Optimization of sampling wells by a spatial-temporal approach in the groundwater level monitoring network (Case study: Sarab plain)

Document Type : Research Article

Authors

Department of Surveying Engineering, College of Earth Sciences Engineering, Arak University of Technology, Arak, Iran

Abstract

Optimizing the piezometric wells in the groundwater monitoring network is important in terms of reducing maintenance costs and improving efficiency and increasing the speed of data updating. Statistical methods or PCA are commonly used to identify significant wells. In geostatistical methods, optimization is performed according to the location of the samples but the temporal information of the wells is not taken into account. In the PCA method, indicator wells are determined in terms of temporal information of wells. In this research, an approach based on a combination of these two methods is presented to consider the spatiotemporal information of wells. The present study was conducted by obtaining data from 47 wells related to the first aquifer of Sarab plain in 1397 in three main stages: 1- Exploratory spatial data analysis (ESDA), 2- Determining the priority of wells based on temporal information of the wells in the neighborhood by PCA method, 3- Investigating the surface accuracy changes by kriging method assuming the removal of wells with low temporal priority. The results showed that 9 wells of Sarab plain (19%) have relative importance less than 0.3. By removing these wells and evaluating the RMSE error in the deleted points, the value of these wells can be calculated with 46cm error through spatial autocorrelation information. Therefore, the removed wells do not enter much spatial information, and by removing them, it is possible to increase the accuracy of measuring the water level in other wells and save time and cost with the same accuracy.

Keywords

References

[1].  Sayadi shahraki A, naseri a, boromandnasab s, soltani a. Designing a network for monitoring groundwater level using the Principal Component Analysis technique. 2020;13(44):29-37.
[2].  Destandau F, Zaiter Y. Spatio-temporal design for a water quality monitoring network maximizing the economic value of information to optimize the detection of accidental pollution. Water Resources and Economics. 2020;32:100156.
[3].  Ou C-P, St-Hilaire A, Ouarda T, Conly FM, Armstrong N, Khalil B, et al. Coupling geostatistical approaches with PCA and fuzzy optimal model (FOM) for the integrated assessment of sampling locations of water quality monitoring networks (WQMNs). Journal of environmental monitoring : JEM. 2012;14.
[4].  Aadil N, Gallardo A, Ahmed S. Optimization of a Groundwater Monitoring Network for a Sustainable Development of the Maheshwaram Catchment, India. Sustainability. 2011;3.
[5].  Abdollahi Mansourkhani M, Mohammadzade H, Amini M, Azizi F. Assessment of Groundwater Quality Spatial Distribution and Appointment Optimize Network of Shahrkord Plain Aquifer Using Geostatistical Methods. Watershed Management Research Journal. 2019;32(2):60-78.
[6].  Taheri Zangi S, Vaezihir A. Vulnerability of Shazand Plain Subsidence Caused by Groundwater Level Reduction Using Weighting Model and Its Validation Analysis Using Radar Interferometry. Iranian journal of Ecohydrology. 2020;7(1):183-94.
[7].  Hosseini M, Kerachian R. A data fusion-based methodology for optimal redesign of groundwater monitoring networks. Journal of Hydrology. 2017;552:267-82.
[8].  Lashkaripour G, Rostami Barani H, Kohandel A, Tarshizi H. Groundwater level drop and land subsidence in Kashmar plain. 10th Iranian Geological Society Conference; Tehran2006.
[9].  Khashei A, Shahidi A, Rahnama S. Comparision of Birjand Plain Aquifer Chromium Monitoring Network Using Principal Component Analysis (PCA) and Entropy Theory. Environment and Water Engineering. 2021;7(2):220-31.
[10].  komasi m, goudarzi h. Multi-Objective Optimization Groundwater Network Using Genetic Algorithm (NSGA-II) and Empirical Bayesian Kriging (EBK) Method (Case Study: Silakhor plain). Irrigation and Water Engineering. 2021;11(3):204-20.
[11].  Shahidi A, Khashei Siouki A, Ramezani Y, Nazeri Tehrani M. Design of rain gauge monitoring network using irregularity theory (Case study: Urmia Lake Basin). Irrigation and Drainage of Iran. 2019;13(2):296-308.
[12].  Vu MT, Jardani A, Massei N, Fournier M. Reconstruction of missing groundwater level data by using Long Short-Term Memory (LSTM) deep neural network. Journal of Hydrology. 2020;597:125776.
[13].  Khodaverdi M, Hashemi sR, Khashei-Siuki A, Pourreza- Bilondi M. Optimal Design of Groundwater-Quality Sampling Networks with MOPSO-GS (Case Study: Neyshabour Plain). Water and Irrigation Management. 2020;9(2):199-210.
[14].  Wang C, Zhao L, Sun W, Xue J, Xie Y. Identifying redundant monitoring stations in an air quality monitoring network. Atmospheric Environment. 2018;190:256-68.
[15].  Seifipour K, Mirabbasi R, Mirzaei M. Application of Entropy Theory in Assessing Groundwater Quality Monitoring Network of Sefiddasht. Hydrogeology. 2020;4(2):63-73.
[16].  Sottani A, Meggiorin M, Ribeiro L, Rinaldo A. Comparison of two methods for optimizing existing groundwater monitoring networks: application to the Bacchiglione Basin, Italy2020.
[17].  Galán-Madruga D, García-Cambero JP. An optimized approach for estimating benzene in ambient air within an air quality monitoring network. Journal of Environmental Sciences. 2022;111:164-74.
[18].  Noori gheidari Mh. Determintion of Effective Wells to Monitor the Ground Water Level Using the Principal Components Analysis. JSTNAR. 2013;17(64):149-59.
[19].  Camacho J, Pérez-Villegas A, García-Teodoro P, Maciá-Fernández G. PCA-based multivariate statistical network monitoring for anomaly detection. Computers & Security. 2016;59:118-37.
[20].  Teng SY, How BS, Leong WD, Teoh JH, Siang Cheah AC, Motavasel Z, et al. Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries. Journal of Cleaner Production. 2019;225:359-75.
[21].  Ghadban N, Honeine P, Francis C, Mourad-Chehade F, Farah J, editors. Strategies for principal component analysis in wireless sensor networks. 2014 IEEE 8th Sensor Array and Multichannel Signal Processing Workshop (SAM); 2014 22-25 June 2014.
[22].  Babaei Hessar S, Hamdami Q, Ghasemieh H. Identify the Effective Wells in Determination of Groundwater Depth in Urmia Plain Using Principle Component Analysis. Water and Soil. 2017;31(1):40-50.
[23].  Silva M, Santos A, Santos R, Figueiredo E, Sales C, Costa JCWA. Deep principal component analysis: An enhanced approach for structural damage identification. Structural Health Monitoring. 2018;18(5-6):1444-63.
[24].  Raeisi A, Ghafouri H-R, Moslemzadeh M. Minimization of Groundwater Observation Wells Using Geostatistics and Optimization Technique (Case study: Dezfoul-Andimeshk plain). Journal of Water and Soil Conservation. 2018;25(3):79-96.
[25].  Hooshangi N. Determination of valuable piezometric wells in groundwater level prediction by considering spatiotemporal information. GEO. 2020;13(49):37-49.
[26].  Hooshangi N, Alesheikh A, Nadiri A. Optimization of Piezometers Number for Groundwater Level Prediction Using PCA and Geostatistical Methods. Water and Soil Science. 2016;25(4/2):53-66.
[27].  Jahanbakhsh Asl S, Sari Sarraf B, Khorshid Doost AM, Rostamzadeh H. Evaluation of vegetation changes in Sarab plain and analysis of drought and wet seasons Geography 2009;23(7):118-34.
[28].  karami F, Rostamzadeh H. Investigation of effective factors in salinization of Sarab plain lands. Iranian Journal of Natural Resources. 2008;4(3).
[29].  Kaffash Charandabi N. Forecasting Air Pollution based on Monitoring Station with using Kalman Filter. New Approaches in Civil Engineering. 2019;3(3):46-60.
[30].  Preda C, Saporta G, Mbarek M. The NIPALS algorithm for missing functional data. Revue Roumaine de Mathématiques Pures et Appliquées. 2010;55.
[31].  Hooshangi N, Alesheikh AA. Evaluation OF ANN, ANFIS and FUZZY Systems in estimation of solar radiation in Iran. Journal of gematics science and technology. 2015;4(3):187-200.
Iranian journal of Ecohydrology
Volume 8, Issue 3
October 2021
Pages 777-790

Files

History

  • Receive Date: 22 April 2021
  • Revise Date: 22 July 2021
  • Accept Date: 08 August 2021
  • First Publish Date: 20 September 2021
  • Publish Date: 23 September 2021

Share

How to cite

Statistics