Main Article Content
The GIS-aided spatial interpolation was applied on collected groundwater data to predict selected parameters (i.e., pH, electrical conductivity, and temperature) for the selected water wells distributed over Mosul City in Iraq. A descriptive statistical analysis was conducted on collected samples to explore the statistical indices. The skewness test was also employed to test the distribution of data sets around their mean values. The natural logarithms function achieved least skewness values and thus was applied to transfer data sets in order to adjust normality of the data sets distribution. Among all applied semivariogram models, the J-Bessel semivariogram model was optimal in terms of root mean square error (RMSE) values. The average standard errors were 0.2217, 740.5, and 1.209 for pH, EC, and temperature, respectively.
Al-Tamir, M. A. (2021). Stability evaluation of Tigris River raw water and treated drinking water from main water treatment plants within Mosul City. Desalination and Water Treatment, 226, 52–61. (Crossref)
Alqahtany, A. (2023). GIS-based assessment of land use for predicting increase in settlements in Al Ahsa Metropolitan Area, Saudi Arabia for the year 2032. Alexandria Engineering Journal, 62, 269–277. (Crossref)
Beana, B., Sun, Y. & Maguire, M. (2022). Interval-valued kriging for geostatistical mapping with imprecise inputs. International Journal of Approximate Reasoning, 140, 31–51. (Crossref)
Boroh, A. W., Lawou, S. K., Mfenjou, M. L. & Ngounouno, I. (2022). Comparison of geostatistical and machine learning models for predicting geochemical concentration of iron: case of the Nkout iron deposit (south Cameroon). Journal of African Earth Sciences, 195, 104662. (Crossref)
Fan, F. M., Fleischmann, A. S., Collischonn, W., Ames, D. P. & Rigo, D. (2015). Large-scale analytical water quality model coupled with GIS for simulation of point sourced pollutant discharges. Environmental Modelling & Software, 64, 58–71. (Crossref)
Grynyshyna-Poliuga, O. (2019). Characteristic of modelling spatial processes using geostatistical analysis. Advances in Space Research, 64, 415–426. (Crossref)
Hair, J. F., Hult, J. G. T. M., Ringle, C. M. & Sarstedt, M. S. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). Thousand Oaks: SAGE Publications.
Koike, K., Kiriyama, T., Lu, L., Kubo, T., Heriawan, M. N. & Yamada, R. (2022). Incorporation of geological constraints and semivariogram scaling law into geostatistical modeling of metal contents in hydrothermal deposits for improved accuracy. Journal of Geochemical Exploration, 233, 106901. (Crossref)
Kourgialas, N. N., Karatzas, G. P. & Koubouris, G. C. (2017). A GIS policy approach for assessing the effect of fertilizers on the quality of drinking and irrigation water and wellhead protection zones (Crete, Greece). Journal of Environmental Management, 189, 150–159. (Crossref)
Minitab (2010). Minitab 17 Statistical software. State College: Minitab.
Montero, J. M., Fernández-Avilés, G. & Mateu, J. (2015). Spatial and spatio-temporal geostatistical modeling and kriging. Hoboken: John Wiley & Sons. (Crossref)
Raju, N. J. (2016). Geostatistical and geospatial approaches for the characterization of natural resources in the environment challenges, processes and strategies. Berlin: Springer. (Crossref)
Silva, M. V. da, Pandorfi, H., Almeida, G. L. P. de, Silva, R. A. B. da, Morales, K. R. M., Guiselini, C., Santana, T. C., Cangela, G. L. Ch. de, Filho, J. A. D. B., Moraes, A. S., Montenegro, A. A. A. & Oliveira Júnior, J. F. de (2023). Spatial modeling via geostatistics and infrared thermography of the skin temperature of dairy cows in a compost barn system in the Brazilian semiarid region. Smart Agricultural Technology, 3, 100078. (Crossref)
Sukkuea, A. & Heednacram, A. (2022). Prediction on spatial elevation using improved kriging algorithms: An application in environmental management. Expert Systems with Applications, 207, 117971. (Crossref)
Thanoon, S. R. (2018). Application trend surface models with estimation. Tikrit Journal of Pure Science, 23 (10), 118–122. (Crossref)
Xue, S., Korna, R., Fan, J., Ke, W., Lou, W., Wang, J. & Zhu, F. (2023). Spatial distribution, environmental risks, and sources of potentially toxic elements in soils from a typical abandoned antimony smelting site. Journal of Environmental Sciences, 127, 780–790. (Crossref)
Yang, J. W., Chiu, S. Y. & Yen, K. C. (2023). Does the realized distribution-based measure dominate particular moments? Evidence from cryptocurrency markets. Finance Research Letters, 51, 103396. (Crossref)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.