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Information Journal Paper

Title

Evaluation of the Combination of Optimization Algorithms and adaptive Fuzzy-Neural Inference System Compared to Time Series Models in Groundwater Level Estimation

Pages

  245-256

Abstract

 To optimize the management and use of groundwater resources, the temporal-spatial variation of the stagnant level and its prediction and modellig is essential to better understand the behavior of aquifers in response to the natural and human stimuli. Given the increasing development of metamodels and their combination with optimization algorithms for Modeling and predicting hydrogeological variables, the question remains that to what extent these hybrid models can be effective compared to the individual model. To answer this question, in this study, four algorithms of particle overvoltage optimization (PSO), genetics (GA), ant colony (ACOR), and demand evolution (DE) were combined with the model of adaptive fuzzy-neural inference system (ANFIS). The performance of the combined models developed with the ANFIS model was evaluated to estimate the average monthly Groundwater Level of the Sahneh plain aquifer in Kermanshah province over 19 years. The time series model (SARIMA) was used as the reference model. To better compare the results of the models, the same input variables of the Groundwater Level in different time steps (maximum four months based on the self-correlation function of aquifer level) were considered for them. The results of fitness indicators in the training and test phases showed that there was no significant difference between the SARIMA time series model compared to other combined models used. However, given that SARIMA applies average moving processes, authorization, seasonal changes, and delays in Modeling, it can be given more attention in Groundwater Leveling Modeling. The RMSE values of the best hybrid model (ANFIS-GA) and SARIMA were 0. 950 and 0. 1012, respectively. The results also showed that the combination of optimization algorithms considered with the ANFIS model did not improve the model's results compared to the individual ANFIS model in terms of significance. The results of this research can help researchers in consciously choosing the appropriate model in predicting the time of the stagnant aquifer level according to the criteria of efficiency, time and cost of calculations and data preparation to enter the models.

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    APA: Copy

    ZEINALI, M., Ansari Ghojghar, M., MEHRI, Y., & HOSSEINI, S.M.. (2020). Evaluation of the Combination of Optimization Algorithms and adaptive Fuzzy-Neural Inference System Compared to Time Series Models in Groundwater Level Estimation. IRAN-WATER RESOURCES RESEARCH, 16(3 ), 245-256. SID. https://sid.ir/paper/383893/en

    Vancouver: Copy

    ZEINALI M., Ansari Ghojghar M., MEHRI Y., HOSSEINI S.M.. Evaluation of the Combination of Optimization Algorithms and adaptive Fuzzy-Neural Inference System Compared to Time Series Models in Groundwater Level Estimation. IRAN-WATER RESOURCES RESEARCH[Internet]. 2020;16(3 ):245-256. Available from: https://sid.ir/paper/383893/en

    IEEE: Copy

    M. ZEINALI, M. Ansari Ghojghar, Y. MEHRI, and S.M. HOSSEINI, “Evaluation of the Combination of Optimization Algorithms and adaptive Fuzzy-Neural Inference System Compared to Time Series Models in Groundwater Level Estimation,” IRAN-WATER RESOURCES RESEARCH, vol. 16, no. 3 , pp. 245–256, 2020, [Online]. Available: https://sid.ir/paper/383893/en

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