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H. K. Cigizoglu and O. Kisi, “Flow Prediction by Three Back-Propagation Techniques Using K-Fold Partitioning of Neural Network Training Data,” Nordic Hydrology, Vol. 36, No. 1, 2005, pp. 49-64.

has been cited by the following article:

• TITLE: Solving the Carbon Dioxide Emission Estimation Problem: An Artificial Neural Network Model

  AUTHORS: Abdel Karim Baareh

  KEYWORDS: Fossil Fuels; Carbon Emission; Forecasting; Artificial Neural Network; Back Propagation

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.7, June 28, 2013

  ABSTRACT: Climate Pollution due to the Carbon Emission (CO2) from the different fossil fuels is considered as a great and important international challenge to many researchers. In this paper we are providing a solution to forecast the poison CO2 gas emerged from energy consumption. Four inputs data were considered the global oil, natural gas, coal, and primary energy consumption to build our system. In this paper, we used the Artificial Neural Network (ANN) as successful and powerful tool in handling a time series modeling problem. The proposed ANN model was used to train and test the yearly CO2 Emission. The data were trained from year 1982 to 2000, and tested for the year 2003 to 2010. From the results obtained we can see that ANN performance was Excellent and proved its efficiency as a useful tool in solving the climate pollution problems.