International Journal of Earth Sciences and Engineering

Dongfeng Li ¹, Zhang Yunpeng ²

Affiliations
1 School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan-450045, CN
2 School of Electric Power Engineering, North China University of Water Resources and Electric Power, Zhengzhou, Henan-450045, CN

Abstract

Because of the serious pollution of Shaying River, the excessive water storage by sluices and dams on that river is one of the prime reasons for the pollution of trunk stream Huai River. To counter this problem, this paper tries to regulate the water storage by sluices and dams on Shaying River, this paper explores the limit level for pollution control to prevent water pollution incident caused by centralized discharge of polluted water by proposing and defining the concept of limit level for pollution control, and establishing a model of limit level for pollution control of sluices and dams for the purpose of reservoir profiting and pollution-control. Besides, this paper applies the above method to multiple sluices and dams on Shaying River in Huai River Basin, and solves the model by multi-objective genetic algorithm. The calculation results indicate that the established model is a practical, effective way to determine the limit level for pollution control of sluices and dams on Shaying River which provides important technical support for the development of pollution-control regulation among sluices and dams in Huai River Basin in China.

Keywords

Shaying River, Sluices and Dams, Limit Level for Pollution Control, Multi-Objective Genetic Algorithm, Pollution-Control Regulation.

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Yunpeng Zhang ¹, Dongfeng Li ¹

Affiliations
1 North China University of Water Resources and Electric Power, Zhengzhou, Henan-450011, CN

Abstract

Without giving consideration to series variation, commonly used reconstruction methods may lead to series over-fitting or bring about too much uncertainty. Under the condition of abrupt changes and on the basis of Bayesian Decision and Generalized Linear Model, this paper puts forward a reconstructed model with the aid of empirical mode decomposition (EMD), Generalized Linear Regression, and support vector machine (SVM). Firstly, the model identifies the variation points of the series with Bayesian Decision to divide the series into segments. Secondly, EMD is performed on the series segment by segment. Finally, modeling and simulation are conducted by SVM and optimal subset regression. Taking the runoff from 1951 to 2010 in Huaxian Station of Weihe River as an example, the author acquires the EMD components and frequencies of the annual runoffs in the three stages (1951-1971, 1972-1984, and 1994-2010) before/after the 3 variation points (1971, 1984 and 1994), and reconstructs the historical runoff data and uses the SVR to predict each component of the EMD. The segmental optimal subset regression features small prediction error and high precision because each IMF component produced in EMD decomposition has strong regularity.

Keywords

Reconstruction, Abrupt Changes, Bayesian Decision, Empirical Mode Decomposition, Runoff.

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