Reservoir water level forecasting using wavelet support vector regression (WSVR) based on teaching learning-based optimization algorithm (TLBO)

Malekpour, Mohammad Mahdi; Malekpoor, Hossein

doi:10.1007/s00500-022-07296-1

Cited by 4 publications

(1 citation statement)

References 36 publications

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“…Ruishen et al [18], based on the connotation of drought-limited water level, a new method is proposed to calculate the graded and staged drought-limited water level of a reservoir, which uses the reverse order recursive algorithm and fully considers the differences in the annual water demand process of different drought levels and different industries; Li et al [19] analyzed the causal relationship between the variables in the water supply and demand system, established a System Dynamics model for water supply volume, and calculated the drought limit water level based on the inflow process and water supply volume. Malekpour et al [20], the wavelet support vector regression method based on the teaching and learning algorithm was applied to the water level prediction of the Zayanderud dam in Iran. This model is promoted through wavelet transform and optimization algorithms, which promote the reduction of model errors and the improvement of accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimal Determination and Dynamic Control Analysis of the Graded and Staged Drought Limit Water Level of Typical Plateau Lakes

Wang

et al. 2023

Water

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The technical research on determining the drought limit water level can be used as an important basis for starting the emergency response of drought resistance in the basin and guiding the drought resistance scheduling of water conservancy projects. When the concept of drought limit water level was first proposed, the main research object was reservoirs, and the method for determining the lake drought limit water level was not established. Referring to the calculation method of reservoir drought limit water level, the drought limit water level is used as a single warning indicator throughout the year, which lacks graded and staged standards, and also lacks rationality and effectiveness in practical application. Therefore, this article has improved the concept of lake drought limit water level (flow). Under different degrees of drought and water use patterns during the drought period, combined with the characteristics of lake water inflow, considering the factors such as ecology, water supply, and demand, lake inflow, evapotranspiration loss, a graded and staged standard of lake drought limit water level has been developed. For different types of lakes, a general method for determining the lake’s graded and staged drought limit water level has been established. The SCSSA-Elman neural network is used to construct the medium and long-term water inflow prediction model for lakes, and the calculation results of this model are used for the warning and dynamic control analysis of the lake drought limit water level. The application of this method has the characteristics of strong applicability and high reliability. Finally, the determination method and dynamic control method of the lake’s graded and staged drought limit water level have been successfully applied at Dianchi Lake in Yunnan.

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Section: Introductionmentioning

confidence: 99%