Comparison of artificial neural network and regression pedotransfer function for prediction of soil cation exchange capacity at Iraq, Ray alJazeera, Mosul region :
Other Title(s)
مقارنة بين نموذجي الشبكات العصبية و الانحدار الخطي المتعدد في تخمين السعة التبادلية الكاتيونية للتربة باستخدام الدوال التحويلية في العراق في منطقة ري الجزيرة في مدينة الموصل
Joint Authors
Mahmud, Sahar Ismail
Hamid, Firas Shawkat
Sarhan, Ibrahim Khalil
Source
Journal of Engineering Sciences and Information Technology
Issue
Vol. 4, Issue 2 (30 Jun. 2020), pp.90-109, 20 p.
Publisher
Publication Date
2020-06-30
Country of Publication
Palestine (Gaza Strip)
No. of Pages
20
Main Subjects
Information Technology and Computer Science
Abstract EN
The study of soil characteristics such as the ability to exchange positive ions CEC (Cation Exchange Capacity) play a significant part in study of ecological researches, also it is important for decision concerning pollution prevention and crop management.
CEC represent the quantity of negative charges in soil, since direct method for measuring CEC are cumbersome and time consuming Lead to the grow of indirect technique in guessing of soil CEC property.
Pedotransfer function (PTFs) is effective in estimating this parameter of easy and more readily available soil properties, 80 soil sample were taken from diverse horizons of 20 soil profiles placed in the Aljazeera Region, Iraq.
The aim of this study was to compare Neural Network model (feed forward back propagation network) and Stepwise multiple linear regression to progress a Pedotransfer function for forecasting soil CEC of Mollisols and Inseptisols in Al Jazeera Irrigation Project using easily available features such as clay, sand and organic matter.
The presentation of Neural Network model and Multiple regression was assessed using a validation data set.
For appraise the models, Mean Square Error (MSE) and coefficient of determination R2 were used.
The MSE and R2 resultant by ANN model for CEC were 2.2 and 0.96 individually while these result for Multiple Regression model were 3.74 and 0.88 individually.
Result displayed 8% improvement in increasing R2 and also improvement 41% for decreasing MSE for ANN model, this pointed that artificial neural network with three neurons in hidden layer had improved achievement in forecasting soil cation exchange capacity than multiple regression.
So we can conclude that ANN model by use (MLP) multilayer perceptron for predicting CEC from measure available soil properties have more accuracy and effective compared with (MLR) multiple linear regression model
American Psychological Association (APA)
Mahmud, Sahar Ismail& Hamid, Firas Shawkat& Sarhan, Ibrahim Khalil. 2020. Comparison of artificial neural network and regression pedotransfer function for prediction of soil cation exchange capacity at Iraq, Ray alJazeera, Mosul region :. Journal of Engineering Sciences and Information Technology،Vol. 4, no. 2, pp.90-109.
https://search.emarefa.net/detail/BIM-1279266
Modern Language Association (MLA)
Mahmud, Sahar Ismail…[et al.]. Comparison of artificial neural network and regression pedotransfer function for prediction of soil cation exchange capacity at Iraq, Ray alJazeera, Mosul region :. Journal of Engineering Sciences and Information Technology Vol. 4, no. 2 (Jun. 2020), pp.90-109.
https://search.emarefa.net/detail/BIM-1279266
American Medical Association (AMA)
Mahmud, Sahar Ismail& Hamid, Firas Shawkat& Sarhan, Ibrahim Khalil. Comparison of artificial neural network and regression pedotransfer function for prediction of soil cation exchange capacity at Iraq, Ray alJazeera, Mosul region :. Journal of Engineering Sciences and Information Technology. 2020. Vol. 4, no. 2, pp.90-109.
https://search.emarefa.net/detail/BIM-1279266
Data Type
Journal Articles
Language
English
Notes
يتضمن ملحق : ص. 103-109
Record ID
BIM-1279266