Evaluation and Accuracy of Artificial Intelligence, Geostatistics and Inverse distance weighting Methods in Simulation the Groundwater Depth

Subject Areas : Article frome a thesis

Atefeh Sayadi Shahraki ¹ , saeed boroomand nasab ² , abd ali naseri ³ , amir soltani mohammadi ⁴

1 - 1- Ph.D. of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz. Ahvaz, Iran.
2 - Associate Professor of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz. Ahvaz, Iran.
3 - Associate Professor of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz. Ahvaz, Iran.
4 - Associate Professor of Irrigation and Drainage, Faculty of Water and Enviromental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

Received: 2019-09-28 Accepted : 2021-10-26 Published : 2021-10-23

Keywords: Geostatistics, Artificial neural network, Groundwater depth, IDW,

Abstract :

Successful management of groundwater resources using numerical models requires knowledge of spatial distribution of hydraulic heads and it's fluctuation, aquifer parameters and other input data. One of the most basical issues in groundwater resources management is the estimation of water table from observation well network data. In this study, three methods of artificial intelligence, geostatistics (Kriging) and IDW with evapotranspiration, air temperature, precipitation and geographic inputs were used to simulate the depth of groundwater Salman Farsi Sugarcane Plantation. The results showed that the highest simulation accuracy of groundwater depth in Salman Farsi Sugarcane Plantation was related to the artificial neural network model with the highest R2 (0/92) index and lowest RMSE and MAE (1.25 and 1.74) values. Also, among the Kriging and IDW models used, the accuracy of the Kriging model was more than the IDW model. Due to the acceptable accuracy of the results of the presented models, the water resource planner and decision maker in this field can apply this optimum interpolated groundwater depth to monitoring the spatiotemporal fluctuation of groundwater depth in this area by updating it's data.

References:

1. Bameri, A.; Khormali, F.; Kiani, F.; and Dehghani, A.A. 2012. Spatial variability of soil organic carbonon different slope positions of loess hillslopes in Toshan area, Golestan Province. Journal of Soil and Water Conservation Research, 19 (2): 43-62. [In Persian].


2. Bayatvarkeshi, M.; and Fasihi, R. 2018. Comparison of numerical model, neural intelligent and GeoStatistical in estimating groundwater table. researches in Geographical Sciences, 18 (48) :165-182. [In Persian].


3. Dayhoff, J. E. 1990. Neural Network Principles. Prentice-Hall International, U.S.A.


4. Goovaerts, P. 1997. Geostatistics for Natura Resources Evaluation. NewYork: Oxford University Press.


5. Hasani Paak, A. 1998. Geostatistics. University of Tehran Press, 330 pp. [In Persian].


6. Hosseinali Zadeh, M.; and Yaghubi, A. 2010. Temporal and spatial changes of groundwater aquifer surface using geostatistics. Iranian Journal of Watershed Management Science and Engineering, 4 (10): 63-67. [In Persian].


7. Jahani, S.; and Delbari, M. 2009. Evaluation and Estimation of the 24 hour Maximum Rainfall in the Provice of Golestan. Journal of Water Engineering, 2 (4): 13-22. [In Persian].


8. Jamshidi Avanaki, M.; and Ebrahimi, K. 2012. Comparison of artificial neural network and geostatistical approach in predicting groundwater level - Case study: Mashhad plain aquifer. Second Conference on Environmental Planning and Management. University of Tehran. [In Persian].


9. Khanna, T. 1990. Foundation of neural networks. Addison-Wesley Publishing Company, U.S.A.


10. Karthikeyan, L., Kumar, N.D., Graillot, D. and Gaur, S., 2012. Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks. Water Resources Management, 27(3): 871–883.


11. Kord, M.; Yuosefi, N.; and  Abbas Novinpour, E. 2019. Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS), Inverse Distance Weighting and Geostatistics Methods for Estimating the Water Table (Case Study: Dehgolan Plain, Kurdistan Province). Journal of Echo Hydrology, 6 (1): 51-64. [In Persian].


12. Mansouri, F. 2005. Investigation of design parameters of underground drainage systems in irrigation and drainage project of sugarcane development plan, case study of Amirkabir unit. Master's thesis. Campus of Agriculture and Natural Resources, University of Tehran. [In Persian].


13. Misaghi, F.; and Mohammadi, K. 2006. Zoning of rainfall information using classical and geostatistical methods and comparison with artificial neural network. Scientific Journal of Agriculture, 23 (13): 1-4. [In Persian].


14. Mirzaei, A.; amd Nazemi, A.H. 2011. Water level prediction using intelligent systems (Case study: Shabestar plain). Journal of Water Resources Engineering, 4 (8): 1-10. [In Persian].


15. Nikbakht, S.; and Delbari, M. 2013. Estimation of groundwater water table using geostatistical methods. Journal of Water and Sustainable Development, 1 (1): 49-56. [In Persian].


16. Piri, H.; and Bameri, A. 2014. Investigating the quantity variation trend of ground water table using geostatistics and GIS (Case study: Sirjan Plain). Journal of Remote Sensing and GIS, 5 (1): 29-44. [In Persian].


17. Rashidi, S., Mohammadan, M. and Azizi, K., 2015. Predicting of Groundwater Level Fluctuation Using ANN and ANFIS in Lailakh plain. Journal of Renewable Natural Resources Bhutan, 3 (1):77-84.


18. Rostami Fathabadi, M. 2016. Evaluation and accuracy of geostatistical methods in estimating the optimal groundwater level, Case study: Northwest of Kermanshah plain. Journal of Geographical Sciences, 12 (25): 33-49. [In Persian].


19. Shayan, M.A. 2000. Correlation between meteorological factors and yield indices in sugarcane cultivation in Haft Tappeh region. Master Thesis, Campus of Agriculture and Natural Resources, University of Tehran. [In Persian].


20. Yue, S., Kang, S., Li, F. and  Zhang L. 2009. Comparison of interpolation methods for depth to groundwater and its temporal and spatial variations in the Minqin oasis of northwest China. Environmental Modelling & Software, 24(10): 1163-1170.


21. Zare Abyaneh, H.; and Bayat Varkeshi, M. 2014. Development and application of statistical and neural, Fuzzy, Genetic Algorithm models in estimation of spatial distribution of water table level. Journal of Soil and Water Conservation Research, 20 (4): 1-25. [In Persian].