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Water Resources Management

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20-09-2021

Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence

Authors: V. Gholami, M. R. Khaleghi, S. Pirasteh, Martijn J. Booij

Published in: Water Resources Management | Issue 2/2022

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Abstract

Water quality experiments are difficult, costly, and time-consuming. Therefore, different modeling methods can be used as an alternative for these experiments. To achieve the research objective, geospatial artificial intelligence approaches such as the self-organizing map (SOM), artificial neural network (ANN), and co-active neuro-fuzzy inference system (CANFIS) were used to simulate groundwater quality in the Mazandaran plain in the north of Iran. Geographical information system (GIS) techniques were used as a pre-processer and post-processer. Data from 85 drinking water wells was used as secondary data and were separated into two splits of (a) 70 percent for training (60% for training and 10% for cross-validation), and (b) 30 percent for the test stage. The groundwater quality index (GWQI) and the effective water quality factors (distance from industries, groundwater depth, and transmissivity of aquifer formations) were implemented as output and input variables, respectively. Statistical indices (i.e., R squared (R-sqr) and the mean squared error (MSE)) were utilized to compare the performance of three methods. The results demonstrate the high performance of the three methods in groundwater quality simulation. However, in the test stage, CANFIS (R-sqr = 0.89) had a higher performance than the SOM (R-sqr = 0.8) and ANN (R-sqr = 0.73) methods. The tested CANFIS model was used to estimate GWQI values on the area of the plain. Finally, the groundwater quality was mapped in a GIS environment associated with CANFIS simulation. The results can be used to manage groundwater quality as well as support and contribute to the sustainable development goal (SDG)-6, SDG-11, and SDG-13.

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Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1–4):28–40. https://doi.org/10.1016/j.jhydrol.2011.06.013CrossRef

Allawi MF, Jaafar O, Mohamad Hamzah F, Mohd NS, Deo RC, El-Shafie A (2018) Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region. Theor Appl Climatol 134(1–2):545–563CrossRef

Aneetha AS, Bose S (2012) The combined approach for anomaly detection using neural networks and clustering techniques. Comput Sci Eng Int J 2(4):37–46

Bahrami S, Ardejani FD, Baafi E (2016) Application of artificial neural network coupled with genetic algorithm and simulated annealing to solve groundwater inflow problem to an advancing open-pit mine. J Hydrol 536:471–484CrossRef

Besalatpour AA, Ayoubi S, Hajabbasi MA, Gharipour A, Yousefian Jazi A (2014) Feature selection using the parallel genetic algorithm for the prediction of the geometric mean diameter of soil aggregates by machine learning methods. Arid Land Res Manage 28(4):383–394CrossRef

Brown RM, McClelland NI, Deininger RA, Tozer RG (1970) A water quality index: do we dare? Water Sewage Works 117:339–343

Castelletti A, Galelli S, Restelli M, Soncini-Sessa R (2012) Data-driven dynamic emulation modelling for the optimal management of environmental systems. Environ Model Softw 34:30–43CrossRef

Chang FJ, Chang LC, Huang HL (2002) Real-time recurrent learning neural network for stream-flow forecasting. Hydrol Processes 16(13):2577–2588CrossRef

Choubin B, Borji M, Sajedi Hosseini F, Mosavi AH, Dineva AA (2020a) Mass wasting susceptibility assessment of snow avalanches using machine learning models. Sci Rep 10(1):18363. https://doi.org/10.1038/s41598-020-75476-wCrossRef

Choubin B, Sajedi Hosseini F, Fried Z, Mosavi AH (2020b) Application of bayesian regularized neural networks for groundwater level modeling, 2020, CANDO-EPE 2020—Proceedings, IEEE 3rd International Conference and Workshop in Obuda on Electrical and Power Engineering, 9337753:209–212

Cohen S, Intrator N (2002) Automatic model selection in a hybrid perceptron radial network. Inf Fusion 3(4):259–266CrossRef

Crawford NH, Linsley RK (1966) Digital simulation in hydrology: Stanford watershed model IV, technical report 10-department of civil engineering. Stanford University, Stanford

Ehsani AH, Quiel F (2008) Geomorphometric feature analysis using morphometric parametrization and artificial neural networks. Geomorphology 99(1–4):1–12CrossRef

Fang Z, Wang Y, Peng L, Hong H (2020) A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. Int J Geog Inf Sci 35(2):321–347CrossRef

Farjad B, Helmi ZMS, Thamer AM, Pirasteh S (2012) Groundwater Intrinsic vulnerability and risk mapping. Water Manage 165(8):441–450

Gholami V, Darvari Z, Mohseni Saravi M (2015a) Artificial neural network technique for rainfall temporal distribu-tion simulation (case study: Kechik region). Caspian J Environ Sci (CJES) 13(1):53–60

Gholami V, Aghagoli H, Kalteh AM (2015b) Modeling sanitary boundaries of drinking water wells on the Caspian Sea southern coasts. Iran Environ Earth Sci 74(4):2981–2990CrossRef

Gholami V, Khaleghi MR, Taghvaei E (2020) Groundwater quality modeling using self-organizing map (SOM) and geographic information system (GIS) on the Caspian southern coasts. J Mountain Sci 17:1724–1734. https://doi.org/10.1007/s11629-019-5483-yCrossRef

Gholami V, Sahour H, Amri MAH (2021) Soil erosion modeling using erosion pins and artificial neural networks. Catena 196:104902. https://doi.org/10.1016/j.catena.2020.104902CrossRef

Ghose DK, Panda SS, Swain PC (2010) Prediction of water table depth in western region, Orissa using BPNN and RBF neural networks. J Hydrol 394(3–4):296–304CrossRef

Giljanovic NS (1999) Water quality evaluation by index in Dalmata. Water Res 33(16):3423–3440CrossRef

Haddad OB, Tabari MMR, Fallah-Mehdipour E, Mariño MA (2013) Groundwater model calibration by meta-heuristic algorithms. Water Resour Manag 27:2515–2529CrossRef

Haider H, Singh P, Ali W, Tesfamariam S, Sadiq R (2015) Sustainability evaluation of surface water quality management options in developing countries: multicriteria analysis using fuzzy UTASTAR method. Water Resour Manag 29:2987–3013. https://doi.org/10.1007/s11269-015-0982-2CrossRef

Han HG, Chen QL, Qiao JF (2011) An efficient self-organizing RBF neural network for water quality prediction. Neural Netw 24(7):717–725CrossRef

Heydari M, Talaee PH (2011) Prediction of flow through rockfill dams using a neuro-fuzzy computing technique. J Math Comput Sci 2(3):515–528CrossRef

Horton RK (1965) An index number system for rating water quality. J Water Pollut Control Fed 37(3):300–306

House MA, Ellis JB (1987) The development of water quality indices for operational management. Water Sci Technol 19(9):145–154CrossRef

Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of the rainfall-runoff process. Water Resour Res 31(10):2517–2530CrossRef

Huang PC, Hsu KL, Lee KT (2021) Improvement of two-dimensional flow-depth prediction based on neural network models by preprocessing hydrological and geomorphological data. Water Resour Manage 35:1079–1100CrossRef

Isik S, Kalin L, Schoonover J, Srivastava P, Lockaby BG (2013) Modeling effects of changing land use/cover on daily streamflow: an artificial neural network and curve number based hybrid approach. J Hydrol 485(2):103–112CrossRef

Iwashita F, Friedel MJ, Roberto C, Filho S (2011) Using self-organizing maps to analyze high-dimensional geochemistry data across Paraná, Brazil. Conference: 15th Simpósio Brasileiro de Sensoriamento Remoto, April 2011

Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice-Hall, Hoboken

Jassar KK, Dhindsa KS (2015) Comparative study and performance analysis of clustering algorithms. Int J Comput Appl 975:8887

Klobucar D, Subasic M (2012) Using self-organizing maps in the visualization and analysis of forest inventory. iForest Biogeosc For 5(5):216–223CrossRef

Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69CrossRef

Kohonen T (1984) Self-organization and associative memory. Springer, Berlin

Kohonen T (2005) “Intro to SOM”. SOM Toolbox. Retrieved July 18 2006

Kohonen T, Hynninen J, Kangas J, Laaksonen J (1996) SOMPAK: The self-organizing map program package. Report A31. Helsinki University of Technology, Laboratory of Computer and Information Science, Espoo, Finland. http://www.cis.hut.fi/research/som_lvq_paks.html

Krishna B, Satyajit Rao YR, Vijaya T (2008) Modeling groundwater levels in an urban coastal aquifer using artificial neural networks. Hydrol Processes 22(8):1180–1188CrossRef

Laaksonen J, Honkela T (eds) (2011) Advances in self-organizing maps, WSOM 2011. Springer, Berlin

Li W, Hsu CY (2020) Automated terrain feature identification from remote sensing imagery: a deep learning approach. Int J Geogr Inf Sci 34(4):637–660CrossRef

Li J, Shi Z, Liu F (2020) Evaluating spatiotemporal variations of groundwater quality in northeast Beijing by self-organizing map. Water 12(5):1382. https://doi.org/10.3390/w12051382CrossRef

Lim TS, Loh WY, Shih YS (2000) A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithm. Mach Learn 40(3):203–238CrossRef

Lin GF, Wang TC, Chen LH (2016) A forecasting approach combining self-organizing map with support vector regression for reservoir inflow during Typhoon periods. Adv Meteorol. https://doi.org/10.1155/2016/7575126CrossRef

Loganathan C, Girija KV (2013) Hybrid learning for adaptive neuro fuzzy inference system. Int J Eng Sci 2(11):6–13

Maliqi E, Jusufi K, Singh SK (2020) Assessment and spatial mapping of groundwater quality parameters using metal pollution indices, graphical methods and geoinformatics. Anal Chem Lett 10(2):152–180CrossRef

May RJ, Dandy GC, Maier HR, Nixon JB (2008) Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ Modell Softw 23(10–11):1289–1299CrossRef

Mazandaran Rural Water and Sewer Company (ABFAR) (2017) Department of Researches and exploitation. The qualitative experiments for drinking water wells

McGarry KJ, Werner S, MacIntyre J (2001) Knowledge extraction from radial basis function networks and multi-layer perceptrons. Int J Comput Intell Appl 1(3):369–382CrossRef

Melloul AJ, Collins M (1998) A proposed index for aquifer water-quality assessment: the case of Israel’s Sharon region. J Environ Manage 54(2):131–142CrossRef

Memarian H, Pourreza Bilondi M, Rezaei M (2016) Drought prediction using co-active neuro-fuzzy inference system, validation, and uncertainty analysis (case study: Birjand, Iran). Theor Appl Climatol 125(3–4):541–554CrossRef

Millar EE, Hazell EC, Melles SJ (2019) The ‘cottage effect’ in citizen science? spatial bias in aquatic monitoring programs. Int J Geogrl Inf Sci 33(8):1612–1632CrossRef

Mosaffa M, Nazif S, Amirhosseini YK, Balderer W, Meiman MH (2021) An investigation of the source of salinity in groundwater using stable isotope tracers and GIS: a case study of the Urmia Lake basin, Iran. Groundwater Sustainable Dev 12:100513CrossRef

Mosavi AH, Sajedi Hosseini F, Choubin B, Abdolshahnejad M, Gharechaee HR, Lahijanzadeh A, Dineva AA (2020a) Susceptibility prediction of groundwater hardness using ensemble machine learning models. Water (Switzerland) 12(10):2770. https://doi.org/10.3390/w12102770CrossRef

Mosavi AH, Sajedi-Hosseini F, Choubin B, Taromideh F, Rahi GR, Dineva AA (2020b) Susceptibility mapping of soil water erosion using machine learning models. Water (Switzerland) 12(7):1995. https://doi.org/10.3390/w12071995CrossRef

Mosavi AH, Sajedi Hosseini F, Choubin B, Goodarzi M, Dineva AA (2020c) Groundwater salinity susceptibility mapping using classifier ensemble and bayesian machine learning models. IEEE Access 8(9162111):145564–145576. https://doi.org/10.1109/access.2020.3014908CrossRef

Muller SJ, Van Niekerk A (2016) An evaluation of supervised classifiers for indirectly detecting salt-affected areas at irrigation scheme level. Int J Appl Earth Obs Geoinf 49:138–150

Nikoo MR, Mahjouri N (2013) Water quality zoning using probabilistic support vector machines and self-organizing maps. Water Resour Manage 27:2577–2594CrossRef

Pal J, Chakrabarty D (2020) Assessment of artificial neural network models based on the simulation of groundwater contaminant transport. Hydrogeol J 28(1–2):1–17. https://doi.org/10.1007/s10040-020-02180-4CrossRef

Pirasteh S, Tripathi NK, Ayazi MH (2006) Localizing groundwater potential zones in parts of karst Pabdeh Anticline, Zagros Mountain, south-west Iran using geospatial techniques. Int J Geoinf 2(2):35–42

Pramanik N, Panda RK (2010) Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrol Sci J 55(8):1455–1456CrossRef

Saeedi M, Abessi O, Sharifi F, Meraji H (2010) Development of groundwater quality index. J Environ Monit Assess 163:327–335CrossRef

Sahour H, Gholami V, Vazifedan M (2020) A comparative analysis of statistical and machine learning techniques for mapping the spatial distribution of groundwater salinity in a coastal aquifer. J Hydrol 591:125321. https://doi.org/10.1016/j.jhydrol.2020.125321CrossRef

Scottish Development Department (1975) Towards cleaner water. Edinburgh: HMSO, Report of a River Pollution Survey of Scotland

Shi X, Jiang S, Xu H, Jiang F, He Z, Wu J (2016) The effects of artificial recharge of groundwater on controlling land subsidence and its influence on groundwater quality and aquifer energy storage in Shanghai. China Environ Earth Sci 75:195. https://doi.org/10.1007/s12665-015-5019-xCrossRef

Singh P (2014) An efficient concept-based mining model for analysis partitioning clustering. Int J Recent Technol Eng 2(6):1–3

Singh K, Shashtri S, Mukherjee M, Kumari R, Avatar R, Singh A, Prakash Singh R (2011) Application of GWQI to assess the effect of land-use change on groundwater quality in lower Shiwaliks of Punjab: remote sensing and GIS-based approach. J Water Resour Manag 25(7):1881–1898CrossRef

Talei A, Chua LHC, Quek C (2010) A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling. Expert Syst Appl 37(12):7456–7468CrossRef

TAMAB (Water Resources Research Organization of Iran) (2017) Mazandaran regional water company, hydrogeology studies of Mazandaran Plain. Atlas report

Tokar AS, Marcus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J Hydrol Engine ASCE 45:156–161CrossRef

Tongal H, Booij MJ (2018) Simulation and forecasting of streamflows using machine learning models coupled with base flow separation. J Hydrol 564:266–282CrossRef

Toor AK, Singh A (2013) Analysis of clustering algorithms based on number of clusters, error rate, computation time and map topology on large data set. Int J Emerging Trends Technol Comput Sci 2(6):94–98

Ullah N, Choudhury P (2013) Flood flow modeling in a river system using adaptive neuro-fuzzy inference system. Environ Manag Sustainable Dev 2(2):54–68. https://doi.org/10.5296/emsd.v2i2.3738CrossRef

Vapnik V (1998) Statistical learning theory. Wiley, New York

Varvani J, Khaleghi MR (2019) A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers. Acta Geophys 67(1):205–214CrossRef

Wang YM, Chang JX, Huang Q (2010) Simulation with RBF neural network model for reservoir operation rules. Water Resour Manag 24:2597–2610CrossRef

Wu S, Wang Z, Du Z, Huang B, Zhang F, Liu R (2020) Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships. Int J Geog Inf Sci 35(3):582–608CrossRef

Xie Y, Cai J, Bhojwani R, Shekhar S, Knight J (2020) A locally-constrained yolo framework for detecting small and densely-distributed building footprints. Int J Geog Inf Sci 34(4):777–801CrossRef

Title: Comparison of Self-Organizing Map, Artificial Neural Network, and Co-Active Neuro-Fuzzy Inference System Methods in Simulating Groundwater Quality: Geospatial Artificial Intelligence
Authors: V. Gholami
M. R. Khaleghi
S. Pirasteh
Martijn J. Booij
Publication date: 20-09-2021
Publisher: Springer Netherlands
Published in: Water Resources Management / Issue 2/2022
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-021-02969-2

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