Data Handling (GMDH) model.

Module III: mapping DSVI by integrating the results from Modules I and II in Iran’s Hadishahr Plain aquifer.

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Nadiri, A.A., Habibi, I., Gharekhani, M. et al. Introducing dynamic land subsidence index based on the ALPRIFT framework using artificial intelligence techniques. Earth Sci Inform 15, 1007–1021 (2022). https://doi.org/10.1007/s12145-021-00760-w

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  • DOI: https://doi.org/10.1007/s12145-021-00760-w

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