Prediction of Zayandeh Rood Dam Inflow and Hydrological Wet and Dry Periods Using Bayesian Networks

Document Type : Research Article

Authors

• Parisa Noorbeh
• Abbas Roozbahani
• Hamid Kardan Moghaddam

University of Tehran

https://doi.org/10.22067/jsw.v32i3.72084

Abstract

Introduction: During the last decades, runoff decreasing is observed in our country as many dam reservoirs face water supply crisis even in normal periods. This decreasing trend is mainly due to the uncontrolled withdrawals, lack of supply and demand management as well as droughts. Using different flow prediction methods for surface water resources state analysis is important in water resources planning aspects. These methods can provide the possibility of planning for proper operation by using different factors to meet the needs of the region. Due to the stochastic nature of the hydrological processes, various models are used for prediction. Among these models, Bayesian Networks (BNs) probabilistic model has been considered by many researchers in recent years and it has shown the efficiency on these issues. Due to the growth of demand in different sectors and crises caused by drought of the water supply system that has put the basin under water stress, the water shortage has appeared in different sectors. Regarding to the strategic situation of Zayandeh Rood Dam in providing water resources for tap water, industry, agriculture and environmental water rights in Gavkhooni basin, this research presents the development of a model for prediction of Zayandeh Rood Dam annual inflow and hydrological wet and dry periods. Since the uncertainty of the predictions increase when the prediction horizon increases, this factor is the most important challenge of long-term prediction. Using Bayesian Network with reducing this uncertainty, provides the possibility of planning for water resources management, especially for optimal water allocation.
Materials and Methods: In this study for prediction of zayandeh Rood dam inflow five scenarios were defined by applying Bayesian Network Probabilistic approach. According to this, prediction of numerical annual dam inflow (scenario1), annual wet and dry hydrological periods (scenario 2, 3, 4) and range of annual inflow (scenario 5) were performed. For this purpose rainfall, runoff, snow, and discharge of transferred water to the basin from the first and the second tunnel of koohrang and Cheshmeh Langan tunnel were considered as predictor variables and the amount of Zayandeh Rood Dam inflow was selected as predictant for modeling and different conditions of input variable’s learning have been analyzed considering different patterns. Calibration and validation of the model have been done based on observed annual inflow data and the relevant predictors in scenario 1, by using SDI Hydrological drought index and long-term average of inflow to classify the runoff and clustering the other parameters in scenario 2, 3 and 4 and with classification of annual inflow data and other parameters by using clustering in scenario 5. To achieve this target, K-means method has been used for clustering and Davies-Bouldin and Silhouette Width has been used to determine optimal number of clusters.
Results and Discussion: The results of Bayesian Network modeling showed that the scenario 1 has a good potential to predict the dam inflow so that the best pattern of this scenario (considering discharge of first tunnel of Koohrang and Cheshmeh Langan tunnel, Zayandeh Rood natural inflow and rainfall with two years lag time as predictor variables), has had a correlation coefficient of 0.78 between observed and predicted dam inflow and relative error of 0.21 which shows an acceptable accuracy in prediction. Among scenarios 2, 3 and 4 for prediction of wet and dry hydrological periods, scenario 2 in which classification of runoff has been based on the long-term average, in the best pattern (with dam inflow with one-year lag predictor), is able to be predicted up to 75% accuracy. The analysis of the results showed that the scenario 5 is not very accurate in prediction of dam inflow’s range.
Conclusions: The results showed that the Bayesian Network model has a good efficiency to predict annual dam inflow numerically as well as hydrological dry and wet periods. Obtained results from prediction of hydrological dry and wet periods will be effective in better planning of water resources in order to considering possible ways of drought effect reduction. The overall results provide the possibility of water resources planning for the water authorities of this region. Systematic planning leads to optimal use of water and soil resources and helps considerably to analyze and modify the policy or rule curve of this dam for allocating water to downstream especially for agriculture and environment and industry sectors.

Keywords

• Bayesian Networks
• Clustering
• Runoff Prediction
• SDI index
• Zayandeh Rood Dam

20.1001.1.20084757.1397.32.3.14.2

References

1- Abrishamchi A., Jamali S., Mariño M. A., and Tajrishy M. 2006. Stream flow forecasting and reservoir operation models using fuzzy inference systems. P. 373-382 Operations Management Conference 9-12 May. 2006, California, USA.

2- Abrishamchi A., Tajrishy M., Tafarojnoruz A., Chehrenegar B., and Shadzad, Sh. 2008. Using satellite data to improve the accuracy of river flow forecasting modelsa case study of Zayande-rood river basin, Iran. AWRA 2008 SPRING SPECIALTY CONFERENCE 17-19 March.2008, San Mateo, California.

3- Adamowski J., and Sun K. 2010. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology, 390: 85-91.

4- Ahmadi F., Radmanesh F., and Mirabbasi Najafabadi R. 2016. Comparing the performance of Support Vector Machines Bayesian networks in predictiong daily river flow (Case study: Baranduz Chai River). Journal of Water and Soil Condervation, 22(6):171-186 (In Persian with English abstract).

5- Anbari M. J., Tabesh M., and Roozbahani A. 2017. Risk assessment model to prioritize sewer pipes inspection in wastewater collection networks. Journal of Environmental Management, 190: 91-101.‏

6- Araghinejad S., and Karamouz M. 2005. Long-Lead Streamflow Forecasting using Artificial Neural Networks and Fuzzy Inference System. Journal of Iran-Water Resources Research, 1(2): 29-41 (In persian with English abstract ).

7- Bayes Server. 2017. Bayes Server researcher user guide, verion 7.25.

8- Eghtedar Nezhad M., Bazrafshan O., and Sadeghi Lari A. 2017. Adaptive Evaluation of SPI, RDI and SDI Indices in Analyzing the Meteorological and Hydrological Drought Characteristics (Case Study: Bam Plain). Journal of Water and Soil Science , 26(4.2): 61-81 (In Persian with English abstract).

9- Halkidi M., Batistakis Y., and Vazirgiannis. M. 2001. on clustering validation techniques. J. Intel. Infor. Sys, 17: 107-145.

10- Heckman J. J., and Snyder Jr J. M. 1996. Linear probability models of the demand for attributes with an empirical application to estimating the preferences of legislators (No. w5785). National bureau of economic research.‏

11- Hugin Expert. 2017. Hugin researcher user guide, version 8.5.

12- Humphrey G. B., Gibbs M. S., Dandy G. C., and Maier H. R. 2016. A hybrid approach to monthly streamflow forecasting: integrating hydrological model outputs into a Bayesian artificial neural network. Journal of Hydrology, 540: 623-640.

13- Kardan moghadam H. and Roozbahani A. 2015. Assessment of Byesian Network Models in Monthly Predecion of Groundwater Level (Case study: Birjand aquifer). Journal of Water and Irrigation Management, 5(2): 139-151 (In Persian with English abstract).

14- Liedloff A.C., Woodward E.L., Harrington G.A., and Jackson S. 2013. Integrating indige-nous ecological and scientific hydro-geological knowledge using a BayesianNetwork in the context of water resource development. Journal of Hydrology, 499: 177–187.

15- Mollaramezani M., and Tabesh M. 2014. Long-term forecasting of urban water demand using Bayesian networks, 8th national congress of Civil engineering , Noshirvani university of technology, Babol, Iran (In Persian ).

16- Momeni M. 2011. Clustering of data (Cluster analysis)" Moalef publication (1): 130 (In Persian)

17- Murphy K. 2001. An introduction to graphical models. Rap. tech: 19-1.

18- Naqdi Bansole K., and Musavi J. 2014. Prediction of Zayandehrood dam reservoir inflow using the K-Nearest Neighbors algorithm (KNN). 5th National Conference on water Resources Management. 2014, Tehran, Iran.

19- Noori N., and Latif K. 2016. Coupling SWAT and ANN models for enhanced daily streamflow prediction. Journal of Hydrology, 533: 141-151.

20- Noori R., Karbassi A.R., Moghaddamnia A., Han D., Zokaei-Ashtiani M. H., Farokhnia A., and Gousheh, M. G. 2011. Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology, 401(3): 177-189.

21- Rao A.R., and Srinivas V.V. 2008. Regionalization of Watersheds: an Approach Based onCluster Analysis, vol. 58. Springer Science & Business Media.

22- Roozbahani A. 2011. Risk Based Decision Making Model of Urban Water Systems Management, PhD thesis, College of engineering , university of Tehran , Tehran, Iran.

23- Shin J. Y., Ajmal M., Yoo J., and Kim T. W. 2016. A Bayesian network-based probabilistic framework for drought forecasting and outlook. Advances in Meteorology 2016.

24- Sikorska A. E., and Seibert J. 2016. Value of different precipitation data for flood prediction in an alpine catchment: A Bayesian approach. Journal of Hydrology, 556: 961-971.

25- Valipour M. 2015. Long‐term runoff study using SARIMA and ARIMA models in the United States, Meteorological Applications, 22(3): 592-598.

26- Wan Y., and Konyha K. 2015. A simple hydrologic model for rapid prediction of runoff from ungauged coastal catchments, Journal of Hydrology, 528: 571-583.