Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model
Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect...
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| Format: | Article |
| Language: | fas |
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Mashhad University of Medical Sciences
2021-06-01
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| Series: | Pizhūhish dar Bihdāsht-i Muḥīṭ. |
| Subjects: | |
| Online Access: | https://jreh.mums.ac.ir/article_18218_248822a3356b1a1fe7721e9bb3b681c6.pdf |
| _version_ | 1823937844303888384 |
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| author | Ali Reza Karimiyan Aslan Egdernezhad |
| author_facet | Ali Reza Karimiyan Aslan Egdernezhad |
| author_sort | Ali Reza Karimiyan |
| collection | DOAJ |
| description | Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect water resources. Most of these models require input parameters that are hardly available or their measurements are time-consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by the human brain are a better choice. Materials and Methods: The present study simulated the groundwater level and salinity in Ramhormoz plain using ANN and ANN+PSO models and compared their results with the measured data. The data collected as inputs of the two models included minimum temperature, maximum temperature, average temperature, wind speed at 2 m altitude, minimum relative humidity, maximum relative humidity, average relative humidity, and sunshine hours gathered from 2011 to 2017. Results: The results indicated that the highest prediction accuracy of groundwater level and salinity was achieved by the ANN-PSO model with the logarithm sigmoid activation function. Thus, the MAE and RMSE statistics had the minimum and R^2 had the maximum value for the model. Conclusion: Considering the high efficiency of artificial neural network models with Particle Swarm Optimization algorithm training, it can be used to make managerial decisions, ensure the results of monitoring, and reduce costs. Keywords: Groundwater Level; Simulation; Groundwater Salinity; Artificial Neural Networks Model |
| first_indexed | 2024-12-17T02:36:52Z |
| format | Article |
| id | doaj.art-32d901a86d354f73b2189ce7dfbd906b |
| institution | Directory Open Access Journal |
| issn | 2423-5202 2423-5202 |
| language | fas |
| last_indexed | 2024-12-17T02:36:52Z |
| publishDate | 2021-06-01 |
| publisher | Mashhad University of Medical Sciences |
| record_format | Article |
| series | Pizhūhish dar Bihdāsht-i Muḥīṭ. |
| spelling | doaj.art-32d901a86d354f73b2189ce7dfbd906b2022-12-21T22:06:50ZfasMashhad University of Medical SciencesPizhūhish dar Bihdāsht-i Muḥīṭ.2423-52022423-52022021-06-0171172610.22038/jreh.2021.56527.141418218Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network modelAli Reza Karimiyan0Aslan Egdernezhad1Department of Civil Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.Assistant Professor, Department of Water Sciences and Engineering, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran.Abstract Background and Aim: Because of their high effectiveness and fewer expenses than other methods, groundwater models have been developed and used by hydrogeologists as water resource management tools. In this regard, many models have been developed, which propose better management to protect water resources. Most of these models require input parameters that are hardly available or their measurements are time-consuming and expensive. Among them, Artificial Neural Network (ANN) models inspired by the human brain are a better choice. Materials and Methods: The present study simulated the groundwater level and salinity in Ramhormoz plain using ANN and ANN+PSO models and compared their results with the measured data. The data collected as inputs of the two models included minimum temperature, maximum temperature, average temperature, wind speed at 2 m altitude, minimum relative humidity, maximum relative humidity, average relative humidity, and sunshine hours gathered from 2011 to 2017. Results: The results indicated that the highest prediction accuracy of groundwater level and salinity was achieved by the ANN-PSO model with the logarithm sigmoid activation function. Thus, the MAE and RMSE statistics had the minimum and R^2 had the maximum value for the model. Conclusion: Considering the high efficiency of artificial neural network models with Particle Swarm Optimization algorithm training, it can be used to make managerial decisions, ensure the results of monitoring, and reduce costs. Keywords: Groundwater Level; Simulation; Groundwater Salinity; Artificial Neural Networks Modelhttps://jreh.mums.ac.ir/article_18218_248822a3356b1a1fe7721e9bb3b681c6.pdfgroundwater levelsimulationgroundwater salinityartificial neural networks model |
| spellingShingle | Ali Reza Karimiyan Aslan Egdernezhad Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model Pizhūhish dar Bihdāsht-i Muḥīṭ. groundwater level simulation groundwater salinity artificial neural networks model |
| title | Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model |
| title_full | Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model |
| title_fullStr | Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model |
| title_full_unstemmed | Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model |
| title_short | Simulation of groundwater level and groundwater salinity parameters of Ramhormoz plain using artificial neural network model and optimized artificial neural network model |
| title_sort | simulation of groundwater level and groundwater salinity parameters of ramhormoz plain using artificial neural network model and optimized artificial neural network model |
| topic | groundwater level simulation groundwater salinity artificial neural networks model |
| url | https://jreh.mums.ac.ir/article_18218_248822a3356b1a1fe7721e9bb3b681c6.pdf |
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