Evolving an Information Diffusion Model Using a Genetic Algorithm for Monthly River Discharge Time Series Interpolation and Forecasting

Chengzu Bai Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, and Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China

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Mei Hong Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, and Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China

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Dong Wang Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing, China

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Ren Zhang Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People's Liberation Army University of Science and Technology, Nanjing, China

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Longxia Qian Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, Nanjing, China

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Abstract

The identification of the rainfall–runoff relationship is a significant precondition for surface–atmosphere process research and operational flood forecasting, especially in inadequately monitored basins. Based on an information diffusion model (IDM) improved by a genetic algorithm, a new algorithm (GIDM) is established for interpolating and forecasting monthly discharge time series; the input variables are the rainfall and runoff values observed during the previous time period. The genetic operators are carefully designed to avoid premature convergence and “local optima” problems while searching for the optimal window width (a parameter of the IDM). In combination with fuzzy inference, the effectiveness of the GIDM is validated using long-term observations. Conventional IDMs are also included for comparison. On the Yellow River or Yangtze River, twelve gauging stations are discussed, and the results show that the new method can simulate the observations more accurately than traditional IDMs, using only 50% or 33.33% of the total data for training. The low density of observations and the difficulties in information extraction are key problems for hydrometeorological research. Therefore, the GIDM may be a valuable tool for improving water management and providing the acceptable input data for hydrological models when available measurements are insufficient.

Corresponding author address: M. Hong, Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, 60 Shuanglong Road, Nanjing 211101, China. E-mail: flowerrainhm@126.com

Abstract

The identification of the rainfall–runoff relationship is a significant precondition for surface–atmosphere process research and operational flood forecasting, especially in inadequately monitored basins. Based on an information diffusion model (IDM) improved by a genetic algorithm, a new algorithm (GIDM) is established for interpolating and forecasting monthly discharge time series; the input variables are the rainfall and runoff values observed during the previous time period. The genetic operators are carefully designed to avoid premature convergence and “local optima” problems while searching for the optimal window width (a parameter of the IDM). In combination with fuzzy inference, the effectiveness of the GIDM is validated using long-term observations. Conventional IDMs are also included for comparison. On the Yellow River or Yangtze River, twelve gauging stations are discussed, and the results show that the new method can simulate the observations more accurately than traditional IDMs, using only 50% or 33.33% of the total data for training. The low density of observations and the difficulties in information extraction are key problems for hydrometeorological research. Therefore, the GIDM may be a valuable tool for improving water management and providing the acceptable input data for hydrological models when available measurements are insufficient.

Corresponding author address: M. Hong, Research Center of Ocean Environment Numerical Simulation, Institute of Meteorology and Oceanography, People’s Liberation Army University of Science and Technology, 60 Shuanglong Road, Nanjing 211101, China. E-mail: flowerrainhm@126.com
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