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Optimal design of groundwater monitoring networks using gamma test theory

Conception optimale des réseaux de surveillance des eaux souterraines à l’aide de la théorie du test gamma

Diseño óptimo de redes de monitoreo de aguas subterráneas utilizando la teoría de pruebas gamma

基于伽玛检验理论的地下水监测网优化设计

Projeto ideal de redes de monitoramento de águas subterrâneas usando a teoria dos testes gama

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Abstract

Gamma test theory (GTT) is introduced as a novel method to determine the optimal number and location of groundwater monitoring wells without requiring temporal monitoring data. This method is based on the calculation of a statistic, called gamma, for the data of one monitoring period. The optimal wells are selected such that while they have the lowest gamma value, a further increase in the number of wells does not cause much change in their gamma value. The method was applied to the design of an optimal monitoring network for groundwater electrical conductivity (EC) on Kish Island, Hormozgan Province, Iran. The water EC of 55 wells, selected from 244 existing wells, was measured during one monitoring period on Kish Island and their latitude and longitude were recorded. A groundwater EC monitoring network was optimized using a GTT-based optimization algorithm. Based on the results, to estimate the spatial distribution of groundwater EC on Kish Island with maximum achievable accuracy, it was necessary to monitor at least 110 wells, which were identified. Finally, the water EC of the proposed wells was monitored in three monitoring periods and the proposed network was evaluated in these periods. Results indicated that the proposed wells are also optimum in these periods and the spatial distribution of groundwater EC can be estimated with maximum achievable accuracy using the EC data of the proposed 110 wells. The current study provides a time- and cost-effective method to achieve an efficient groundwater monitoring network especially when there is data limitation.

Résumé

La théorie du test gamma (TTG) est introduite comme une nouvelle méthode pour déterminer le nombreoptimal et la localisation des points de suivi des eaux souterraines sans nécessiter des données de suivitemporel. Cette méthode se base sur un paramètre statistique, dénommé gamma, pour les données d’unepériode de suivi. Les puits optimaux sont sélectionnés de telle sorte que, bien qu’ils aient la valeurgamma la plus faible, une augmentation ensuite du nombre de puits n’entraine pas un changementsignificatif de leur valeur gamma. Cette méthode a été appliquée à la conception du réseau optimum desuivi de la conductivité électrique des eaux souterraines (CE) sur l’île de Kish, Province de Hormozgan,Iran. La conductivité électrique de l’eau de 55 puits sélectionnés parmi 244 forages existants, a été mesurédurant une période de suivi sur l’ile de Kish et leur latitude et longitude ont été enregistrées. Un réseau desuivi de la CE des eaux souterraines a été optimisé en utilisant un algorithme d’optimisation basé sur laTTG. A partir de ces résultats et afin d’estimer la distribution spatiale de la CE des eaux souterraines surl’île de Kish avec le maximum de précision possible, il était nécessaire de suivre au moins 110 puits quiont par ailleurs été identifié. Enfin, la CE de l’eau des puits proposés a été mesuré durant trois périodesde suivi et le réseau a été évalué ensuite sur ces périodes. Les résultats montrent que les puits proposéssont également optimum pour cette nouvelle période et la distribution spatiale de la CE des eauxsouterraines a été estimé avec un maximum de précision possible en utilisant la CE des 110 puits. Cetteétude propose une méthode efficace nécessitant peu de temps et à faible coût pour la conception d’unréseau de suivi des eaux souterraines et plus spécialement lorsqu’on dispose d’un nombre limité dedonnées.

Resumen

La teoría de pruebas gamma (GTT) se introduce como un método novedoso para determinar el número y la ubicación óptima de pozos de monitoreo de aguas subterráneas sin requerir datos de monitoreo temporal. Este método se basa en el cálculo de una estadística, llamada gamma, para los datos de un período de seguimiento. Los pozos óptimos se seleccionan de tal manera que, si bien tienen el valor gamma más bajo, un aumento adicional en el número de pozos no causa mucho cambio en su valor gamma. El método se aplicó al diseño de una red óptima de monitoreo de la conductividad eléctrica CE) de las aguas subterráneas en la isla de Kish, provincia de Hormozgan, Irán. La CE del agua de 55 pozos, seleccionados de entre 244 pozos existentes, se midió durante un período de monitoreo en la isla de Kish y se registraron su latitud y longitud. Se optimizó una red de monitoreo de CE de aguas subterráneas utilizando un algoritmo de optimización basado en GTT. Con base en los resultados, para estimar la distribución espacial de la CE de las aguas subterráneas en la Isla de Kish con la máxima precisión alcanzable, fue necesario monitorear al menos 110 pozos, los cuales fueron identificados. Finalmente, la CE de agua de los pozos propuestos fue monitoreada en tres períodos de monitoreo y la red propuesta fue evaluada en esos períodos. Los resultados indicaron que los pozos propuestos son también óptimos en estos períodos y que la distribución espacial de la CE de aguas subterráneas puede estimarse con la máxima precisión alcanzable utilizando los datos de la CE de los 110 pozos propuestos. El presente estudio proporciona un método de tiempo y costo efectivo para lograr una red eficiente de monitoreo de aguas subterráneas, especialmente cuando hay limitaciones de datos.

摘要

引入了伽玛测试理论(GTT)的新方法来确定地下水监测井的最佳数量和位置,而无需随时间变化的监测数据。此方法基于监测周期数据的统计量(称为gamma)的计算。选择最佳孔,以使它们具有最低的伽玛值,但孔数的进一步增加不会引起其伽玛值的很大变化。该方法已应用于伊朗Hormozgan 省Kish 岛的地下水电导率(EC)最佳监测网络的设计。在监测期内,在Kish 岛上测量了从244 口现有井中选出的55 口井水的EC,并记录了它们的经度和纬度。使用基于GTT 的优化算法优化了地下水EC 监测网络。根据这些结果,要以最大可能的精度估算Kish 岛上地下水EC 的空间分布,有必要监测至少110 口已确定的井。最后,在三个监测期内对拟建井水中EC 进行了监测,并在这三个时期对拟建网络进行了评估。结果表明,建议的井在这些时期也是最佳的,并且可以使用建议的110 口井的EC 数据以最大可能的精度估算地下水EC 的空间分布。当前的研究提供了一种节省时间和成本的方法来实现有效的地下水监测网络,尤其是在数据有限的情况下。

Resumo

A teoria do teste Gama (TTG) é introduzida como um novo método para determinar o número e a locação ideal de poços de monitoramento de águas subterrâneas sem a necessidade de dados de monitoramento temporal. Esse método é baseado no cálculo de uma estatística, chamada gama, para os dados de um período de monitoramento. Os poços ótimos são selecionados de forma que, embora tenham o menor valor gama, um aumento adicional no número de poços não cause muita alteração em seu valor gama. O método foi aplicado ao projeto de uma rede de monitoramento ideal para o monitoramento da condutividade elétrica (CE) de águas subterrâneas na ilha de Kish, província de Hormozgan, Irã. A CE da água de 55 poços, selecionada entre 244 poços existentes, foi medida durante um período de monitoramento na ilha de Kish e sua latitude e longitude foram registradas. Uma rede de monitoramento de CE das águas subterrâneas foi otimizada usando um algoritmo de otimização baseado na TTG. Com base nos resultados, para estimar a distribuição espacial da CE das águas subterrâneas na ilha de Kish com a máxima precisão possível, foi necessário monitorar pelo menos 110 poços identificados. Finalmente, a CE das águas dos poços propostos foi monitorada em três períodos de monitoramento e a rede proposta foi avaliada nesses períodos. Os resultados indicaram que os poços propostos também são ótimos nesses períodos e a distribuição espacial da CE água subterrânea pode ser estimada com a máxima precisão possível, usando os dados da CE dos 110 poços propostos. O presente estudo fornece um método efetivo de tempo/custo para obter uma rede eficiente de monitoramento de águas subterrâneas, especialmente quando há limitação dos dados.

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References

  • Abiye T, Masindi K, Mengistu H, Demlie M (2018) Understanding the groundwater-level fluctuations for better management of groundwater resource: a case in the Johannesburg region. Groundw Sustain Dev 7:1–7

    Article  Google Scholar 

  • Al Dahaan S, Al-Ansari N, Knutsson S (2016) Influence of groundwater hypothetical salts on electrical conductivity total dissolved solids. Engineering 8:823–830

    Article  Google Scholar 

  • Ataie-Ashtiani B (2010) Study of optimum groundwater usage in Kish Island. Final reports, Iran Kish Free Zone Organization (KFZO), Sanaee, Iran

  • Ayvaz MT, Elçi A (2018) Identification of the optimum groundwater quality monitoring network using a genetic algorithm based optimization approach. J Hydrol 563:1078–1091

    Article  Google Scholar 

  • Azadi S, Amiri H, Rakhshandehroo GR (2016) Evaluating the ability of artificial neural network and PCA-M5P models in predicting leachate COD load in landfills. Waste Manag 55:220–230

    Article  Google Scholar 

  • Azadi S, Karimi-Jashni A (2016) Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: a case study of Fars province, Iran. Waste Manag 48:14–23

    Article  Google Scholar 

  • Azadi S, Karimi-Jashni A, Javadpour S (2018) Modeling and optimization of photocatalytic treatment of landfill leachate using tungsten-doped TiO2 nano-photocatalysts: application of artificial neural network and genetic algorithm. Process Saf Environ Prot 117:267–277

    Article  Google Scholar 

  • Baalousha H (2010) Assessment of a groundwater quality monitoring network using vulnerability mapping and geostatistics: a case study from Heretaunga Plains, New Zealand. Agric Water Manag 97:240–246

    Article  Google Scholar 

  • Babanezhad E, Qaderi F, Ziri MS (2018) Spatial modeling of groundwater quality based on using Schoeller diagram in GIS base: a case study of Khorramabad, Iran. Environ Earth Sci 77:339

    Article  Google Scholar 

  • Chen I-T, Chang L-C, Chang F-J (2018) Exploring the spatio-temporal interrelation between groundwater and surface water by using the self-organizing maps. J Hydrol 556:131–142

    Article  Google Scholar 

  • Cheo AE, Voigt H-J, Wendland F (2017) Modeling groundwater recharge through rainfall in the far-north region of Cameroon. Groundw Sustain Dev 5:118–130

    Article  Google Scholar 

  • Esquivel JM, Morales GP, Esteller MV (2015) Groundwater monitoring network design using GIS and multicriteria analysis. Water Resour Manag 29:3175–3194

    Article  Google Scholar 

  • Haghiabi AH, Parsaie A, Ememgholizadeh S (2018) Prediction of discharge coefficient of triangular labyrinth weirs using adaptive neuro fuzzy inference system. Alexandria Eng J 57:1773–1782

    Article  Google Scholar 

  • Haykin S (1994) Neural networks: a comprehensive foundation. Prentice Hall, Englewood Cliffs, NJ

  • Hosseini M, Kerachian R (2017) A data fusion-based methodology for optimal redesign of groundwater monitoring networks. J Hydrol 552:267–282

    Article  Google Scholar 

  • Iturrarán-Viveros U, Parra JO (2014) Artificial neural networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well-log data. J Appl Geophys 107:45–54

    Article  Google Scholar 

  • Izady A, Abdalla O, Ahmadi T, Chen M (2017) An efficient methodology to design optimal groundwater level monitoring network in Al-Buraimi region, Oman. Arab J Geosci 10:26

    Article  Google Scholar 

  • Kang SY (1992) An investigation of the use of feedforward neural networks for forecasting. PhD Thesis, Kent State University, Kent, OH

  • Kemp S, Wilson I, Ware J (2004) A tutorial on the gamma test. Int J Simulat: Syst Sci Tech 6:67–75

    Google Scholar 

  • Khashei-Siuki A, Sarbazi M (2015) Evaluation of ANFIS, ANN, and geostatistical models to spatial distribution of groundwater quality (case study: Mashhad plain in Iran). Arab J Geosci 8:903–912

    Article  Google Scholar 

  • Koncar N (1997) Optimisation methodologies for direct inverse neurocontrol. University of London, London

  • Lafdani EK, Nia AM, Ahmadi A (2013) Daily suspended sediment load prediction using artificial neural networks and support vector machines. J Hydrol 478:50–62

    Article  Google Scholar 

  • Leach JM, Coulibaly P, Guo Y (2016) Entropy based groundwater monitoring network design considering spatial distribution of annual recharge. Adv Water Resour 96:108–119

    Article  Google Scholar 

  • Luo Q, Wu J, Yang Y, Qian J, Wu J (2016) Multi-objective optimization of long-term groundwater monitoring network design using a probabilistic Pareto genetic algorithm under uncertainty. J Hydrol 534:352–363

    Article  Google Scholar 

  • Malik A, Kumar A, Piri J (2017) Daily suspended sediment concentration simulation using hydrological data of Pranhita River basin, India. Comput Electron Agric 138:20–28

    Article  Google Scholar 

  • Maroufpoor S, Fakheri-Fard A, Shiri J (2019) Study of the spatial distribution of groundwater quality using soft computing and geostatistical models. ISH J Hydraul Eng 25:232–238

    Article  Google Scholar 

  • Marquez R, Coimbra CF (2011) Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Sol Energy 85:746–756

    Article  Google Scholar 

  • Minsker B (2003) Long-term groundwater monitoring: the state of the art. ASCE, Washington, DC

  • Mirzaie-Nodoushan F, Bozorg-Haddad O, Loáiciga HA (2017) Optimal design of groundwater-level monitoring networks. J Hydroinf 19:920–929

    Article  Google Scholar 

  • Noori R, Karbassi A, Sabahi MS (2010) Evaluation of PCA and gamma test techniques on ANN operation for weekly solid waste prediction. J Environ Manag 91:767–771

    Article  Google Scholar 

  • Pitcher E (2017) Do fault-related folds follow the same scale law properties as their associated faults? MSc Thesis, Durham University, Durham, NC

  • Preziosi E, Petrangeli A, Giuliano G (2013) Tailoring groundwater quality monitoring to vulnerability: a GIS procedure for network design. Environ Monit Assess 185:3759–3781

    Article  Google Scholar 

  • Qaderi F, Babanezhad E (2017) Prediction of the groundwater remediation costs for drinking use based on quality of water resource, using artificial neural network. J Clean Prod 161:840–849

    Article  Google Scholar 

  • Rezayat H, Bell JR, Plotkowski AJ, Babu SS (2018) Multi-solution nature of topology optimization and its application in design for additive manufacturing. Rapid Prototyp J

  • Sakakibara K, Tamaki H, Nishikawa I (2007) Autonomous distributed approaches for pickup and delivery problems with time windows. SICE Annual Conference 2007, Takamatsu, Japan, September 2007, pp 2639–2642

  • Salem GSA, Kazama S, Shahid S, Dey NC (2018) Impacts of climate change on groundwater level and irrigation cost in a groundwater dependent irrigated region. Agric Water Manag 208:33–42

    Article  Google Scholar 

  • Storck P, Eheart JW, Valocchi AJ (1997) A method for the optimal location of monitoring wells for detection of groundwater contamination in three-dimensional heterogenous aquifers. Water Resour Res 33:2081–2088

    Article  Google Scholar 

  • Tam VT, Nga TTV (2018) Assessment of urbanization impact on groundwater resources in Hanoi, Vietnam. J Environ Manag 227:107–116

    Article  Google Scholar 

  • Trabelsi N, Triki I, Hentati I, Zairi M (2016) Aquifer vulnerability and seawater intrusion risk using GALDIT, GQI SWI and GIS: case of a coastal aquifer in Tunisia. Environ Earth Sci 75:669

  • Tuinhof A, Foster S, Kemper K, Garduno H, Nanni M (2003) Groundwater monitoring requirements for managing aquifer response and quality threats. World Bank Briefing Note 9, World Bank, Washington, DC

  • Wang Y, Li J, Gu J, Zhou Z, Wang Z (2015) Artificial neural networks for infectious diarrhea prediction using meteorological factors in Shanghai (China). Appl Soft Comput 35:280–290

    Article  Google Scholar 

  • Wu S-C, Ke K-Y, Lin H-T, Tan Y-C (2017) Optimization of groundwater quality monitoring network using risk assessment and geostatistic approach. Water Resour Manag 31:515–530

    Article  Google Scholar 

  • Yin S, Gu X, Xiao Y, Wu W, Pan X, Shao J, Zhang Q (2017) Geostatistics-based spatial variation characteristics of groundwater levels in a wastewater irrigation area, northern China. Water Sci Technol Water Supply 17:1479–1489

    Article  Google Scholar 

  • Zhang G, Patuwo BE, Hu MY (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62

    Article  Google Scholar 

Download references

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We wish to thank the three anonymous reviewers of this manuscript, the editor, and the editorial team for providing valuable comments and constructive suggestions.

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Correspondence to Sirus Javadpour.

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Azadi, S., Amiri, H., Ataei, P. et al. Optimal design of groundwater monitoring networks using gamma test theory. Hydrogeol J 28, 1389–1402 (2020). https://doi.org/10.1007/s10040-020-02115-z

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