Evolution algorithm for water storage forecasting response to climate change with little data sets:the Wolonghu Wetland,China  

作　　者：尼庆伟 叶人珍 杨凤林 雷坤 

机构地区：[1]School of Environmental & Biological Science & Technology,Dalian University of Technology [2]Chinese Research of Enviromental Sciences [3]Huazhong Agricultral University

出　　处：《Journal of Harbin Institute of Technology(New Series)》2011年第2期127-133,共7页哈尔滨工业大学学报（英文版）

基　　金：Sponsored by the National Basic Research Program of China(Grant No. 2006CB403302);the National Education Ministry foundation of China(Grant No.705011);the National Special Science and Technology Program Water Pollution Control and Treatment (Grant No.2009ZX07526-006,2008AX07208-001)

摘　　要：An attempt of applying a novel genetic programming(GP) technique,a new member of evolution algorithms,has been made to predict the water storage of Wolonghu wetland response to the climate change in northeastern part of China with little data set.Fourteen years(1993-2006) of annual water storage and climatic data set of the wetland were taken for model training and testing.The results of simulations and predictions illustrated a good fit between calculated water storage and observed values(MAPE=9.47,r=0.99).By comparison,a multilayer perceptron(MLP)(a popular artificial neural network model) method and a grey model(GM) with the same data set were applied for performances estimation.It was found that GP technique had better performances than the other two methods both in the simulation step and predicting phase and the results were analyzed and discussed.The case study confirmed that GP method is a promising way for wetland managers to make a quick estimation of fluctuations of water storage in some wetlands under condition of little data set.An attempt of applying a novel genetic programming （GP） technique, a new member of evolution al- gorithms, has been made to predict the water storage of Wolonghu wetland response to the climate change in northeastern part of China with little data set. Fourteen years （1993-2006） of annual water storage and climatic data set of the wetland were taken for model training and testing. The results of simulations and predictions il- lustrated a good fit between calculated water storage and observed values （ MAPE -- 9.47, r -- 0. 99 ）. By comparison, a muhilayer perceptron （MLP） （a popular artificial neural network model） method and a grey model （GM） with the same data set were applied for performances estimation. It was found that GP technique had better performances than the other two methods both in the simulation step and predicting phase and the results were analyzed and discussed. The case study confirmed that GP method is a promising way for wetland managers to make a quick estimation of fluctuations of water storage in some wetlands under condition of little data set.

关 键 词：water storage little data set evolution algorism Wolonghu wetland 

分 类 号：X143[环境科学与工程—环境科学]