Construction of Artificial Water Quality Parameters with No Trend in Reservoirs (Chahnimeh No.1 in Sistan)

Document Type : Technical Note (5 pages)

Authors

1 assistant Prof., University of Sistan and Baluchestan, Zahedan, Iran

2 Master graduated, University of Sistan and Baluchestan, Zahedan, Iran

Abstract

Identification and monitoring the water resources which has a special position is one of the principal steps in quality management of water resources. In Sistan and Baluchestan province, this principle seems to be more important, because this state located in a hot and dry area and has deficiency in usable water resources. The aim of this study is predicting the quality of without trend parameters in Chahnime No.1 of Sistan using neural network and comparing it with Markov chain method. In the present study some parameters such as DO, temperature, Phytoplankton, Zooplankton, Ammonia and Phosphorus have been considered. The mean error percentages of neural network method for these parameters were 5.543, 7.714, 12.825, 5.625, 52.396 and 4.141 respectively, while mean error percentages of Markov chain scheme were 11.169, 8.948, 5.315, 12.934, 33.88, and 8.401, respectively. Obtained results showed that neural network method provided better results in comparing with Markov chain.

Keywords

Main Subjects

References

Alihamzeh M and Mohammad Rezapour Tabari M (2014) Qualitative behavior identification of surface reservoir in case of sudden injection of contaminant load. Iran-Water Resources Research 10(1):39-50
Ansarifard M, Boustani F (2013) Prediction of water quality parameters in rivers by using artificial neural network. International conference on environmental planning & management (In Persian)
Bakhtiari B, Shahraki N, Ahmadi MM (2014) Estimation probability of daily precipitation by using Markov chain models in different climates of Iran. Iran-Water Resources Research 10(2):44-55 (In Persian)
Hashemi Monfared SA, Mirbagheri SA, Sadrnejad SAA (2014) Three-dimensional, integrated seasonal separate advection–diffusion model (ISSADM) to predict water quality patterns in the Chahnimeh reservoir. Environmental Modeling & Assessment 19(1):71-83
Khosravi M (2010) Temporal and spatial analysis of the stability of the Hamoon lakes. Iran-Water Resources Research 6(3):68-79 (In Persian)
Publication No. 330 (2008) Executive instruction for water quality monitoring in dams reservoirs. Ministry of energy
Afsari M (2015) Construction of artificial water quality parameters with no trend in reservoirs by means of Markov chain and comparison with artificial neural network. MsC thesis, University of Sistan and Baluchestan
Rajaee T, Rahimi Benmaran R, Jafari H (2015) Prediction of quality parameters (NO3, DO) of Karaj river using ANN, MLR, and denoising-based combined wavelet-neural network based on models. Iran. J. Health & Environment 7(4):511-530 (In Persian)
Rezaee Harooni A (2014) Construction of artificial parameters no data basins by means of Markov chain and comparison with artificial intelligence, MSC thesis, University of Sistan and Baluchestan, Zahedan, Iran (In Persian)
Tafarojnoruz A, Rezaeir Banis N, Izadjoo F, Asghari Pari A, Shafaei Bajestan M (2007) Water quality modelling of Kondok reservoir using Hec-5Q. 7th International River Engineering Conference, Shahid Chamran University, Ahwaz, Iran (In Persian)
Shakiba H (2010) Investigation and forecasting monthly and annual temperature using ANN (case study: Karaj). MSC thesis, University of Sistan and Baluchestan, Zahedan, Iran (In Persian)
 
 
Iran-Water Resources Research
Volume 13, Issue 2
September 2017
Pages 226-232

Files

History

  • Receive Date: 16 July 2016
  • Revise Date: 06 November 2016
  • Accept Date: 08 November 2016

Share

How to cite

Statistics