Predicting and managing reservoir total phosphorus by using modified grammatical evolution coupled with a macro-genetic algorithm

  • Authors:

  • Li Chen;Shuh-Ji Kao;Seydou Traore

  • Affiliations:

  • Department of Civil Engineering, Chung Hua University, Hsinchu 30012, Taiwan, ROC;Research Center for Environmental Change, Academia Sinica, Taipei 11529, Taiwan, ROC and State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China;Department of Tropical Agriculture and International Cooperation, National Pingtung University of Science and Technology, Neipu Hsiang, Pingtung 91201, Taiwan, ROC

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2012

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Abstract

A model that predicts the monthly water quality for a subtropical deep reservoir was constructed based on a newly developed programming system, the incremental grammatical evolution (IGE). IGE was designed to execute Grammatical Evolution (GE) by iteratively introducing the optimal solution until convergence, and to explore complex veiled relationships between inputs and outputs when physical models cannot be defined in advance. A disadvantage of traditional GE is that it tends to select the most significant input variables and may become trapped in a local optimum. The IGE adequately manages the large input dimensionality by incrementally expanding the search depth. From three IGE runs, we extracted four significant input variables from 15 input variables, including watershed chemical loads, precipitation, inflow, and outflow, and expressed them appropriately in a sophisticated mathematical manner with accepted complexity. The IGE-derived equation yields the optimal predictive capability, especially for peak total phosphorous (TP) values, compared to traditional multilinear regression (MLR) and back-propagation neural network (BPNN) models. The sensitivity analyses reconfirm the effectiveness of the selected variables in the nonlinear mathematical equations. Although BPNN and IGE demonstrate similar performances, we preferred the latter because of its transparency in providing a formula with measurable parameters. After obtaining the IGE-derived model, a Macro-evolutionary Genetic Algorithm (MEGA) was applied to enhance searching efficiency and genetic diversity during optimization, and subsequently deduced the reduction rates of TP loads from various input sources to achieve the water quality requirement of the reservoir. This practice benefits the reservoir management by revealing the forcing functions that are manageable to prevent reservoir eutrophication.