Variable Analysis for Supporting Reservoir Impoundment Modelling

R. Harimukti Rosita, Pitojo Tri Juwono, Lily Montarcih Limantara, Emma Yuliani

Abstract

This research investigates accurate predictions of increases in reservoir levels, and it was conducted for 10 different reservoirs. The methodology consists of developing a series of symbolic regression models and evaluating the performance based on the root mean square error. Dams are constructed primarily to impound and store a large body of water. However, when dam construction is complete, the flow to the dam site resumes and the reservoir begins to fill with water. The first filling of a reservoir can be defined as the increase in the water level behind the dam from the time the construction is complete until it reaches the desired operating level. The construction works below were undertaken within a restricted timeframe owing to the rising water levels during reservoir filling. For a multipurpose dam constructed in the upstream reach of a river, various water users acquire water, and the dam must release outflow for supplying water to users engaged in irrigation, water supply, and maintenance flow. Regardless of whether it takes several months or years to occur naturally or with the aid of pumping units, the first filling of a reservoir should be planned, controlled, and closely monitored to reduce the risk of failure. The results show that the symbolic regression model provides accurate predictions with an RMSE of less than one day, producing a complex non-linear relationship between reservoir volume variables and average inflow for determining the duration of reservoir filling.

 

Keywords: reservoir impoundment, filling duration, regression.

 

https://doi.org/10.55463/issn.1674-2974.51.6.18


Full Text:

PDF


References


ZHANG J., FENG L., CHEN S., HUANG T., CHEN L., WANG D., DAI M., and ZHANG D. Impoundment Impact of the Three Gorge Reservoir on the Hydrological Regime in the Lower Han River, China. Water, 2018, 10(11): 1670. https://doi.org/10.3390/w10111670

CHUO M., MA J., LIU D., and YANG Z. Effects of the impounding process during the flood season on algal blooms in Xiangxi Bay in the Three Gorges Reservoir, China. Ecological Modelling, 2019, 392: 236–249. https://doi.org/10.1016/j.ecolmodel.2018.11.017

TSCHERNUTTER P., & KAINRATH A. Design considerations and behavior of reinforced concrete core dams during construction and impounding. Water Science and Engineering, 2016, 9(3): 212–218. https://doi.org/10.1016/j.wse.2016.11.006

LINSLEY R. K., FRANZINI J. B., FREYBURG D. L., and TCHOBANOGLOUS G. Water Resources Engineering. 4th ed. Irwin McGraw-Hill, New York, 1992.

HUNG Y. C., CHEN T. T., TSAI T. F., and CHEN H. X. A Comprehensive Investigation on Abnormal Impoundment of Reservoirs—A Case Study of Qionglin Reservoir in Kinmen Island. Water, 2021, 13(11): 1463. https://doi.org/10.3390/w13111463

ZHOU C., SUN N., CHEN L., DING Y., ZHOU J., ZHA G., LUO G., DAI L., and YANG X. Optimal Operation of Cascade Reservoirs for Flood Control of Multiple Areas Downstream: A Case Study in the Upper Yangtze River Basin. Water, 2018, 10(9): 1250. https://doi.org/10.3390/w10091250

TEEGAVARAPU R. S. V., & SIMONOVIC S. P. Simulation of Multiple Hydropower Reservoir Operations Using System Dynamics Approach. Water Resources Management, 2014, 28(7): 1937–1958. https://doi.org/10.1007/s11269-014-0586-2

HASHMI M. Z., & SHAMSELDIN A. Y. Use of Gene Expression Programming in regionalization of flow duration curve. Advances in Water Resources, 2014, 68: 1–12. https://doi.org/10.1016/j.advwatres.2014.02.009

TOWFIGHI S. pySRURGS - a python package for symbolic regression by uniform random global search. Journal of Open Source Software, 2019, 4(41): 1675. https://doi.org/10.21105/joss.01675

JAIN S. K. Introduction to reservoir operation. National Institute of Hydrology, Roorkee, 2019. https://nihroorkee.gov.in/sites/default/files/uploadfiles/IntroductiontoReservoir-Operation.pdf


Refbacks

  • There are currently no refbacks.