Assessment of groundwater corrosivity in Hamedan Province, Iran using an adaptive neuro-fuzzy inference system (ANFIS)

Abyaneh, Hamid Zare; Varkeshi, M Bayat; Mohammadi, Kourosh; Howard, Ken; Marofi, Safar

doi:10.1007/s12303-011-0032-x

Cited by 7 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to test and evaluate developed ANN models performance, Correlation Coefficient (R), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) were used to compare the convergence between the measured and estimated values. The correlation coefficient (R) is a common indicator for assessing the ANN models; the correlation coefficient (R) was determined utilizing [22].…”

Section: Statistical Criteriamentioning

confidence: 99%

A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

AL-Salihi¹,

AL-Ramahy²

2017

jaar

View full text Add to dashboard Cite

Soil temperature is an important meteorological variable which plays a significant role in hydrological cycle. In present study, artificial intelligence technique employed for estimating for 3 daysa head soil temperature estimation at 10 and 20 cm depth. Soil temperature daily data for the period 1 January 2012 to 31 December 2013 measured in three stations namely (Mosul, Baghdad and Muthanna) in Iraq. The training data set includes 616 days and the testing data includes 109 days. The Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian regularization algorithms. To evaluate the ANN models, Root mean square error (RMSE), Mean absolute error (MAE), Mean absolute percentage error (MAPE) and Correlation Coefficient (r) were determined. According to the four statistical indices were calculated of the optimum ANN model, it was ANN model (3) in Muthanaa station for the depth 10 cm and ANN model (3) in Baghdad station for the depth 20 were (RMSE=0.959oC, MAE=0.725, MAPE=4.293, R=0.988) and (RMSE=0.887OC, MAE=0.704, MAPE=4.239, R=0.993) respectively, theses statistical criteria shown the efficiency of artificial neural network for soil temperature estimation.

show abstract

Section: Statistical Criteriamentioning

confidence: 99%

A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

AL-Salihi¹,

AL-Ramahy²

2017

jaar

View full text Add to dashboard Cite

show abstract

Incorporation of information entropy theory, artificial neural network, and soft computing models in the development of integrated industrial water quality index

Egbueri

2022

Environ Monit Assess

View full text Add to dashboard Cite

Signatures of contamination, corrosivity and scaling in natural waters from a fast-developing suburb (Nigeria): insights into their suitability for industrial purposes

Egbueri

2020

Environ Dev Sustain

View full text Add to dashboard Cite

Assessment of groundwater corrosivity in Hamedan Province, Iran using an adaptive neuro-fuzzy inference system (ANFIS)

Cited by 7 publications

References 15 publications

A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

A neural network model for estimation soil temperature bases on limited meteorological parameters in selected provinces in Iraq

Incorporation of information entropy theory, artificial neural network, and soft computing models in the development of integrated industrial water quality index

Signatures of contamination, corrosivity and scaling in natural waters from a fast-developing suburb (Nigeria): insights into their suitability for industrial purposes

Contact Info

Product

Resources

About