Efficiency of Meta Model Simulators and Multivariate Linear Regression in Hydrograph Simulation of Aquifer Representation Loor-Andimeshk

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ZEINALI M.; GOLABI M.R.; BAHRAMI M.

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Journal: Journal of Water and Soil Conservation Year:2018 | Volume:25 | Issue:4 Page(s): 49-69

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Title

Efficiency of Meta Model Simulators and Multivariate Linear Regression in Hydrograph Simulation of Aquifer Representation Loor-Andimeshk

Author(s)

ZEINALI M. | GOLABI M.R. | BAHRAMI M. | Issue Writer Certificate

Keywords

Hydrograph representing aquiferQ1

Meta model gene expression simulatorQ1

Meta model Neural-Fuzzy simulatorQ2

ModelingQ1

Abstract

Background and Objectives: The reduction of atmospheric precipitation, limited water resources and the increase to withdraw from groundwater have led to a decline in the water table of the plains and therefore the Modeling of groundwaters is an effective tool for managing and protecting these resources. Most studies carried out on groundwater prediction are related to the prediction of the water level and less attention has been paid to hydrographs of the aquifer. Therefore, the purpose of this study was to first introduce hydrograph Modeling of the aquifer using a neuro-fuzzy metamorphic model and then compare the result with Modeling by the trans-model of gene expression simulator, which both models for the first time in this study for this The purpose of the study was to address the fundamental question of whether fuzzyfunction models, which are fairly acceptable in most of the studies that lack data and information, can also be better in this case study than the simulation model The gene expression, which has shown good performance in most recent studies. Materials and Methods: The study area, Laur-Andimeshk Plain, is part of the Dezful-Andimashk Plain. In the area of the Laura-Andimashk Plain, the 8 piezometer loops, which have relatively good distribution in the region, form the plain piezometric network. To do this study, using the geometric coordinates of each pysometer and the monthly statistical information of the 8th pizometer of the Laura-Andimashk plain, for 5 years (89-88-93-92) and using the Tesine method in the GIS environment, weighted average Peü someter was obtained and the time series of the groundwater level of the plain, which represents the hydrograph of the representative aquifer of the study area, was calculated. Results: By comparing the transmutation of the neuro-fuzzy simulator and the transmutation of the gene expression simulator, it is observed that in the training stage, the coefficient of nonfuzzy-simulation of the transcoding explanation is more than the transcoding explanatory factor of the gene expression simulator. However, in the test stage, the transdermal expression coefficient of the gene expression simulator is greater than the fuzzy-fuzzy simulator. On the other hand, according to the mean square error of the error, it is observed that the transcendental-neural-fuzzy simulator has a lower mean square root mean square error in the test phase. Based on the OI criterion, the closer the values are to one, the better the model performs, it is observed that the transmutation of the gene expression simulator has, by difference, a more OI benchmark than that of the neuro-fuzzy simulator and can be seen by observing the table values It is concluded that the performance of the trans-model of the gene expression simulator is better than the framed-neural-fuzzy simulator and, in the absence of data and information for the Modeling of the hydrograph representing the aquifer, using conceptual models such as the Modflow, the Transmodel gene expression simulator can be a good alternative. Conclusion: The results showed that Transformer gene expression simulator with coefficient of explanation of 74 percent at test stage compared to non-fuzzy-simulation model with coefficient of explanation of 64 percent has better performance and it can be concluded that models based on fuzzy function, which in most of the studies that lack data and information, we have relatively acceptable results. In this case study, we did not have a better performance than the conventional model of gene expression simulation. In the absence of data and information for Modeling hydrographs, representing the aquifer using conceptual models such as Modflow, Gene expression simulator can be a good alternative.

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APA: Copy

ZEINALI, M., GOLABI, M.R., & BAHRAMI, M.. (2018). Efficiency of Meta Model Simulators and Multivariate Linear Regression in Hydrograph Simulation of Aquifer Representation Loor-Andimeshk. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 25(4 ), 49-69. SID. https://sid.ir/paper/156021/en

Vancouver: Copy

ZEINALI M., GOLABI M.R., BAHRAMI M.. Efficiency of Meta Model Simulators and Multivariate Linear Regression in Hydrograph Simulation of Aquifer Representation Loor-Andimeshk. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2018;25(4 ):49-69. Available from: https://sid.ir/paper/156021/en

IEEE: Copy

M. ZEINALI, M.R. GOLABI, and M. BAHRAMI, “Efficiency of Meta Model Simulators and Multivariate Linear Regression in Hydrograph Simulation of Aquifer Representation Loor-Andimeshk,” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 25, no. 4 , pp. 49–69, 2018, [Online]. Available: https://sid.ir/paper/156021/en

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