Adhikary, P.P., Jyotiprava, D. (2017). Comparison of Deterministic and Stochastic Methods to Predict Spatial Variation of Groundwater Depth.
Applied Water Science, 17(6), 339-348.
https://doi.org/10.1007/s13201-014-0249-8
AliJani, F., Mousavi, F. & Mohammadi, H. (2022). The Study of Effects of Restoration and Resilience Plan on Groundwater of Qaleh Tol Plain, North East Khuzestan.
Sustainable Water and Development Journal, 9(3), 39-48. https://doi.org/
10.22067/JWSD.V9I3.2212.1196 (In Persian).
Arslan, H. (2014). Estimation of Spatial Distrubition of Groundwater Level and Risky Areas of Seawater Intrusion on the Coastal Region in Carsamba Plain. Turkey, Using Different Interpolation Methods. Environmental monitoring and assessment 186(8), 123-134. https://doi.org/10.1007/s10661-014-3764-z
BazrAfshan, O. & GarkaniNejad, Z. (2019). Optimizing of Piezometeric Wells Number for Groundwater Level Prediction Using Factor Analysis (Case Study: Minab Plain).
Iran Irrigation and Water Journal, 9(2), 80-94. https://doi.org/
10.22125/IWE.2019.87268 (In Persian).
Bhattacharjee, S., Chakravarty, S., Maity, S., Dureja, V. & Gupta, K.K. (2005). Metal Contents in the Groundwater of Sahebgunj District Jharkhand. India with Special Reference to Arsenic.
Chemosphere 58(9), 103-117.
https://doi.org/10.1016/j.chemosphere.2004.09.055
Bhunia, G.S., Shit, P.K. & Maiti, R. (2018). Comparison of Gis-Based Interpolation Methods for Spatial Distribution of Soil Organic Carbon (Soc).
Journal of the Saudi Society of Agricultural Sciences 17(2), 114-126.
https://doi.org/10.1016/j.jssas.2016.02.001
Bronowicka, U., Mielniczuk, J., Obroslak, R. & Przystupa, W. (2019). A Comparison of Some Interpolation Techniques for Determining Spatial Distribution of Nitrogen Compounds in Groundwater. International Journal of Environmental Research, 7(2), 1-9. https://doi.org/10.1007/s41742-019-00208-6
Daliakopoulos, I.N., Coulibaly, P. & Ioannis, K.T. (2005). Groundwater Level Forecasting Using Artificial Neural Networks.
Journal of hydrology, 30(9), 229-240.
https://doi.org/10.1016/j.jhydrol.2004.12.001
Delgado, C., Pacheco, J., Cabrera, A., Batllori, E., Orellana, R. & Bautista, F. (2010). Quality of Groundwater for Irrigation in Tropical Karst Environment: The Case of Yucatan, Mexico.
Agricultural Water Management, 97(10), 123-133.
https://doi.org/10.1016/j.agwat.2010.04.006
Ebadi, J. & Rezaei Moghadam, m. (2019). Estimating Accuracy of Artificial Neural Networks and Geo Statistical Methods in Interpolating Groundwater Levels Case Study: Shabestar-Sufian Plain.
Scientific-Research Quarterly of Geographical Information, 28(110), 133-45.
https://doi.org/10.22131/sepehr.2019.36618 (In Persian).
Eldrandaly, K.A. & AbuZaid, M.S. (2011). Comparison of Six Gis-Based Spatial Interpolation Methods for Estimating Air Temperature in Western Saudi Arabia. Journal of environmental Informatics 18(1), 38-49. https://doi.org/10.3808/jei.201100197
Elumalai, V., Brindha, K., Sithole, B. & Lakshmanan, E. (2017). Spatial Interpolation Methods and Geostatistics for Mapping Groundwater Contamination in a Coastal Area. Environmental Science and Pollution Research, 24(12), 160-171. https://doi.org/10.1007/s11356-017-8681-6
Gholizadeh Sarabi, Sh., Joodavi, A., Majidi Khalilabad, M., Ebrahimi, A. & Ronaghi, A. (2022). Methodology for Groundwater Mointoring Network Assessment and Design, Part2: IranEvaluation of Monitoring Network by Acceptance Probability Method.
Journal of Water and Sustainable Development, 9(3), 1-10. https://doi.org/
10.22067/JWSD.V9I3.2203.1131 (In Persian).
Ghorbani, F. & Salarijezi, A. (2018). Evaluation of the Empirical Bayesian Kriging Method in Groundwater Level Zoning.
Journal of Water and Soil Conservation Research, 25(1), 165-182. https://doi.org/
10.22069/JWSC.2018.13571.2826 (In Persian).
Ghosh, M., Pal, D.K. & Santra, S.C. (2019). Spatial Mapping and Modeling of Arsenic Contamination of Groundwater and Risk Assessment through Geospatial Interpolation Technique. Environment, Development and Sustainability, 22(7), 2861-2880. https://doi.org/10.1007/s10668-019-00322-7
Gong, G., Mattevada, S. & O’Bryant, S.E. (2014). Comparison of the Accuracy of Kriging and IDW Interpolations in Estimating Groundwater Arsenic Concentrations in Texas.
Environmental research, 130(3), 59-69.
https://doi.org/10.1016/j.envres.2013.12.005
Habibabadi, N.G. & Derakhshan, H. (2023). Groundwater-Drought Conjunctive Management: A Review of California Experiences.
Journal of Water and Sustainable Development, 10(1), 77-86. https://doi.org/
10.22067/JWSD.V10I1.2302-1219 (In Persian).
Jie, C., Zhang, H., Hui, Q., Jianhua, W. & Xuedi, Z. (2013). Selecting Proper Method for Groundwater Interpolation Based on Spatial Correlation.
International Conference on Digital Manufacturing & Automation. https://doi.org/
10.1109/ICDMA.2013.282
Kalhor, A., Araabi, B.N. & Lucas, C. (2013). Evolving Takagi–Sugeno Fuzzy Model Based on Switching to Neighboring Models.
Applied Soft Computing, 13(2), 939-946.
https://doi.org/10.1016/j.asoc.2012.09.015
Kaminska, A. & Grzywna, A. (2014). Comparison of Deteministic Interpolation Methods for the Estimation of Groundwater Level.
Journal of Ecological Engineering, 15(4), 55-65.
https://doi.org/10.12911/22998993.1125458
Khodadadi, M., Nassery, H.R. & Nikpeyman, Y. (2022). Assessment of the Groundwater Restoration and Balancing with Emphasis on Smart Meters in Shahriar Plain.
Sustainable Water and Development Journal, 9(3), 49-56. https://doi.org/
10.22067/JWSD.V9I3.2212.1197 (In Persian).
Li, J. & Heap, A.D. (2014). Spatial Interpolation Methods Applied in the Environmental Sciences: A Review.
Environmental Modelling & Software, 53(3), 173-189.
https://doi.org/10.1016/j.envsoft.2013.12.008
Ma, S., Chen, F., Wang, Q. & Zhao, Z. (2012). Sugeno Type Fuzzy Complex-Value Integral and Its Application in Classification.
Procedia Engineering, 29(6), 41-51.
https://doi.org/10.1016/j.proeng.2012.01.634
Nikzad, M., Moradi, H.R. & Jalili, Kh. (2018). Estimation of Temporal and Spatial Variations of the Level of the Aquifers in Bisotun Plain of Kermanshah Province with Geostatistical Methods. Iran Irrigation and Water Journal, 8(4), 79-92. (In Persian).
Ohmer, M., Liesch, T., Goeppert, N. & Goldscheider, N. (2017). On the Optimal Selection of Interpolation Methods for Groundwater Contouring: An Example of Propagation of Uncertainty Regarding Inter-Aquifer Exchange.
Advances in water resources, 109(3), 121-132.
https://doi.org/10.1016/j.advwatres.2017.08.016
Piazza, A.D., Conti, F.L., Noto, L.V., Viola, F. & Loggia, G.L. (2011). Comparative Analysis of Different Techniques for Spatial Interpolation of Rainfall Data to Create a Serially Complete Monthly Time Series of Precipitation for Sicily Italy.
International Journal of Applied Earth Observation and Geoinformation, 13(3), 396-408.
https://doi.org/10.1016/j.jag.2011.01.005
Rasoli, M.M., Ketabchi, H. & MahmoudZadeh, D. (2023). Evaluation of Interpolation Methods for Zoning the Groundwater Level and Determining the Flow Direction (Case Study: two study areas of Iran). 21st Iranian Hydraulic Conference, Martyr Chamran University of Ahvaz. (In Persian).
Regional water organization Razavi Khorasan. 2017. (In Persian).
Samadi, M. (2017). Spatio-Temporal Modeling of Groundwater Level Variations of Urban and Rural Areas in Kashan Aquifer Using GIS Techniques.
Environmental Science and Technology Quarterly, 19(1), 63-77. https://doi.org/
10.22034/jest.2017.10329 (In Persian).
Shahid, S.U., Iqbal, J. & Khan, S.J. (2017). A Comprehensive Assessment of Spatial Interpolation Methods for the Groundwater Quality Evaluation of Lahore. Punjab Pakistan.
NUST Journal of Engineering Sciences, 10(1), 1-13.
https://doi.org/10.24949/njes.v10i1.239
Sivakugan, N., Al-Adili, A.S. & Ali, M.H. (2018). Comparison between Deterministic and Stochastic Interpolation Methods for Predicting Groundwater Level in Baghdad.
Engineering and Technology Journal, 36(12), 12-25. https://doi.org/
10.30684/etj.36.12A.2
Subedi, M.R., Xi, W., Edgar, C.B., Rideout-Hanzak, S. & Hedquist, B.C. (2019). Assessment of Geostatistical Methods for Spatiotemporal Analysis of Drought Patterns in East Texas USA. Spatial Information Research, 27(1), 11-21. https://doi.org/10.1007/s41324-018-0216-9
Wang, S., Huang, GH., Lin, QG., Li, Z., Zhang, H. & Fan, YR. (2014). Comparison of Interpolation Methods for Estimating Spatial Distribution of Precipitation in Ontario Canada. International Journal of Climatology, 34(14), 37-51. https://doi.org/10.1002/joc.3941
Xiao, Y., Gu, X., Yin, S., Shao, J., Cui, Y., Zhang, Q. & Niu, Y. (2016). Geostatistical Interpolation Model Selection Based on Arcgis and Spatio-Temporal Variability Analysis of Groundwater Level in Piedmont Plains, Northwest China. SpringerPlus, 5(1), 425-436. https://doi.org/10.1186/s40064-016-2073-0