PREDICTION OF MONTHLY DISSOLVED OXYGEN USING WAVELET AND ARTIFICIAL NEURAL NETWORK COMBINED MODEL

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RAJAEE T.; BOROUMAND A.

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Journal: Journal of Water and Soil Conservation Year:2016 | Volume:22 | Issue:6 Page(s): 153-169

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Title

PREDICTION OF MONTHLY DISSOLVED OXYGEN USING WAVELET AND ARTIFICIAL NEURAL NETWORK COMBINED MODEL

Author(s)

RAJAEE T. | BOROUMAND A. | Issue Writer Certificate

Keywords

DISSOLVED OXYGENQ1

WAVELET TRANSFORMQ1

ARTIFICIAL NEURAL NETWORKQ2

WATER QUALITYQ2

DAM RESERVOIRQ1

Abstract

Background and Objectives: Qualitative and quantitative management of water resources to meet the demand for different usages is the major approach in each country policy. In this regard, DAM RESERVOIRs WATER QUALITY monitoring is an important step in the management of these resources. Previous researches described in this study, show that, ARTIFICIAL NEURAL NETWORK based models can be used to predict the qualitative indices of water resources efficiently. The objective of this study is to develop an efficient model in order to predict the concentration of DISSOLVED OXYGEN in the DAM RESERVOIR.Materials and Methods: The data used in this study consisted of monthly DISSOLVED OXYGEN data from January 1998 to December 2007 were obtained from Boulder reservoir, Colorado, (USA). This study investigated the prediction of DISSOLVED OXYGEN in a gauging station in the reservoir by ARTIFICIAL NEURAL NETWORK, multi linear regression and conjunction of wavelet analysis and ARTIFICIAL NEURAL NETWORK models. In the proposed wavelet analysis and ARTIFICIAL NEURAL NETWORK model, observed time series of DISSOLVED OXYGEN was decomposed at different scales by wavelet analysis. Then, total effective time series of this WATER QUALITY index was imposed as inputs to the ARTIFICIAL NEURAL NETWORK model for prediction of one month ahead DISSOLVED OXYGEN.Results: Results showed that the wavelet analysis and ARTIFICIAL NEURAL NETWORK combined model performance were better in prediction rather than the ARTIFICIAL NEURAL NETWORK and multi linear regression models. Using wavelet analysis improved the modeling results considerably. In the combined model, E, and RMSE is obtained 0.96 and 0.22 respectively. ARTIFICIAL NEURAL NETWORK and the combined wavelet with ARTIFICIAL NEURAL NETWORK models produced reasonable predictions for the minimum values that lead anaerobic condition in reservoir.Conclusion: The results showed that using wavelet analysis in conjunction with ARTIFICIAL NEURAL NETWORK, improved the modeling performance. Also the results of this research indicate that the wavelet analysis and ARTIFICIAL NEURAL NETWORK combined model is a promising model for DISSOLVED OXYGEN predicting in reservoirs such as those found in Boulder reservoir.

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APA: Copy

RAJAEE, T., & BOROUMAND, A.. (2016). PREDICTION OF MONTHLY DISSOLVED OXYGEN USING WAVELET AND ARTIFICIAL NEURAL NETWORK COMBINED MODEL. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 22(6), 153-169. SID. https://sid.ir/paper/156353/en

Vancouver: Copy

RAJAEE T., BOROUMAND A.. PREDICTION OF MONTHLY DISSOLVED OXYGEN USING WAVELET AND ARTIFICIAL NEURAL NETWORK COMBINED MODEL. JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2016;22(6):153-169. Available from: https://sid.ir/paper/156353/en

IEEE: Copy

T. RAJAEE, and A. BOROUMAND, “PREDICTION OF MONTHLY DISSOLVED OXYGEN USING WAVELET AND ARTIFICIAL NEURAL NETWORK COMBINED MODEL,” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 22, no. 6, pp. 153–169, 2016, [Online]. Available: https://sid.ir/paper/156353/en

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