Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Afkhamifar Saeid; SARRAF AMIRPOUYA

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: WATERSHED ENGINEERING AND MANAGEMENT Year:2020 | Volume:12 | Issue:2 Page(s): 351-364

Download Full-Text

Persian Verion

View:

601

Download:

Cites:

Information Journal Paper

Title

Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization

Author(s)

Afkhamifar Saeid | SARRAF AMIRPOUYA | Issue Writer Certificate

Keywords

Artificial Neural NetworkQ2

Extreme learning machineQ1

Groundwater levelQ1

Quantum particle swarm optimizationQ1

Wavelet transformsQ1

Abstract

Today, due to the importance of sustainable groundwater management, Groundwater level modeling and forecasting are used to assess and evaluate water resources. The purpose of this study is to evaluate the performance of two models of Extreme Learning Machines (ELM) and Artificial Neural Network (ANN) and the combination of two models with wavelet transmission algorithms (W-ELM and W-ANN), which ultimately to increases the predictive power and optimization of input weights (the weights between the input and hidden layers) of models, Quantum particle swarm optimization algorithm (QPSO) has been used. Also, in this study, the data of Ground Water Level of observation wells (GWL), precipitation (P) and average temperature (T) of Urmia Plain aquifer with a time series of 36 years (1981 – 2017) which were collected on monthly scale, are used. Also, in order to evaluate the performance of models, correlation coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used. In this regard, 80% of the data (September 1981 to August 2010) are used for training section and 20% of data (September 2010 to August 2017) used for the test section of models. Based on the results of this study, the hybrid model of W-ELM-QPSO with correlation coefficient (R) 0. 991, 0. 983 and 0. 975, respectively for periods of one, two and three months in the test section, have a better performance than other models and also in addition to predicting power, this model has a high speed in terms of training and testing speed than other models.

Cites

No record.

References

No record.

Cite

APA: Copy

Afkhamifar, Saeid, & SARRAF, AMIRPOUYA. (2020). Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization. WATERSHED ENGINEERING AND MANAGEMENT, 12(2 ), 351-364. SID. https://sid.ir/paper/234738/en

Vancouver: Copy

Afkhamifar Saeid, SARRAF AMIRPOUYA. Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization. WATERSHED ENGINEERING AND MANAGEMENT[Internet]. 2020;12(2 ):351-364. Available from: https://sid.ir/paper/234738/en

IEEE: Copy

Saeid Afkhamifar, and AMIRPOUYA SARRAF, “Prediction of groundwater level in Urmia Plain aquifer using hybrid model of wavelet Transform-Extreme Learning Machine based on quantum particle swarm optimization,” WATERSHED ENGINEERING AND MANAGEMENT, vol. 12, no. 2 , pp. 351–364, 2020, [Online]. Available: https://sid.ir/paper/234738/en

Related Journal Papers