Volume 12, Issue 24 (9-2021)
jwmr 2021, 12(24): 54-64 |
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moghaddasi M, mardiyan M, parsa M. (2021). Comparison and Assessment of Intelligent and Geostatistical Models for Analysis of Spatial Variations of Groundwater Quality (Komijan Plain). jwmr. 12(24), 54-64. doi:10.52547/jwmr.12.24.54
URL: http://jwmr.sanru.ac.ir/article-1-1051-en.html
URL: http://jwmr.sanru.ac.ir/article-1-1051-en.html
Arak University
Abstract: (2176 Views)
Extended Abstract
Introduction and Objective: Nowadays, with development of urban, industrial and agricultural, apply of groundwater is more important. So sustainability and development the exploitation of groundwater for types of different customers and goals, it is necessary that quantitative and qualitative characteristics it be investigated and evaluated.
Material and Methods: Fuzzy Adaptive Neural Network (FANN) and Geostatistical method on based Geographic Information System are used for Komijan plain, Markazi province, Iran. The first, data 36 wells was collected from Rural Water and Sewage Company. Then using semi variogram types such as: gussian, linear, spherical and also Kriging and Co-Kriging methods, geostatistical model was evaluated using indicators: R2 and RMSE. Then, for Fuzzy Adaptive Neural Network model Membership functions such as: triangular, generalized bells and gaussian was investigated and the best model was determined using indicators: R2 and RMSE.
Results: According to results R2 and RMSE in geostatistical, spherical, linear and exponential modle was selected as best for EC, TDS and pH variables, repectively. Also on based semi variogram, Kriging method has a better performance than the cokriging method for all studied variables with high determination coefficient 0.73, 0.66 and 0.85 respectively for EC, TDS and pH and lower in RMSE. The results showed in Fuzzy Adaptive Neural Network, EC variable, the fuzzy generalized bell function with a correlation coefficient of 0.98 and mean square error of 144.59 in the test stage, is good. For TDS variable, gaussian function with a correlation coefficient of 0.98 and mean square error of 0.33 119 at the test stage is best. also for pH variable, the generalized bell function with a correlation coefficient of 0.99 and mean square error of 103.10 at the test stage has a better performance than other fuzzy functions in the modeling. By comparing the results of Geostatistical and Fuzzy Adaptive Neural Network, it can be seen that the FANN model has a higher efficiency than Geostatistical model.
Conclusion: Regarding the results of zoning maps, it is shown that in the northern part of the plain, EC has low, while in the central and west, EC is above 2000 µSiemens/cm. Also for TDS variable, t is low in the northern part of the plain, while in the south and southwest, is above 1000 mg /lit. Alos changes in pH value showed that variation of this variable is low and the highest level of pH is in the northern part and the lowest in the southern part.
Introduction and Objective: Nowadays, with development of urban, industrial and agricultural, apply of groundwater is more important. So sustainability and development the exploitation of groundwater for types of different customers and goals, it is necessary that quantitative and qualitative characteristics it be investigated and evaluated.
Material and Methods: Fuzzy Adaptive Neural Network (FANN) and Geostatistical method on based Geographic Information System are used for Komijan plain, Markazi province, Iran. The first, data 36 wells was collected from Rural Water and Sewage Company. Then using semi variogram types such as: gussian, linear, spherical and also Kriging and Co-Kriging methods, geostatistical model was evaluated using indicators: R2 and RMSE. Then, for Fuzzy Adaptive Neural Network model Membership functions such as: triangular, generalized bells and gaussian was investigated and the best model was determined using indicators: R2 and RMSE.
Results: According to results R2 and RMSE in geostatistical, spherical, linear and exponential modle was selected as best for EC, TDS and pH variables, repectively. Also on based semi variogram, Kriging method has a better performance than the cokriging method for all studied variables with high determination coefficient 0.73, 0.66 and 0.85 respectively for EC, TDS and pH and lower in RMSE. The results showed in Fuzzy Adaptive Neural Network, EC variable, the fuzzy generalized bell function with a correlation coefficient of 0.98 and mean square error of 144.59 in the test stage, is good. For TDS variable, gaussian function with a correlation coefficient of 0.98 and mean square error of 0.33 119 at the test stage is best. also for pH variable, the generalized bell function with a correlation coefficient of 0.99 and mean square error of 103.10 at the test stage has a better performance than other fuzzy functions in the modeling. By comparing the results of Geostatistical and Fuzzy Adaptive Neural Network, it can be seen that the FANN model has a higher efficiency than Geostatistical model.
Conclusion: Regarding the results of zoning maps, it is shown that in the northern part of the plain, EC has low, while in the central and west, EC is above 2000 µSiemens/cm. Also for TDS variable, t is low in the northern part of the plain, while in the south and southwest, is above 1000 mg /lit. Alos changes in pH value showed that variation of this variable is low and the highest level of pH is in the northern part and the lowest in the southern part.
Type of Study: Research |
Subject:
هيدرولوژی
Received: 2019/10/27 | Revised: 2022/02/22 | Accepted: 2020/08/2 | Published: 2021/09/1
Received: 2019/10/27 | Revised: 2022/02/22 | Accepted: 2020/08/2 | Published: 2021/09/1
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