List of Articles Artificial Neural Networks model

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  1 - Modeling of Groundwater Quality Parameters Using Artificial Neural Network and Geostatistics Models (Case Study: Zeidoun plain)
  Abdol Amir Echreshzadeh Aslan Egdernezhad
  Background and Aim: One of the obstacles to develop sustainable is the poor quality of water. The assessment of water quality is usually based on chemical decomposition and measurement of chemical parameters of water. Measuring these parameters in big area is costly and More

  Background and Aim: One of the obstacles to develop sustainable is the poor quality of water. The assessment of water quality is usually based on chemical decomposition and measurement of chemical parameters of water. Measuring these parameters in big area is costly and time-consuming, as result it required to estimating methods for prediction of those parameters. The purpose of this study is to model the groundwater quality parameters of Zeydoon plain using ANN+PSO and geostatistics models. Methods: For this purpose, the information of 42 observation wells in Zeidoon plain on a monthly basis for 7 years has been used. Neural network model inputs including qualitative parameters SO42- ، pH ، HCO32-،  Na+، Mg2+، Ca2+، TDS، SAR and EC were considered. Findings: The results of simulation of groundwater quality parameters using ANN + PSO model showed that in SAR simulator model the highest simulation accuracy is related to the model with sigmoid logarithm function, in EC simulator model the highest accuracy is similar. The construction is related to the model with the stimulus function of the sigmoid tangent. Also, in the TDS simulator model, the highest simulation accuracy of the model with the sigmoid tangent stimulus function was obtained. As RMSE and MAE have the lowest value and R2 index has the highest value. The results of simulation of groundwater quality parameters using the geostatistical model showed that the highest accuracy of the kriging model in the simulation is related to EC, SAR and TDS parameters, respectively. Discussion and Conclusion: Finally, comparing the results of comparing the results of ANN + PSO model and Kriging model showed that ANN + PSO model is more accurate in simulating groundwater quality parameters of Zidon plain than Kriging model. Also, the results of this research showed that the combination of intelligent models with optimization algorithms with correct architecture and complete model inputs are used as a useful tool for simulating groundwater quality parameters. Manuscript profile